ASKA (Adaptive Secure Kernel Architecture) is a hardware-rooted secure computing architecture designed for high-assurance operation in complex environments, particularly relevant for the age of AGI. It addresses vulnerabilities in current architectures by employing modularity, hardware-enforced isolation, dynamic adaptability, and decentralized governance. This overview details its core components, referencing the described patent innovations.

1. Core Components and Technologies:

Modular Isolated Execution Stacks (IES) (P1): The foundation of ASKA. Each IES is a fully isolated hardware stack (CPU, memory, I/O, network) minimizing the impact of breaches. Dynamic partitioning allows real-time adjustment into smaller child-IES instances, adapting to workload (P10) and security needs (P4). Hierarchical zones within IES, governed by mini-TRCs (P1) stored on tamper-evident media (P13/P15), provide granular trust and policy management. Secure resource borrowing (SRBM - P9) enables efficient resource utilization while maintaining isolation. Secure boot and attestation (P13) verify component integrity. Sandboxing (P1, P16) facilitates secure testing. Capability-Enhanced Packet-Carried Forwarding State (CE-PCFS - P26) enables fine-grained, capability-based communication between IES and child-IES.

Dynamic Trust Management System (DTMS) (P4): Manages trust relationships between IES instances and zones, using TRCs (P1) stored on a decentralized ledger (P15). Dynamic trust metrics, calculated based on observed behavior (P2, P7, P10) and security posture (P13), influence access control, resource allocation, and software updates. Decentralized Zone Management (DZMS - P4) governs zone membership and inter-zone trust, leveraging SCION-inspired beaconing (P3) for TRC updates. Policy negotiation (P4) and distributed consensus (P13) ensure secure and consistent trust management.

Automated Evolutionary Software Development System (AESDS) (P16): An AI-driven system that continuously monitors, updates, and improves ASKA software. It leverages a knowledge base, performance metrics (P10), and threat intelligence (P5) to generate optimized and secure code. Secure zoned deployment (P3) and TRC-based verification (P1) ensure the integrity of updates. The Isomorphic Architecture Monitoring and Adaptation (IAMA - P16) module analyzes connected legacy systems (P27) via data diodes (P2) to predict vulnerabilities and proactively generate security patches. Multi-layered validation (P1, P20) ensures the safety of new software.

Sovereign Trust Network (STN) (P27): A highly isolated network for sensitive data and cryptographic keys. Its isolated data plane, minimal control plane coupling, and multi-level control system enhance security. Key recovery capabilities, utilizing a network of trusted synchronization nodes and MFA (P23), ensure data availability. An isomorphic security stack, leveraging IAMA (P16), provides proactive protection against legacy system vulnerabilities. Hardware-rooted trust further strengthens security.

Dynamic Trust Gateway (DTG) (P28): Mediates communication between the Authenticated Trust Network (ATN - P3) and the STN (P27). Dynamic channel provisioning (P3, P5), multi-path capability aggregation (P28), deep packet inspection (DPI - P28), and data sanitization (P28) ensure secure and controlled data flow. Adaptive security measures and decentralized governance (P13/P15/P17) enhance resilience and transparency.

Authenticated Trust Network (ATN) (P3): Provides authenticated communication (P5) between authorized entities (P4) using a multi-channel network (P3) with physically segregated channels and dynamic routing. Each channel utilizes a dedicated firewall instance (P3).

Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) (P24): Provides hardware-level encryption for data at rest, using post-quantum cryptography (P5). Granular access control (P4), key management within the enclave, and anti-tamper mechanisms (P7) ensure data confidentiality and integrity. Dynamic resource allocation (P9, P10) optimizes performance.

3D-Printed Microstructure Audit Trail (P14, P17): Generates tamper-evident audit trails using 3D-printed microstructures. Unique identifiers (P14) and secure storage ensure the integrity of the audit log. Integration with ASKA components (P13, P15) enables comprehensive provenance tracking.

Multi-Channel Network (P3): Employs physically segregated channels (P3), each with a dedicated firewall instance, to prevent cross-channel interference. Dynamic routing (P3) and capability-aware forwarding (P2) provide flexible and secure communication.

Quantum-Resistant Communication (P5): Utilizes Quantum Key Distribution (QKD), Distributed Key Management (DKM), and post-quantum cryptography (PQC) to secure communication against quantum computer attacks. Path-aware key distribution and dynamic endpoint discovery (P2, P3) enhance security and flexibility. SIBRA bandwidth reservation (P11) ensures QoS.

Zero-Knowledge Execution Environment (ZKEE) (P6): Enables computation on encrypted data without revealing the underlying information. Zero-knowledge proofs (ZKPs) and decentralized verification (P18) provide secure and verifiable computation.

Quantum-Entangled Auxiliary Memory System (QEAMS) (P34a): Provides out-of-band integrity verification for data, utilizing quantum entanglement between auxiliary memory elements and data blocks in primary storage. This system is implemented as a specialized chiplet (P12) within IES instances (P1).

2. Key Relationships and Interactions:

The components of ASKA interact closely to provide comprehensive security. AESDS updates the STN, DTG, and core components. DTMS provides trust levels and access control policies to various components. STN and DTG interact for secure data handling. The ASKA Hub orchestrates and manages all components. The decentralized ledger provides a tamper-proof audit trail. MDATS correlates digital logs with physical microstructures. MSM monitors system-wide security. HESE-DAR provides secure data storage. SHVS manages secure collaboration. SIZCF facilitates inter-zone collaboration. The Resource Manager and Capability Manager dynamically allocate resources and capabilities based on DTMS trust levels and TRCs. The Policy Engine enforces policies throughout the system. AI engines provide predictive analysis and automation. Quantum-resistant communication secures network channels. ZKEE enables secure private computation. The Secure UI Kernel provides secure user interaction. The Chiplet Architecture enables dynamic hardware integration. Secure Boot verifies system integrity.

3. Supporting Technologies:

ASKA relies on a number of supporting technologies, including Secure Boot (P1), Data Diodes (P2), Multi-Factor Authentication (P23), Secure UI Kernel (P11), Chiplet Architecture (P12), Decentralized Ledger (P13, P15), MDATS (P17), Secure Hyper-Virtualization (P18), Federated Learning (P19), Secure Data Enclaves (P20), and SIZCF (P22). SCION routing (P2, P3, P5, P16, P28) underpins secure communication and update distribution.

Patented Technologies

The following patents underpin ASKA's innovative capabilities:

Patent 1: Modular Isolated Execution Stacks with Hierarchical Zones, Decentralized Trust Management, and Capability-Based Inter-Component Communication - Creates isolated execution environments (IES) with hierarchical zones for granular security. Uses mini-TRCs for localized trust and capability-based communication for secure inter-IES data sharing. Supports dynamic partitioning for flexible resource allocation.

Patent 2: Secure Inter-IES Communication System with Dynamically Reconfigurable Capabilities, Declarative Policies, and Adaptive Security - Establishes secure communication between IES instances using data diodes for unidirectional data flow and capability-augmented PCFS for bidirectional communication. Employs a hierarchical security mesh and declarative policies for adaptive security.

Patent 3: Adaptive Multi-Channel Network with Declarative Policy Enforcement and Capability-Aware Forwarding - Creates a secure and adaptable network with physically segregated channels, managed by a Channel Manager. Uses declarative policies and capability-aware forwarding for fine-grained access control and dynamic traffic management. Integrates legacy systems securely.

Patent 4: Dynamic Trust Management System (DTMS) with Decentralized Zone Management and TRC-Based Trust - Establishes and manages trust relationships between IES instances and across zones using TRCs. Employs dynamic trust metrics, decentralized zone management, and distributed consensus for secure and adaptable trust.

Patent 5: Quantum-Resistant Secure Communication with Path-Aware Key Distribution, Dynamic QKD Endpoint Discovery, and SIBRA Bandwidth Reservation - Implements quantum-resistant communication using QKD, DKM, and PQC. Features path-aware key distribution, dynamic QKD endpoint discovery, and SIBRA bandwidth reservation for secure and resilient communication.

Patent 6: Zero-Knowledge Execution Environment with Decentralized Verification and Zone-Based Trust - Enables private computation on encrypted data within a Zero-Knowledge Execution Environment (ZKEE). Uses zero-knowledge proofs and decentralized verification for secure and verifiable computation.

Patent 7: Hardware-Enforced Anomaly Detection, Isolation, and Self-Healing with Secure SCMP Reporting, Zonal Response Policies, and Timing Side-Channel Detection - Provides hardware-enforced anomaly detection, isolation, and self-healing capabilities. Uses secure reporting, zonal response policies, and timing side-channel detection for proactive security.

Patent 8: Hardware-Based Memory Protection with Capability-Based Access Control and Dynamic Obfuscation - Secures memory through dynamic obfuscation, hardware-enforced segmentation, and real-time verification. Employs capability-based access control and an ORAM-like design for enhanced protection against various attacks.

Patent 9: Secure Resource Borrowing and Granular I/O Management with TRC-Based Policies, Multipath Communication, and Hardware-Enforced Isolation - Enables secure resource borrowing between IES instances and granular I/O management for shared peripherals. Uses TRC-based policies, multipath communication, and hardware isolation for secure resource access.

Patent 10: AI-Powered Predictive Resource Allocation and Adaptive Scaling for IES with Multipath Optimization, Declarative Policies, and Secure Sharing - Implements AI-powered predictive resource allocation and adaptive scaling for IES instances. Uses multipath optimization, declarative policies, and secure sharing for efficient resource management.

Patent 11: Secure UI Kernel with Zonal Isolation, Hardware-Enforced Control-Flow Integrity, and Declarative Policy-Based Rendering - Creates a secure UI kernel with hardware and zonal isolation. Employs hardware-enforced CFI, declarative policies, and secure communication for a protected user interface environment.

Patent 12: Secure and Adaptive Chiplet Architecture with Dynamic Resource Allocation, Capability-Based Access Control, and Hardware-Enforced Isolation - Defines a secure and adaptive chiplet architecture for integrating specialized hardware components. Employs dynamic resource allocation, capability-based access control, and hardware isolation for secure and flexible system design.

Patent 13: Secure and Transparent Zonal Governance System with AI-Driven Authentication, Decentralized Ledger, and Secure Boot Integration - Establishes a secure zonal governance system with AI-driven authentication. Uses a decentralized ledger, secure boot integration, and privacy-preserving techniques for secure and transparent governance.

Patent 14: 3D-Printed Microstructure Audit Trail for Citizen Voting System - Creates a tamper-evident audit trail using 3D-printed microstructures. Each microstructure has unique identifiers linked to digital records, ensuring verifiable and auditable processes.

Patent 15: AI-Powered Governance Auditing and Transparency with TRC Monitoring and Automated Conflict Resolution - Implements AI-powered governance auditing and transparency. Monitors TRCs, resolves conflicts, simulates policy changes, and uses a decentralized ledger for accountability and transparency.

Patent 16: Automated Evolutionary Software Development with Secure, Zoned Deployment, TRC-Based Verification, and Adaptive AI-Driven Security - Creates an automated evolutionary software development system (AESDS) for ASKA. Employs secure deployment, TRC-based verification, adaptive AI-driven security, and isomorphic architecture monitoring for secure and evolving software.

Patent 17: Multi-Dimensional Audit Trail System for ASKA with AI-Driven Microstructure Analysis and Software Provenance Tracking - Develops a multi-dimensional audit trail system (MDATS) using digital logs and physical microstructures. Employs AI-driven analysis, software provenance tracking, and real-time anomaly reporting for comprehensive auditing.

Patent 18: Secure and Adaptive Hyper-Virtualization System for Collaborative Workloads with Decentralized Policy Management, Real-time Security Monitoring, and Privacy-Preserving Data Sharing - Creates a secure hyper-virtualization system (SHVS) for collaborative workloads. Employs decentralized policy management, real-time monitoring, and privacy-preserving data sharing for secure collaboration.

Patent 19: Privacy-Preserving Federated Learning System using Secure Multi-Party Computation across Isolated Execution Stacks - Enables privacy-preserving federated learning using secure multi-party computation (MPC) across isolated IES instances. Supports dynamic orchestration, adaptive privacy techniques, and scalable collaboration for secure and private machine learning.

Patent 20: Secure Data Enclave System with Privacy-Preserving Collaborative Data Analytics - Creates a secure data enclave system within IES instances. Supports privacy-preserving data sharing, collaborative data analytics, and dynamic configuration for secure data analysis.

Patent 21: Blockchain-Enabled Self-Evolving Software System with 3D-Printed Microstructure Provenance Tracking and AI-Driven Security - Develops a blockchain-enabled self-evolving software system integrated with AESDS. Employs blockchain and 3D microstructure provenance tracking for secure and verifiable software evolution.

Patent 22: Secure Inter-Zone Collaboration Framework with Privacy-Preserving Data Exchange and Distributed Ledger Synchronization - Establishes a secure inter-zone collaboration framework (SIZCF) for secure communication and data sharing between zones. Employs privacy-preserving data exchange and distributed ledger synchronization for secure and consistent collaboration.

Patent 23: Adaptive Context-Aware MFA with Biometric and Behavioral Analysis - Implements adaptive context-aware multi-factor authentication (MFA) using biometric and behavioral analysis. Supports out-of-band token verification and decentralized ledger logging for secure authentication.

Patent 24: Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) with Dynamic Resource Allocation and Decentralized Governance Integration - Creates a hardware-enforced secure encrypted enclave (HESE-DAR) for data at rest protection. Employs dynamic resource allocation, granular access control, and decentralized governance integration for secure data storage.

Patent 25: Dynamically Reconfigurable Capability-Based Inter-IES Communication for ASKA - Implements dynamically reconfigurable capability-based communication between IES instances. Uses real-time reconfiguration based on trust levels, workload demands, and policies for adaptive security and resource management.

Patent 26: Capability-Enhanced Packet-Carried Forwarding State for Secure Inter-Component Communication in Multi-Kernel Systems - Enhances PCFS with capabilities for secure inter-component communication. Integrates capabilities into hop fields, providing fine-grained access control and dynamic capability reconfiguration at the data plane level.

Patent 27: Sovereign Trust Network for Secure Key Management and Authentication with Multi-Level Control and Recovery System - Creates a Sovereign Trust Network (STN) with isolated data and control planes for secure key management and authentication. Employs multi-level control and a key recovery system for enhanced security and resilience.

Patent 28: System and Method for Adaptive Secure Inter-Zone Communication Across Authenticated and Sovereign Trust Networks - Develops a Dynamic Trust Gateway (DTG) for secure communication between Authenticated and Sovereign Trust Networks. Employs dynamic channel provisioning, multi-path capability aggregation, and adaptive security for secure inter-zone communication.

Patent 29: Quantum-Entangled One-Time Pad Module for Secure Computing Architectures - Develops a Quantum-Entangled One-Time Pad (QE-OTP) module for secure communication within ASKA. Leverages quantum entanglement for key distribution, dynamic key generation, and fragmented key management within a HESE-DAR. Includes an Isomorphic Legacy System Integration module for backward compatibility.

Patent 30: Spatiotemporal Digest for Raster Content Verification - Introduces a system for verifying the authenticity and integrity of raster content (audio, images, video) using a spatiotemporal digest ("spatiotemporal metadata digest"). Captures environmental data during content creation and generates a non-invertible digest, providing a link to physical reality.

Patent 31: ASKA System with Integrated Spatiotemporal Raster Content Verification - Integrates the spatiotemporal digest verification system into ASKA. Operates within a HESE-DAR under DTMS control, binding the spatiotemporal digest to the content and recording it on the decentralized ledger. Supports legacy system integration.

Patent 32: Decentralized Privacy Blurring Standard with ASKA Integration - Creates a decentralized privacy blurring system for raster content. Uses an opt-in blockchain ledger managed by ASKA's DTMS to store anonymized biometric templates. AI agents on capture devices perform blurring based on these templates and spatiotemporal context.

Patent 33: Decentralized, Hierarchical Bootstrapping and Attestation with Dynamic Trust Integration and Tamper-Evident Audit Trail - Establishes a secure boot and attestation process leveraging a hierarchical trust model, from a Hardware Root of Trust to individual IES zones. Dynamically integrates trust assessments with DTMS and uses a tamper-evident audit trail.

Patent 34a: Quantum-Entangled Auxiliary Memory System for Out-of-Band Integrity Verification - Introduces a Quantum-Entangled Auxiliary Memory (EAM) system for out-of-band integrity verification. Uses quantum entangled storage elements linked to primary storage and a Quantum Entanglement Distribution Network. Employs cryptographic digests and quantum measurement for verification.

Patent 34b: Spatiotemporal Auxiliary Memory System for Out-of-Band Integrity Verification - Presents a Spatiotemporal Auxiliary Memory System (SAMS) for out-of-band integrity verification. Captures spatiotemporal metadata during data writes and generates spatiotemporal metadata digests for verification, linking data integrity to its physical creation context.

Patent 34c: Passively Radiative, Spatiotemporal Auxiliary Memory System for Out-of-Band Integrity Verification - Develops a passively radiative version of the spatiotemporal auxiliary memory system (PR-SAMS). Uses a Passively Radiative Sensor Array for capturing environmental metadata, reducing power consumption while maintaining strong integrity verification.

Diagram 1: Overview

graph

ASKA:::bkg

DataResources:::block

ASKAHub:::block

IESClusterx:::block

DataResources:::block

NetworkCommunication:::block

SovereignTrustNetwork:::block

SecurityMonitoring:::block

Audit:::block

MediaHandling:::bkg_strong

subgraph ASKA["ASKA Instance"]

subgraph Audit[" "]

DLT["Decentralized Ledger (P13, P15)"]:::audit

MDATS["MDATS (P17)"]:::audit --> DLT

Microstructure@{shape: flag, label: "3D Microstructure Audit (P14, P17)" } --> MDATS

Microstructure:::medium_level

DLT --> Hub2Hub(("Hub")):::high_level

end

subgraph DataResources["Data & Resources"]

HESE_DAR --> SecureStorage

QEAMS["QEAMS (P34a)"]:::low_level -.-> HESE_DAR

SAMS["SAMS (P34b)"]:::low_level -.-> HESE_DAR

PRSAMS["PR-SAMS (P34c)"]:::low_level -.-> HESE_DAR

end

subgraph ASKAHub["Hub"]

Hub(("Hub")):::high_level

Hub --> AESDS["AESDS (P16) IAMA (P16)"]:::medium_level

Hub --> DTMS((("       Dynamic Trust    (DTMS)     "))):::medium_level

Hub --> ChannelMgr["Channel Mgr (P3)"]:::medium_level

Hub --> CapMgr["Capability Mgr (P25)"]:::medium_level

Hub --> PolicyEngine["Policy Engine"]:::medium_level

Hub --> ResourceMgr["Resource Mgr (P9, P10)"]:::medium_level

Hub --> DTG["DTG (P28)"]:::medium_level

Hub --> SIZCF["SIZCF (P22)"]:::medium_level

Hub --> UIIntegration["UI Integration"]:::medium_level

Hub --> MediaHandling["Media Handling (P30, P31, P32)"]

Hub --> SecureBoot["Secure Boot (P1, P13, P33)"]:::low_level

Hub --> AnomalyDetector["Anomaly Detector (P7)"]:::low_level

Hub --> GovernanceAI["Governance AI (P15)"]:::medium_level

end

subgraph IESClusterx["IES Cluster (P1)"]

IESCluster[["IES Cluster (P1)"]]:::cluster_level

%% IES1["IES 1"]:::medium_level --> IESCluster

%% IES2["IES 2"]:::medium_level --> IESCluster

%% IESn["IES n"]:::medium_level --> IESCluster

IESInstances[["IES Instances 1..n"]] --> IESCluster

ZKEEInstances["ZKEE Instances (P6)"] --> IESCluster

SecureUI["Secure UI Kernel (P11)"]:::ui --> IESCluster

IESCluster --> SecureChannel["Secure Channels (P3)"]:::network

IESCluster --> ZKEE["ZKEE (P6)"]:::medium_level

IESCluster -.-> HESE_DAR(["HESE-DAR (P24)"]):::data

IESCluster --> LocalMSM["Local MSM (P2)"]:::low_level

ResourceMgr --> IESCluster

CapMgr --> IESCluster

end

subgraph NetworkCommunication["Network & Communication"]

SecureChannel --> Firewall["Firewall (P3)"]:::network

Firewall --> ExternalSystems["External Systems"]:::external

subgraph SovereignTrustNetwork["STN"]

STN["Soverign Trust Network (P27)"] --> SecureStorage[("Secure Storage")]:::data

end

PolicyEngine --> DTG

DTG --> STN

DTG --> ATN["ATN (P3)"]:::medium_level

AESDS --> STN

AESDS --> DTG

ChannelMgr --> SecureChannel

ChannelMgr --> Firewall

SIZCF --> ExternalSystems

IAMA["IAMA (P16)"]:::low_level --> STN

end

subgraph SecurityMonitoring[" "]

LocalMSM --> MSM["Security Monitoring & Response (MSM) (P2)"]:::low_level

MSM --> DTMS

AnomalyDetector --> DTMS

end

GovernanceAI --> DTMS

DTMS --> STN

DTMS --> DTG

DTMS --> CapMgr

DTMS --> PolicyEngine

DTMS --> ResourceMgr

PolicyEngine --> CapMgr

UIIntegration --> SecureUI

subgraph MediaHandling["Media"]

MediaRouter{"Media Router"}:::medium_level

SpatiotemporalDigest{{"Spatiotemporal Digest (P30, P31)"}}:::medium_level --> MediaRouter

PrivacyBlurring{{"Privacy Blurring (P32)"}}:::medium_level --> MediaRouter

%% MediaRouter --> MediaHandling

PadFeeder["Pad Feeder"]:::medium_level --> QEOTP

QEOTP["QE-OTP (P29)"]:::medium_level --> SecureChannel

end

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classDef block fill:#fafaf7,stroke:#988990,stroke-width:2px

classDef bkg_strong fill:#def

classDef high_level fill:#fda,stroke:#333,stroke-width:4px,font-size:1.25em

classDef cluster_level fill:#b9d3fc,stroke:#333,stroke-width:4px,font-size:1.15em

classDef medium_level fill:#b9d3fc,stroke:#333,stroke-width:2px

classDef low_level fill:#aaf,stroke:#333,stroke-width:2px

classDef data fill:#afa,stroke:#333,stroke-width:2px

classDef ui fill:#ffa,stroke:#333,stroke-width:2px

classDef audit fill:#ccf,stroke:#333,stroke-width:2px

classDef network fill:#eec,stroke:#333,stroke-width:2px

classDef external fill:#d8d8d8,stroke:#333,stroke-width:2px

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Diagram 1: Overview

ASKA (Adaptive Secure Kernel Architecture) is a multi-layered, hardware-rooted secure computing architecture designed for high-assurance operation in complex environments, especially those involving advanced general intelligence (AGI). Its core innovation is a defense-in-depth strategy combining hardware isolation, dynamic trust management, AI-driven security, and decentralized governance.

At the heart of ASKA is the ASKA Hub, a central orchestration and management component. The Hub houses critical modules including: the AI-driven AESDS (Automated Evolutionary Software Development System – P16) and IAMA (Isomorphic Architecture Monitoring and Adaptation – P16) for continuous software evolution and proactive patching of legacy systems; the DTMS (Dynamic Trust Management System – P4) for dynamic trust assessment and access control based on observed behavior, security posture, and declared attributes; the Channel Manager (P3) for managing the Multi-Channel Network; the Capability Manager (P25) for dynamic, fine-grained access control; the Policy Engine for enforcing security policies; the Resource Manager (P9, P10) for AI-powered resource allocation and predictive scaling; the DTG (Dynamic Trust Gateway – P28) mediating communication between the ATN (Authenticated Trust Network – P3) and the highly isolated STN (Sovereign Trust Network – P27); the SIZCF (Secure Inter-Zone Collaboration Framework – P22) for secure inter-zone communication; the UI Integration module; a Media Handling subsystem (P30, P31, P32) for spatiotemporal digest verification, privacy blurring, and secure media routing; the Secure Boot process (P1, P13, P33); the Anomaly Detector (P7); and the Governance AI (P15) for auditing and conflict resolution. All significant events are logged to the Decentralized Ledger (DLT – P13, P15) and correlated with a 3D Microstructure Audit Trail (P14, P17) via MDATS (Multi-Dimensional Audit Trail System – P17), ensuring tamper-evident, multi-dimensional auditing.

The IES Cluster (Isolated Execution Stacks – P1) forms the foundation for ASKA’s strong isolation. Multiple independent IES instances, each with dedicated hardware resources (CPU, memory, I/O, network), run applications and processes in complete isolation. IES instances can be dynamically partitioned into smaller child-IES instances (P1) for granular control and adaptive scaling (P10). Secure communication within and between IES instances is facilitated by Secure Channels (P3), with capability-aware forwarding (P26) ensuring fine-grained access control. IES instances utilize ZKEE (Zero-Knowledge Execution Environment – P6) for private computation and the HESE-DAR (Hardware-Enforced Secure Encrypted Enclave for Data at Rest – P24) for secure data storage. Local security monitoring is handled by Local MSMs (Local Security Meshes – P2), reporting to the MSM (Master Security Mesh – P2) for overall system-wide monitoring and analysis. The Secure UI Kernel (P11) provides a trusted user interface, isolated from the underlying system.

ASKA's network infrastructure includes the ATN for internal authenticated communication, the STN for highly sensitive data, and the DTG mediating between them. The Firewall (P3) protects against unauthorized external access. External communication and inter-zone collaboration are facilitated by the SIZCF (P22), leveraging the Multi-Channel Network (P3) and potentially quantum-resistant communication (P5). The IAMA module (P16) proactively identifies and mitigates vulnerabilities in connected legacy systems.

The Resource Manager (P9, P10) dynamically allocates resources based on trust levels (DTMS), security policies (Policy Engine), and workload demands, using multipath optimization for efficiency and resilience. Secure data storage is provided by HESE-DAR (P24), employing post-quantum cryptography (P5). Out-of-band data integrity verification is provided by diverse auxiliary memory systems: QEAMS (Quantum-Entangled Auxiliary Memory System – P34a), SAMS (Spatiotemporal Auxiliary Memory System – P34b), and PR-SAMS (Passively Radiative Spatiotemporal Auxiliary Memory System – P34c). The Media Handling subsystem (P30, P31, P32) uses a Media Router, Spatiotemporal Digest generation and verification, and Decentralized Privacy Blurring to manage media data securely and privately. The entire system is underpinned by decentralized governance (P13, P15), with all actions logged on the DLT and the MDATS (P17) for transparency and accountability, further enhanced by the 3D Microstructure Audit Trail (P14, P17). The QE-OTP (Quantum-Entangled One-Time Pad – P29) provides an additional layer of security for sensitive communications.

Diagram 1: Detailed Description

1. ASKA Hub: The Hub isn't merely a central point; it's the system's brain, orchestrating complex interactions for robust, adaptive security.

AESDS (P16) & IAMA (P16): AESDS isn't just about updates; it's about continuous evolution. Its AI engine, fueled by a knowledge base, performance metrics, user feedback, and threat intelligence, crafts optimized, secure code. IAMA, a crucial AESDS module, analyzes connected legacy systems, building isomorphic models to predict vulnerabilities and proactively generate security patches for ASKA, integrating seamlessly with the STN's isomorphic security stack. This creates a feedback loop where legacy system weaknesses strengthen ASKA.

DTMS (P4): DTMS is the heart of trust. It dynamically calculates trust scores, not just for IES instances, but for all components, influencing access control, resource allocation, and software updates. Its decentralized zone management (DZM) further enhances security by allowing localized trust policies via mini-TRCs stored on tamper-evident media, minimizing the impact of breaches. The consensus mechanisms and integration with the decentralized ledger ensure trust decisions are both secure and transparent.

Channel Manager (P3): This module dynamically manages the multi-channel network, ensuring secure and efficient communication between components. It configures channels based on trust levels, security policies, and real-time conditions, creating adaptable pathways.

Capability Manager (P25): This is where fine-grained control comes alive. The Capability Manager dynamically issues, revokes, and modifies capabilities based on the DTMS's trust evaluations, ensuring least-privilege access and preventing unauthorized actions. This restricts the "blast radius" of potential compromises.

Policy Engine: Enforces system-wide policies, working in concert with the Capability Manager to control access, data flow, and overall system behavior.

Resource Manager (P9, P10): This dynamic orchestrator allocates resources (CPU, memory, I/O, bandwidth) intelligently, optimizing for performance and security. Its AI-powered predictive capabilities anticipate resource demands and adjust allocation dynamically, even leveraging multipath optimization for resilience and efficiency. Secure resource borrowing (SRBM - P9) enables efficient resource sharing between IES instances without compromising isolation.

DTG (P28): The DTG is a critical security gateway. It mediates all communication between the ATN (for general communication) and the STN (for highly sensitive operations), dynamically provisioning channels, performing deep packet inspection, sanitizing data, and enforcing access control policies based on aggregated capabilities. This separation enhances the STN's protection.

SIZCF (P22): This framework enables secure collaboration not just within a ASKA instance, but between different zones, securely exchanging data and coordinating actions while preserving zone-specific security policies. It leverages secure communication channels, privacy-preserving techniques (like differential privacy or homomorphic encryption), and distributed ledger synchronization to maintain consistency and auditability.

UI Integration: This module provides the bridge to the Secure UI Kernel (P11), ensuring secure and trusted communication between user interfaces and the ASKA core.

Media Handling (P30, P31, P32): This subsystem integrates specialized modules for secure media processing. The Media Router directs media data to the appropriate processing components. The Spatiotemporal Digest (P30, P31) module generates and verifies digests, linking media authenticity to physical context. Decentralized Privacy Blurring (P32) automatically blurs faces based on opt-in settings.

Secure Boot (P1, P13, P33): Secure Boot is the bedrock of system integrity. It leverages a Hardware Root of Trust, verifies boot components (using TRCs), and establishes a chain of trust during IES initialization. Its integration with the DTMS dynamically updates trust levels based on the attested boot state, enhancing security. The tamper-evident audit trail, optionally using 3D microstructures, ensures the boot process can be verified.

Anomaly Detector (P7): The Anomaly Detector isn't just a passive observer; it actively monitors system behavior, resource utilization, and communication patterns, employing AI/ML techniques to detect anomalies and potential threats, even including timing side-channel attacks. Its hardware-enforced isolation and self-healing capabilities trigger automated responses (resetting components, reallocating resources, or redeploying software) based on zone-specific policies, enhancing resilience.

Governance AI (P15): The Governance AI continuously analyzes audit trails (both digital and physical), security policies, and TRC information. It leverages AI to identify potential weaknesses, recommend policy adjustments, and automate conflict resolution, improving security posture dynamically.

2. IES Cluster (P1): The IES Cluster is the foundation of ASKA's isolation, where applications and processes run in their own secure worlds.

IES Instances: Each IES instance provides complete hardware isolation, minimizing the "blast radius" of attacks. They interact securely with other components (secure channels, ZKEE, HESE-DAR, Local MSM) using dynamic capabilities, ensuring controlled access and data flow.

Dynamic Partitioning: IES instances can be dynamically divided into smaller, isolated child-IES instances, adapting to workload and security demands in real-time. This partitioning, governed by the Resource Manager (P9, P10), enables granular resource allocation and flexible isolation.

ZKEE Instances (P6): ZKEE instances within the IES cluster enable privacy-preserving computation on encrypted data using zero-knowledge proofs.

Local MSM (P2): Each IES has a dedicated Local Security Mesh for continuous internal monitoring, providing granular visibility into behavior and resource usage, reporting anomalies to the MSM.

Secure UI Kernel (P11): The Secure UI Kernel runs within the IES Cluster, providing a trusted interface isolated from potentially compromised applications. Its multi-region display buffer with dynamic trust levels and hardware-enforced control-flow integrity protect against UI manipulation.

3. Network & Communication: This subsystem ensures data moves securely and efficiently within and beyond ASKA.

Secure Channels (P3): The multi-channel network provides physically segregated channels, each with a dedicated firewall instance, creating secure and independent communication pathways. Dynamic routing (P3) and capability-aware forwarding enhance flexibility and prevent unauthorized access. The use of QE-OTP (P29) further enhances security.

STN (P27): The STN is a fortress for sensitive data. Its isolated data plane, minimal control plane coupling, and multi-level control system provide robust protection against attacks. The key recovery system, leveraging a network of synchronized nodes and MFA, ensures data availability even in compromised scenarios. IAMA's continuous monitoring and proactive patching further enhance the STN’s security.

ATN (P3): The ATN provides secure and authenticated communication for authorized entities within ASKA, leveraging the multi-channel network's security features.

DTG (P28): The DTG isn't a simple gateway; it's a dynamic security checkpoint between the ATN and STN. It dynamically provisions channels, performs deep packet inspection, sanitizes data, and aggregates capabilities from multiple paths, enforcing fine-grained access control and protecting the STN's isolated environment. Its adaptive security measures and integration with the decentralized governance framework enhance resilience and transparency.

Firewall (P3): The Firewall isn't just a boundary; it's an active defense. It enforces access control policies, filters malicious traffic, and protects against various network-based attacks. Its out-of-band nature enhances security by isolating it from potential compromises within ASKA.

IAMA (P16): IAMA, besides its role in AESDS, monitors communication patterns and security events at the network level. It analyzes network traffic metadata for anomalies, enhancing the effectiveness of intrusion detection and allowing adaptive responses to emerging network threats.

4. Security Monitoring & Response (MSM - P2): This subsystem is ASKA's immune system, constantly vigilant and responsive to threats.

MSM (P2): The Master Security Mesh is the central security monitoring hub, aggregating telemetry from Local MSMs and coordinating system-wide responses. It analyzes events, correlates data from various sources (including the Anomaly Detector), and triggers automated responses (isolation, self-healing, resource reallocation) based on DTMS trust levels and security policies.

Local MSM (P2): Each IES instance has its dedicated Local Security Mesh, ensuring that monitoring occurs even within isolated environments. They provide granular security telemetry to the MSM, enhancing overall system visibility.

Anomaly Detector (P7): This module isn't just a detector; it's a first responder. Using AI/ML and hardware-enforced mechanisms, it analyzes system behavior, resource utilization, and communication patterns, detecting anomalies and potential threats, including timing side-channel attacks. Upon detection, it triggers automated responses (isolation, self-healing) based on zonal policies and DTMS trust levels. Secure SCMP reporting ensures that alerts are transmitted securely and tamper-proof, providing valuable data for forensic analysis.

DTMS Integration: The Anomaly Detector’s findings directly influence the DTMS, dynamically adjusting trust levels and access control policies in real-time. This creates a feedback loop: anomalies reduce trust, limiting access and preventing further damage.

5. Data & Resources: This area focuses on securely managing data and allocating resources efficiently.

HESE-DAR (P24): HESE-DAR is more than just encryption; it's a data vault. It provides hardware-level encryption for data at rest, using post-quantum cryptography for long-term security. Granular access control (tied to the DTMS), key management within the enclave, and tamper detection mechanisms create a robust defense against unauthorized access or modification. Dynamic resource allocation optimizes performance.

Secure Storage: Secure Storage represents the broader storage infrastructure within ASKA, leveraging HESE-DAR and other security features to protect data across different storage tiers.

Resource Manager (P9, P10): The Resource Manager dynamically allocates resources to IES instances, HESE-DAR, and other components. It utilizes AI-powered predictive algorithms (P10) to anticipate demands, optimizing for performance and efficiency, even leveraging multipath optimization and declarative policies for flexible and secure resource sharing.

QEAMS (P34a), SAMS (P34b), PR-SAMS (P34c): These out-of-band integrity verification systems offer independent confirmation of data integrity without impacting performance. QEAMS utilizes quantum entanglement, creating a tamper-evident link between primary storage and auxiliary memory elements. SAMS leverages spatiotemporal metadata captured during data writes. PR-SAMS offers a low-power version using passive radiative sensing.

6. UI & External Systems: This subsystem manages the delicate balance between user interaction, external collaboration, and system security.

UI Integration: This module ensures secure and trusted communication between the Secure UI Kernel and the ASKA Hub. It manages UI rendering policies, access controls, and session-specific data flows, protecting the system from UI-based attacks while facilitating seamless user interaction.

Secure UI Kernel (P11): The Secure UI Kernel isn't just an interface; it's a secure gateway. It operates in hardware isolation, employing zonal isolation and hardware-enforced control-flow integrity to protect against attacks. Declarative policy-based rendering manages what's displayed, ensuring only authorized information is visible to users. Its multi-region display buffer with dynamic trust levels prevents information leakage between different security contexts.

SIZCF (P22): This framework isn't about simple communication; it's about secure collaboration between ASKA zones. It enables data sharing, resource exchange, and coordinated actions while enforcing zone-specific security policies and privacy-preserving techniques (differential privacy, homomorphic encryption). Distributed ledger synchronization ensures consistency and auditability across zones.

External Systems: This represents the broader ecosystem outside ASKA. Interaction with these systems is carefully mediated by the Firewall, DTG, and SIZCF, ensuring that external communication doesn't compromise ASKA’s internal security.

7. Media Handling: This subsystem provides specialized and secure processing for media data.

Media Router: The Media Router intelligently routes media streams to appropriate processing modules within ASKA, optimizing for performance, security, and privacy. It integrates with the Resource Manager (P9, P10) for dynamic resource allocation and with IAMA (P16) for security monitoring.

Spatiotemporal Digest (P30, P31): This module generates and verifies spatiotemporal digests for raster content (audio, images, video), providing robust evidence of authenticity and integrity. It captures environmental metadata during content creation, creating a tamper-evident link to physical reality, storing these digests securely within HESE-DAR.

Privacy Blurring (P32): This module enhances user privacy by implementing a decentralized privacy blurring standard, automatically blurring faces and other identifiable information in media content based on user-defined privacy settings recorded on a decentralized ledger. AI agents on capture devices perform local blurring based on policy, while ASKA integrates this into its broader security architecture using verified, trusted mechanisms. It leverages spatiotemporal data (P31) to enhance integrity and prevent manipulation.

4. Security Monitoring & Response (MSM - P2): This continuously monitors system activity and triggers automated responses to security threats.

MSM (P2): The Master Security Mesh aggregates security telemetry from the Local MSMs, providing a holistic view of system security. It correlates events, analyzes patterns, and triggers system-wide responses based on DTMS trust levels and security policies. Its hierarchical structure and integration with the Anomaly Detector enable efficient and scalable security monitoring.

Local MSM (P2): Each IES instance has a dedicated Local MSM, providing granular monitoring within isolated environments. These Local MSMs report anomalies and security events to the Master MSM, enhancing overall system visibility and enabling localized responses.

Anomaly Detector (P7): This module actively analyzes system behavior, resource utilization, and communication patterns using AI/ML and hardware-enforced mechanisms. It detects anomalies, including timing side-channel attacks, and triggers automated responses such as isolation and self-healing. Secure SCMP reporting ensures tamper-proof transmission of alerts. Its integration with the DTMS dynamically adjusts trust levels based on detected anomalies.

DTMS Integration: The Anomaly Detector and MSM directly influence the DTMS, creating a feedback loop: detected anomalies reduce trust scores, which in turn restrict access and limit the potential damage of security breaches.

5. Data & Resources: This layer manages data securely and allocates resources dynamically.

HESE-DAR (P24): HESE-DAR provides hardware-encrypted, isolated storage for sensitive data. It integrates granular access control (managed by DTMS), key management within the enclave (using PQC), and anti-tamper mechanisms. Dynamic resource allocation optimizes storage utilization.

Secure Storage: Secure Storage represents the broader storage infrastructure, which may incorporate multiple HESE-DAR instances, traditional encrypted storage, and other security measures, all managed by ASKA policies.

Resource Manager (P9, P10): The Resource Manager dynamically allocates CPU, memory, I/O, and bandwidth to IES instances, HESE-DAR, and other components. It uses AI-powered predictive algorithms to anticipate demands and optimize resource utilization, leveraging multipath optimization and declarative policies for secure and flexible resource sharing. It also manages secure resource borrowing (SRBM) between IES instances.

QEAMS (P34a), SAMS (P34b), PR-SAMS (P34c): These provide diverse out-of-band integrity verification methods. QEAMS uses quantum entanglement for tamper-proof verification. SAMS leverages spatiotemporal metadata, linking data integrity to its creation context. PR-SAMS offers a low-power version using passive radiative sensing. These systems work independently of primary storage, adding an extra layer of security.

6. UI & External Systems: This subsystem handles secure user interaction and controlled external communication.

UI Integration: This module manages the secure interface between the Secure UI Kernel and the ASKA Hub. It handles UI rendering policies, access controls, and secure data flows, protecting against UI-based attacks.

Secure UI Kernel (P11): The Secure UI Kernel operates in hardware isolation, using zonal isolation and hardware-enforced control-flow integrity to protect against attacks. Declarative policies govern rendering, and a multi-region display buffer with dynamic trust levels prevents information leakage.

SIZCF (P22): The SIZCF provides a secure framework for inter-zone collaboration, enabling controlled data sharing and coordination between ASKA zones while maintaining zone-specific security policies. It uses secure communication channels, privacy-preserving data exchange, and distributed ledger synchronization.

External Systems: Interaction with external systems is carefully controlled by the Firewall, DTG, and SIZCF, preventing external threats from compromising ASKA.

7. Media Handling: This subsystem provides specialized, secure mechanisms for handling media data.

Media Router: The Media Router directs media streams to the appropriate modules, integrating with the Resource Manager and IAMA for resource allocation and security.

Spatiotemporal Digest (P30, P31): This module generates and verifies spatiotemporal digests, linking media integrity to its physical creation context using environmental metadata, strengthening authenticity claims and ensuring tamper-evidence.

Privacy Blurring (P32): This AI-driven module provides automated privacy protection by blurring identifiable information in media based on decentralized, user-defined settings. It integrates with ASKA's core security and leverages spatiotemporal data for enhanced integrity. It interacts with a Decentralized Privacy Ledger maintained by a Government Trusted Authority, implementing privacy regulations in a decentralized manner.

Diagram 3: User Interface Data Flow

graph

subgraph UserInterface["User Interface"]

User["User (P11, P23)"] --> SecureUI["Secure UI Kernel (P11)"]

SecureUI --> UI_Monitor["UI Monitor (P37)"]

UI_Monitor --> InputSanitizer["Input Sanitizer (P11, P37)"]

MFA["MFA (P23)"] --> SecureUI

end

subgraph ASKACore["ASKA Core"]

InputSanitizer -- Authenticated & Sanitized Input --> IESCluster["IES Cluster (P1)"]

IESCluster --> AppLayer["Application Layer"]

AppLayer --> IES1["IES 1"]

AppLayer --> IES2["IES 2"]

AppLayer --> IESn["IES n"]

IES1 --> App1["App 1"]

IES2 --> App2["App 2"]

IESn --> Appn["App n"]

IESCluster --> DTMS["DTMS (P4)"]

DTMS --> PolicyEngine["Policy Engine (P4)"]

DTMS --> CapMgr["Capability Mgr (P25)"]

DTMS --> ResourceMgr["Resource Mgr (P9, P10)"]

App1 -.-> CapMgr

App2 -.-> CapMgr

Appn -.-> CapMgr

App1 --> ResourceMgr

App2 --> ResourceMgr

Appn --> ResourceMgr

subgraph MediaHandling["Media Handling"]

ResourceMgr --> MediaRouter["Media Router"]

MediaRouter --> SpatiotemporalDigest["Spatiotemporal Digest (P30, P31)"]

MediaRouter --> PrivacyBlurring["Privacy Blurring (P32)"]

end

subgraph SecureCommunications["Secure Communications"]

IESCluster

end

subgraph SecurityMonitoringResponse["App Instances"]

IES1

IES2

IESn

end

subgraph AuditingGovernance["Auditing & Governance"]

UI_Monitor --> DLT["Decentralized Ledger (P13, P15, P17, P37)"]

end

classDef high_level fill:#f9f,stroke:#333,stroke-width:4px

classDef medium_level fill:#ccf,stroke:#333,stroke-width:2px

classDef low_level fill:#aaf,stroke:#333,stroke-width:2px

classDef data fill:#afa,stroke:#333,stroke-width:2px

classDef ui fill:#ffa,stroke:#333,stroke-width:2px

classDef audit fill:#ccf,stroke:#333,stroke-width:2px

classDef network fill:#eec,stroke:#333,stroke-width:2px

classDef external fill:#d8d8d8,stroke:#333,stroke-width:2px

linkStyle default stroke:#ccc,stroke-width:2px

Description for Diagram 3: User Interface Data Flows

This diagram depicts the ASKA architecture, illustrating the interplay between user interaction, application execution, core system services, data management, network communication, and security monitoring/auditing.

I. User Interaction Layer:

User interaction is strictly confined to the Secure UI Kernel, ensuring a secure and controlled interface. This component, detailed in Patent 11, leverages hardware and zonal isolation, CFI (Control-Flow Integrity) to prevent code injection, and declarative policies for managing user access and privileges. Communication with the underlying system is unidirectional, preventing unauthorized feedback channels and enhancing protection against reverse engineering attacks.

II. Application Layer:

Applications run within isolated execution environments, specifically IES (Isolated Execution Stacks) instances (Patent 1). Each IES offers full-stack hardware isolation (CPU, memory, I/O, network), minimizing the blast radius of potential security compromises and preventing lateral movement. ASKA allows for dynamic partitioning of IES instances into smaller, isolated child-IES instances to optimize resource usage and further enhance security. The Secure UI Kernel interacts with these IES instances concurrently, managing and controlling access based on user permissions and trust levels.

III. ASKA Core:

The core system manages trust and resource allocation, centered around the DTMS (Dynamic Trust Management System) (Patent 4). The DTMS uses TRCs (Trust Root Configurations) (Patent 1) stored on the decentralized ledger to manage trust relationships, dynamically adjusting trust levels based on real-time data from security monitoring (Local MSMs, Anomaly Detector), performance metrics, and system health. This dynamic adaptation influences access control, resource allocation (via the Resource Manager), and software updates (via AESDS).

The DTMS directly interacts with:

Capability Manager (CapMgr) (Patent 25): Dynamically manages capabilities for inter-IES communication, regulating access rights and resource sharing.
Resource Manager (ResourceMgr) (Patent 9, 10): Coordinates resource allocation based on workload demands, DTMS trust levels, and policy constraints. It also interacts with the newly introduced Media Router module (see Section V).
Policy Engine: Enforces security policies throughout the system, impacting access control, communication, and resource allocation.

IV. Data & Resource Management:

Applications interact with the Resource Manager for resource allocation. The HESE-DAR (Hardware-Enforced Secure Encrypted Enclave for Data at Rest) (Patent 24) is used for encrypting data at rest, employing hardware-level encryption and anti-tamper mechanisms for securing sensitive data.

V. Media Handling:

This section showcases the newly integrated media processing capabilities.

Media Router: This component dynamically routes media data (audio, video, images) to the appropriate processing modules, determined by factors like content type, security policies, and trust levels from the DTMS. The connection to the Resource Manager indicates that it utilizes system resources for processing.
Spatiotemporal Digest (Patent 30, 31): This module generates and verifies spatiotemporal digests, linking media data to its physical creation context for tamper-evidence and authenticity verification. The data is securely stored and processed within the HESE-DAR.
Decentralized Privacy Blurring (Patent 32): This module automatically blurs individuals' faces in media data based on their opt-in status on a decentralized ledger, ensuring privacy while maintaining data integrity. It interacts directly with the Secure UI Kernel to manage the presentation of blurred content to the user.

VI. Network & Communication:

IES instances communicate via Secure Channels (Patent 3) which are managed by the Channel Manager. These channels provide secure communication pathways, potentially using quantum-resistant communication (Patent 5), and enforce fine-grained access control using capability-aware forwarding (Patent 2, 26). The DTG (Dynamic Trust Gateway) (Patent 28) mediates communication between the ATN (Authenticated Trust Network) and STN (Sovereign Trust Network), dynamically provisioning secure channels based on real-time conditions and trust levels.

VII. External Systems:

ASKA interacts with external systems through the ATN (for authenticated communication) and the STN (for highly sensitive data). The SIZCF (Secure Inter-Zone Collaboration Framework) (Patent 22) facilitates secure collaboration and data exchange between different ASKA zones.

VIII. Monitoring & Auditing:

ASKA employs a hierarchical security monitoring system:

Local Security Mesh (LocalMSM) (Patent 2): Monitors each IES instance, reporting anomalies to the MSM (Master Security Mesh).
MSM (Hierarchical Security Mesh) (Patent 2): Aggregates security telemetry, performing system-wide monitoring and anomaly detection.
Anomaly Detector (Patent 7): Identifies anomalies in the aggregated data, using advanced AI/ML techniques.
MDATS (Multi-Dimensional Audit Trail System) (Patent 17): Records all significant events, creating both digital and physical (3D Microstructure) audit trails.
DLT (Decentralized Ledger) (Patent 13, 15): Stores audit trails and governance data.

Diagram 4: Dynamic Trust Gateway (DTG) Architecture

graph

subgraph "Authenticated Trust Network (ATN)"

ATN([ATN])

end

subgraph "Dynamic Trust Gateway (DTG)"

DTG([DTG])

DTG --> Channel_Provisioner[Channel Provisioner];

DTG --> Capability_Aggregator[Capability Aggregator];

DTG --> Security_Monitor[Security Monitor];

DTG --> Audit_Logger[Audit Logger];

style DTG fill:#ccf,stroke:#333,stroke-width:1px

end

subgraph "Sovereign Trust Network (STN)"

STN([STN])

STN --> Secure_Channel1((Secure Channel 1));

STN --> Secure_Channel2((Secure Channel 2));

STN --> Secure_ChannelN((Secure Channel N));

style STN fill:#ccf,stroke:#333,stroke-width:1px

end

ATN --> DTG;

DTG --> STN;

Channel_Provisioner --> Secure_Channel1;

Channel_Provisioner --> Secure_Channel2;

Channel_Provisioner --> Secure_ChannelN;

Secure_Channel1 --> Capability_Aggregator;

Secure_Channel2 --> Capability_Aggregator;

Secure_ChannelN --> Capability_Aggregator;

Capability_Aggregator --> STN;

Security_Monitor --> Audit_Logger;

Security_Monitor --> Channel_Provisioner;

Audit_Logger --> DLT((Decentralized Ledger));

style Audit_Logger fill:#aaf,stroke:#333,stroke-width:1px

style DLT fill:#aaf,stroke:#333,stroke-width:1px

classDef high_level fill:#f9f,stroke:#333,stroke-width:2px

classDef medium_level fill:#ccf,stroke:#333,stroke-width:1px

classDef low_level fill:#aaf,stroke:#333,stroke-width:1px

Description for Diagram 4: Dynamic Trust Gateway (DTG) Architecture

This diagram details the architecture of the Dynamic Trust Gateway (DTG) (P28), a critical component of ASKA responsible for mediating communication between the Authenticated Trust Network (ATN) (P3) and the Sovereign Trust Network (STN) (P27). The DTG's design emphasizes dynamic adaptability, multi-layered security, and decentralized governance to ensure secure and controlled data flow between these distinct trust domains. The design incorporates several novel features to improve resilience and transparency over traditional gateway architectures.

I. Authenticated Trust Network (ATN):

The ATN represents the ASKA's internal network with authenticated communication (P3). It provides secure pathways for communication between authorized entities within the ASKA environment, but with a lower security posture than the STN. Each communication path within the ATN leverages a dedicated firewall instance, preventing cross-channel interference.

II. Dynamic Trust Gateway (DTG):

The DTG sits at the interface between the ATN and STN, providing dynamic and secure communication mediation. The DTG's architecture consists of the following key modules:

Channel Provisioner: This module dynamically provisions secure communication channels (P3, P28) between the ATN and STN based on real-time factors. These factors include trust levels determined by the DTMS (P4), resource availability (P10), and prevailing security policies (P13, P15). Each channel uses a unique set of capabilities (P25) to define allowed communication parameters. The dynamic selection of paths uses techniques such as multi-path capability aggregation (P28) and path selection based on availability, performance, and trust.

Capability Aggregator: This module consolidates capabilities (P25, P26) from multiple communication paths, allowing flexible access control and providing resilience against path failures. It resolves any capability conflicts to provide a unified access policy to the STN.

Security Monitor: This module continuously monitors communication for security threats and anomalies. It employs deep packet inspection (DPI - P28) to enforce security policies, detect malicious traffic, and prevent unauthorized access. The Security Monitor directly integrates with the DTMS (P4) for trust-based decisions. It also incorporates mechanisms for detecting timing side-channel attacks (P7).

Audit Logger: This module records all significant communication events, including channel provisioning, capability assignments, security assessments, and policy updates. These logs are sent to the Decentralized Ledger (DLT) (P13, P15) for permanent, tamper-evident storage and are correlated with physical microstructures (P14,P17) via the Multi-Dimensional Audit Trail System (MDATS) (P17).

III. Sovereign Trust Network (STN):

The STN (P27) represents the high-security network, designed for sensitive data and cryptographic keys. It uses dedicated secure channels to maintain its isolation and prevent data leakage. This is enhanced by its isolated data plane and minimal control plane coupling, preventing common attack vectors.

Relationships and Interactions:

The diagram illustrates the data flow and control mechanisms within the DTG:

The ATN initiates communication requests that are processed by the DTG.
The Channel Provisioner establishes the communication channels based on real-time analysis, incorporating multi-path aggregation and capabilities (P2, P25, P26).
The Capability Aggregator consolidates the capabilities, ensuring consistent security and access control to the STN.
The Security Monitor continuously performs DPI and anomaly detection.
The Audit Logger maintains a tamper-proof audit trail of all DTG activities, logged to the DLT.
The Security Monitor reports potential breaches or policy violations to the Audit Logger, which logs them to the DLT.
The Security Monitor also provides feedback to the Channel Provisioner to dynamically adjust channel configurations in response to threats.

This architecture provides a highly secure and adaptable gateway solution for managing communication between networks with varying security requirements. The use of dynamic channel provisioning, multi-path aggregation, deep packet inspection, and comprehensive auditing creates a resilient system capable of responding to changing threat landscapes and maintaining data integrity and confidentiality. Decentralized governance ensures transparency and accountability.

Diagram 5: ASKA Detailed Overview

graph LR

subgraph ASKA

subgraph IES["IES (P1)"]

direction LR

IESCore[Core] --> ChildIES1[Child IES]

IESCore --> ChildIES2[Child IES]

ChildIES1 <--> ChildIES2(("CE-PCFS (P26)"))

ChildIES1 -.-> HESE-DAR["HESE-DAR (P24)"]

ChildIES2 -.-> HESE-DAR

ChildIES1 <--> ZKEE["ZKEE (P6)"]

ChildIES2 <--> ZKEE

ResMgr["Resource Mgr (P9, P10)"] <--> ChildIES1

ResMgr <--> ChildIES2

SecMon["Security Monitor (P7)"] --> ChildIES1

SecMon --> ChildIES2

style IES fill:#f9f,stroke:#333,stroke-width:2px

end

Hub[ASKA Hub] --> IES

Hub --> DTMS["DTMS (P4)"]

Hub --> AESDS["AESDS (P16)"]

Hub --> DTG["DTG (P28)"]

Hub -.-> SIZCF["SIZCF (P22)"]

Hub -.-> SHVS["SHVS (P18)"]

DTMS -.-> IES

DTMS -.-> DTG

DTMS -.-> STN["STN (P27)"]

DTMS <--> DZMS["DZMS (P4)"]

AESDS --> DTG

AESDS --> IES

AESDS --> STN

IAMA["IAMA (P16)"] --> STN

DTG <--> STN

DTG <--> ATN["ATN (P3)"]

style Hub fill:#ccf,stroke:#333,stroke-width:2px

end

subgraph External Systems/Legacy

Legacy[Legacy System] --> IAMA

Ext1[External System/Zone] <--> SIZCF

Ext2[External High-Trust] <--> STN

end

subgraph Supporting Technologies

DLT["Decentralized Ledger (P13, P15)"] -.-> Hub

DLT -.-> DTMS

DLT -.-> AESDS

DLT -.-> SIZCF

MDATS["MDATS (P17)"] -.-> Hub

MDATS -.-> IES

MDATS -.-> DTG

MSM["MSM (P2)"] -.-> IES

MSM -.-> DTG

MSM -.-> STN

MultiChannel["Multi-Channel Network (P3)"] -.-> ATN

MultiChannel -.-> DTG

Quantum["Quantum-Resistant Comm (P5)"] -.-> DTG

Quantum -.-> STN

SecureUI["Secure UI Kernel (P11)"] -.-> IES

Chiplets["Chiplet Arch (P12)"] -.-> IES

Chiplets -.-> HESE-DAR

FedLearn["Federated Learning (P19)"] -.-> IES

SDE["Secure Data Enclaves (P20)"] -.-> IES

MFA["Adaptive MFA (P23)"] -.-> Hub

MFA -.-> STN

CapMgr["Capability Mgr (P25)"] -.-> IES

CapMgr -.-> DTG

style DLT fill:#ffc,stroke:#333,stroke-width:2px

end

linkStyle 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 stroke:#000,stroke-width:1.5px;

linkStyle 21,22,23 stroke:#f00,stroke-width:1.5px;

Description for Diagram 5: ASKA Detailed Overview

This patent describes ASKA, a novel system architecture for establishing and maintaining secure communication and collaboration amongst multiple isolated execution environments (IEEs) while integrating with legacy systems and external entities. ASKA employs a multi-layered, dynamically adaptable approach based on hardware-enforced isolation, decentralized trust management, and AI-driven security. This architecture incorporates several innovative features, including a modular Isolated Execution Stack (IES), a Dynamic Trust Management System (DTMS), an Automated Evolutionary Software Development System (AESDS), and a Sovereign Trust Network (STN), all interacting through a central ASKA Hub.

1. System Architecture Overview:

ASKA comprises a hierarchical system architecture. At its core is a set of Modular Isolated Execution Stacks (IES, P1) operating in complete hardware isolation (CPU, memory, I/O, network). Each IES can be dynamically partitioned (P1) into smaller, isolated Child IES instances to handle workloads and security requirements, enabling fine-grained control over resource allocation and compartmentalization. Child IES instances communicate using the Capability-Enhanced PCFS (CE-PCFS, P26) protocol, which incorporates capabilities directly into packet hop fields for flexible, policy-driven access control and reduced dependency on centralized trust management. Secure Resource Borrowing (SRBM, P9) enables the sharing of idle resources among Child IES instances.

A central ASKA Hub orchestrates and manages all components. This includes: IES lifecycle management (P1), resource allocation (P9, P10), software updates (P16), policy enforcement (P4), and inter-zone communication (P22). The Hub also houses the Capability Manager (P25), dynamically managing capabilities and access rights, and the Channel Manager (P3) controlling the Multi-Channel Network (P3). The Dynamic Trust Management System (DTMS, P4) manages trust relationships between IES instances and zones (P18), using dynamically generated Trust Root Configurations (TRCs) stored on a Decentralized Ledger (P13, P15). The Decentralized Ledger (DLT) additionally facilitates transparent and auditable governance (P13, P15), logging policy changes, security events, and other significant actions.

The Automated Evolutionary Software Development System (AESDS, P16) uses an AI engine to automatically generate and refine software for all ASKA components, including proactive security updates and dynamic policy adaptations. This process is integrated with an Isomorphic Architecture Monitoring and Adaptation (IAMA, P16) module, which continuously monitors the connected legacy systems to predict and mitigate vulnerabilities.

The Sovereign Trust Network (STN, P27) represents a highly secure and isolated data plane, crucial for handling sensitive information. Data transfer to and from the STN is mediated by the Dynamic Trust Gateway (DTG, P28). The DTG employs dynamic channel provisioning, multi-path capability aggregation (P28), Deep Packet Inspection (DPI), and data sanitization. It provides high assurance of data integrity and confidentiality in communication with the STN. The Authenticated Trust Network (ATN, P3) is connected to the DTG providing a secure pathway for authenticated communication.

The Multi-Dimensional Audit Trail System (MDATS, P17) combines digital audit logs (P13, P15) with a 3D-printed microstructure audit trail (P14), providing comprehensive and tamper-evident auditing capabilities. The Security Monitoring System (MSM, P2) and the Secure Hyper-Virtualization System (SHVS, P18) further strengthen the system's security posture by providing continuous monitoring and enabling secure collaborative contexts between IES instances across zones, respectively. Zero-Knowledge Execution Environments (ZKEE, P6) allow computations on encrypted data.

2. Key Innovative Features:

Modular Isolated Execution Stacks (IES, P1): Hardware-enforced isolation, dynamic partitioning, hierarchical zones with mini-TRCs, and secure resource borrowing (SRBM, P9) create isolated execution environments, dramatically minimizing the impact of any security compromise.

Dynamic Trust Management System (DTMS, P4): Dynamically adapts trust levels based on real-time behavior and security posture, enforcing fine-grained access control and resource allocation.

Automated Evolutionary Software Development System (AESDS, P16): AI-driven software evolution and proactive security patching reduces vulnerabilities and ensures continuous adaptation.

Sovereign Trust Network (STN, P27): Provides a highly secure and isolated data plane for handling sensitive information, employing multi-level control system with key recovery capabilities.

Dynamic Trust Gateway (DTG, P28): Dynamically manages secure communication between ATN and STN, employing multi-path capability aggregation for resilience and fine-grained access control.

Multi-Dimensional Audit Trail System (MDATS, P17): Combines digital and physical audit trails for tamper-evident and comprehensive logging.

Capability-Enhanced PCFS (CE-PCFS, P26): Enhances PCFS with fine-grained access control capabilities within hop fields, reducing dependency on centralized trust management and increasing efficiency.

Diagram 6: Security Monitoring and Response

graph LR

subgraph "ASKA Security Monitoring"

A[ASKA Hub] --> B(Master Security Mesh);

B --> C(Security Policy Engine);

C --> D(DTMS);

D --> E(Access Control);

B --> F(Anomaly Detection Engine);

F --> G(Alerting System);

G --> A;

F --> H(Response System);

H --> I(Isolator);

H --> J(Self-Healer);

H --> K(Resource Manager);

I --> SecureOS;

J --> SecureOS;

K --> SecureOS;

style B fill:#ccf,stroke:#333,stroke-width:2px

style F fill:#ccf,stroke:#333,stroke-width:2px

style G fill:#ccf,stroke:#333,stroke-width:2px

style H fill:#ccf,stroke:#333,stroke-width:2px

style I fill:#ccf,stroke:#333,stroke-width:2px

style J fill:#ccf,stroke:#333,stroke-width:2px

style K fill:#ccf,stroke:#333,stroke-width:2px

subgraph "Local Security Meshes (Patent 2)"

L[Local MSM 1] --> B;

M[Local MSM 2] --> B;

N[... Local MSM n] --> B;

style L fill:#bbf,stroke:#333,stroke-width:2px

style M fill:#bbf,stroke:#333,stroke-width:2px

style N fill:#bbf,stroke:#333,stroke-width:2px

end

subgraph "Data Sources"

O[IES Instances] --> L;

P[Network Channels] --> B;

Q[HESE#8209;DAR] --> B;

R[Secure UI Kernel] --> B;

style O fill:#aaf,stroke:#333,stroke-width:2px

style P fill:#aaf,stroke:#333,stroke-width:2px

style Q fill:#aaf,stroke:#333,stroke-width:2px

style R fill:#aaf,stroke:#333,stroke-width:2px

end

subgraph "Audit Trail (Patent 17)"

G --> S[MDATS];

H --> S;

S --> DLT;

style S fill:#ddf,stroke:#333,stroke-width:2px

end

subgraph "Decentralized Ledger (Patent 15)"

DLT[Decentralized Ledger];

end

linkStyle default stroke:#555,stroke-width:1px

classDef secure fill:#ccf,stroke:#333,stroke-width:2px

classDef mesh fill:#bbf,stroke:#333,stroke-width:2px

classDef data fill:#aaf,stroke:#333,stroke-width:2px

classDef audit fill:#ddf,stroke:#333,stroke-width:2px

Description for Diagram 6: Security Monitoring and Response

Legend:

ASKA Hub (A): Central orchestration and management.
Master Security Mesh (MSM) (B): Centralized security monitoring point receiving data from Local MSMs.
Security Policy Engine (C): Defines and updates security policies based on risk profiles.
DTMS (D): Dynamic Trust Management System, adjusting trust levels.
Access Control (E): Enforces access policies based on trust levels.
Anomaly Detection Engine (F): Uses AI/ML to detect anomalies from various sources.
Alerting System (G): Generates alerts for security incidents, reported to the Hub.
Response System (H): Orchestrates responses to anomalies.
Isolator (I): Isolates compromised components (hardware or software).
Self-Healer (J): Attempts to restore system integrity automatically.
Resource Manager (K): Adjusts resource allocation based on response requirements.
Secure OS: Operating System facilitating isolation and remediation.
Local Security Meshes (Patent 2) (L, M, N): Distributed monitoring points within each IES instance.
Data Sources (O, P, Q, R): Various sources providing data for the MSM and Anomaly Detection Engine.

IES Instances (O): Individual isolated execution environments.
Network Channels (P): Communication paths monitored for malicious activity.
HESE-DAR (Q): Hardware-enforced secure enclave for data at rest.
Secure UI Kernel (R): User interface providing potential attack vectors.

MDATS (S): Multi-Dimensional Audit Trail System, logging all events.
Decentralized Ledger (DLT): Stores audit trails and governance information.

Description:

This diagram illustrates the multi-layered security monitoring and response system within ASKA, highlighting its dynamic, adaptive, and decentralized nature. The system is designed to detect and respond to a wide range of threats, from internal anomalies to external attacks, leveraging advanced techniques for real-time threat assessment and automated remediation.

I. Distributed Security Monitoring:

The system employs a hierarchical monitoring architecture:

Local Security Meshes (Patent 2): Each Isolated Execution Stack (IES - P1) incorporates a Local Security Mesh (LSM), continuously monitoring its internal processes and resource usage. These LSMs utilize hardware-based mechanisms (P7, P8) for efficient and secure monitoring, minimizing performance overhead and reducing reliance on software-based monitoring susceptible to attacks. The LSMs report telemetry data to the Master Security Mesh.

Master Security Mesh (MSM) (B): This centralized monitoring point aggregates data from all Local Security Meshes, providing a system-wide security overview. The MSM uses a distributed consensus mechanism (P13, P15) to ensure data integrity and prevent manipulation. The MSM is also fed external threat intelligence data (P5) to enhance its context-awareness.

II. Threat Assessment and Policy Enforcement:

The MSM provides data to the Security Policy Engine (C), which defines security policies based on risk profiles and real-time context. These policies incorporate trust levels from the Dynamic Trust Management System (DTMS - P4) and are dynamically updated based on threat intelligence, system status, and user interaction patterns. The Security Policy Engine directly impacts the DTMS (D), influencing trust assessments which in turn govern access control. The DTMS provides real-time trust assessments for access control decisions.

III. Anomaly Detection and Response:

Anomaly Detection Engine (F): This component, potentially leveraging AI/Machine Learning (P10, P16), analyzes data from the MSM and other data sources to detect anomalies and potential security threats. It uses statistical methods (P7), heuristic analysis (P7), machine learning models, and real-time threat intelligence (P5) to identify abnormal behaviors, including subtle side-channel attacks (P7).

Alerting System (G): Upon detecting anomalies, the Anomaly Detection Engine triggers the Alerting System, which generates alerts to the ASKA Hub (A), allowing for human intervention if needed. Alerts are also logged in the MDATS.

Response System (H): This automated response system orchestrates remediation actions based on the severity of the anomaly and pre-defined policies. It interacts with three key modules:

Isolator (I): This module is responsible for isolating compromised components, preventing further spread of malicious activity. This may involve disabling network interfaces (P3), revoking capabilities (P2, P25), or shutting down affected IES instances (P1).

Self-Healer (J): This module attempts to automatically restore system integrity by resetting components, reallocating resources (P9, P10), or redeploying software (P16). Self-healing actions are guided by zone-specific policies (P13) and DTMS trust levels.

Resource Manager (K): This module dynamically adjusts resource allocation (P9, P10) in response to the anomaly to support the isolation or self-healing process, ensuring system stability and preventing resource exhaustion.

IV. Auditing and Logging:

The MDATS (S) plays a critical role in maintaining a detailed, tamper-evident audit trail of all security events. MDATS collects data from the Alerting System, Response System, and other security modules. This multi-dimensional audit trail (P17) combines digital logs with physical 3D microstructures (P14), ensuring verifiability and providing comprehensive provenance tracking. All audit trail information is logged on the Decentralized Ledger (DLT - P13, P15) for permanent storage and auditable access.

Data Sources:

The system gathers data from diverse sources, including IES instances (O), Network Channels (P), HESE-DAR (Q), and the Secure UI Kernel (R), providing a holistic view of system security.

This design offers a robust, responsive, and adaptive security system, effectively mitigating a wide spectrum of threats. The hierarchical monitoring structure, decentralized governance, automated response system, and comprehensive auditing contribute to the overall security and reliability of the ASKA architecture. The system's capacity for dynamic adaptation is crucial for responding to the ever-evolving threat landscape.

Diagram 7: Key Management and Recovery

graph

subgraph "ASKA Key Management & Recovery"

A[ASKA Hub] --> B(Key Management System);

B --> C(Key Generation);

C --> D(Secure Key Storage);

D --> E[IES Instances];

B --> F(Key Usage Monitoring);

F --> B;

B --> G(Key Revocation);

G --> D;

style B fill:#ccf,stroke:#333,stroke-width:2px

style D fill:#ccf,stroke:#333,stroke-width:2px

style G fill:#ccf,stroke:#333,stroke-width:2px

class B,D,G key

subgraph "Sovereign Trust Network (STN) (Patent 27)"

H[STN Key Manager] --> D;

I[STN Authentication Manager] --> H;

H --> J(Key Recovery Controller);

J --> K(Synchronization Nodes);

K --> L[External High-Trust Environments];

style H fill:#bbf,stroke:#333,stroke-width:2px

style J fill:#bbf,stroke:#333,stroke-width:2px

style K fill:#bbf,stroke:#333,stroke-width:2px

class H,J,K stn

end

subgraph "Dynamic Trust Gateway (DTG) (Patent 28)"

M[DTG Capability Manager] --> B;

M --> N(Dynamic Channel Provisioning);

N --> O[ATN/STN Communication];

style M fill:#aaf,stroke:#333,stroke-width:2px

style N fill:#aaf,stroke:#333,stroke-width:2px

class M,N dtg

end

subgraph "Hardware Enclaves"

P["HESE-DAR (Patent 24)"] --> D;

style P fill:#ddf,stroke:#333,stroke-width:2px

end

subgraph "Audit Trail (Patent 17)"

F --> Q[MDATS];

G --> Q;

Q --> R[Decentralized Ledger];

style Q fill:#ccf,stroke:#333,stroke-width:2px

class Q audit

end

linkStyle default stroke:#555,stroke-width:1px

classDef key fill:#ccf,stroke:#333,stroke-width:2px

classDef stn fill:#bbf,stroke:#333,stroke-width:2px

classDef dtg fill:#aaf,stroke:#333,stroke-width:2px

classDef enclave fill:#ddf,stroke:#333,stroke-width:2px

classDef audit fill:#ccf,stroke:#333,stroke-width:2px

Description for Diagram 7: Key Management and Recovery

Legend:

ASKA Hub (A): Central orchestration and management of key management.
Key Management System (B): Oversees key generation, storage, usage monitoring, and revocation.
Key Generation (C): Generates cryptographic keys (potentially leveraging Quantum-Resistant methods from Patent 5).
Secure Key Storage (D): Stores cryptographic keys securely (potentially using HESE-DAR).
IES Instances (E): Isolated execution environments using the keys.
Key Usage Monitoring (F): Tracks key usage for security and audit purposes.
Key Revocation (G): Revokes compromised keys.
STN Key Manager (H): Manages keys specifically for the Sovereign Trust Network.
STN Authentication Manager (I): Uses keys for authentication within the STN.
Key Recovery Controller (J): Manages the key recovery process within the STN.
Synchronization Nodes (K): Globally distributed nodes for key recovery synchronization.
External High-Trust Environments (L): External systems involved in key recovery.
DTG Capability Manager (M): Uses keys for dynamic channel provisioning in the DTG.
Dynamic Channel Provisioning (N): Establishes secure communication paths.
ATN/STN Communication (O): Communication between ATN and STN, using keys.
HESE-DAR (Patent 24) (P): Hardware-enforced secure encrypted enclave for key storage.
MDATS (Q): Multi-Dimensional Audit Trail System, logging key management events.
Decentralized Ledger (R): Stores key management and recovery audit trails.

Description:

This diagram details the architecture of ASKA's key management and recovery system, emphasizing its design for high security, resilience, and robust auditability. The system incorporates several novel features to address the challenges of managing cryptographic keys in a complex, distributed, and potentially hostile environment.

I. Centralized Key Management:

The ASKA Hub (A) orchestrates key management via the Key Management System (B). This system is responsible for:

Key Generation (C): The generation of cryptographic keys, potentially leveraging quantum-resistant methods from Patent 5. Key generation incorporates robust randomness sources (P7) to prevent predictability.

Secure Key Storage (D): Secure storage of cryptographic keys. This may involve multiple layers, including hardware-based secure elements and HESE-DAR enclaves (P24), depending on the sensitivity of the key.

Key Usage Monitoring (F): Continuous monitoring of key usage patterns to identify potential anomalies and unauthorized access.

Key Revocation (G): Mechanisms for quickly and securely revoking compromised keys. This process updates access control lists across the system, blocking access to affected keys.

II. Sovereign Trust Network (STN) Key Management (Patent 27):

The STN (P27) maintains a dedicated key management subsystem, further enhancing security and resilience:

STN Key Manager (H): Manages cryptographic keys used within the STN, employing stricter access control policies and encryption (P5,P24).

STN Authentication Manager (I): Uses keys for secure authentication within the STN, incorporating multi-factor authentication (MFA - P23) and hardware-rooted trust.

Key Recovery Controller (J): Manages the process of key recovery in case of compromise or loss. This module employs a hierarchical and distributed architecture.

Synchronization Nodes (K): Globally distributed, trusted nodes responsible for maintaining key replicas and synchronizing key recovery operations. This distributed approach prevents single points of failure.

External High-Trust Environments (L): External systems, carefully vetted and integrated via ASKA's robust communication channels, participate in the key recovery process, providing additional layers of security and redundancy.

III. Dynamic Trust Gateway (DTG) Key Integration (Patent 28):

The DTG (P28) leverages ASKA's key management system for secure communication between ATN and STN:

DTG Capability Manager (M): This module uses keys for dynamic channel provisioning (P28) within the DTG and to manage capabilities (P25) for access control between ATN and STN.

Dynamic Channel Provisioning (N): The DTG’s dynamic channel provisioning relies on the key management system to authenticate communication paths and enforce access control policies.

IV. Hardware Enclaves and Secure Storage:

The HESE-DAR (P24) plays a critical role in providing high-assurance secure storage for keys, integrating with the Key Management System for secure key retrieval and storage.

V. Auditing and Logging:

The Multi-Dimensional Audit Trail System (MDATS - P17) meticulously logs all key management and recovery events. This includes key generation, usage, revocation, and recovery attempts. This detailed audit trail is stored on the Decentralized Ledger (R - P13, P15), ensuring transparency and tamper-evidence. The MDATS uses techniques (P14) to integrate physical tamper-evident audit trails (3D microstructures) alongside the digital audit trails for increased assurance.

This architecture provides a robust and secure solution for managing cryptographic keys in a highly secure environment. The multi-layered approach, use of hardware security mechanisms (P24), and integration with ASKA's core security components contribute to high assurance and resilience. Decentralized governance and the comprehensive audit trail ensure transparency and accountability in all key operations. The key recovery system ensures high data availability in the face of various threats.

Diagram 8: Zone Management and Inter-Zone Communication

graph LR

subgraph "ASKA Zone Management & Inter#8209;Zone Communication"

A[ASKA Global Control Plane] --> B(Zone Creation & Management);

B --> C(Zone Hierarchy);

C --> D{Zones};

B --> E(TRC Management);

E --> C;

E --> F[Decentralized Ledger];

style B fill:#ccf,stroke:#333,stroke-width:2px

style E fill:#ccf,stroke:#333,stroke-width:2px

class B,E management

subgraph Zones

D --> G[ASKA Hub];

G --> H(IES Clusters);

G --> I(Local Security Mesh);

H --> J[Secure Communication Agent];

J --> K[Multi-Channel Network];

K --> L[Other Zones];

style G fill:#bbf,stroke:#333,stroke-width:2px

style H fill:#bbf,stroke:#333,stroke-width:2px

style J fill:#bbf,stroke:#333,stroke-width:2px

style K fill:#bbf,stroke:#333,stroke-width:2px

class G,H,J,K zone

end

subgraph "Inter#8209;Zone Communication (Patent 22)"

L --> M[SIZCF];

M --> N[External Systems];

style M fill:#aaf,stroke:#333,stroke-width:2px

class M interzone

end

subgraph "Auditing & Governance (Patent 17)"

E --> O[MDATS];

O --> F;

style O fill:#ddf,stroke:#333,stroke-width:2px

class O audit

end

linkStyle default stroke:#555,stroke-width:1px

classDef management fill:#ccf,stroke:#333,stroke-width:2px

classDef zone fill:#bbf,stroke:#333,stroke-width:2px

classDef interzone fill:#aaf,stroke:#333,stroke-width:2px

classDef audit fill:#ddf,stroke:#333,stroke-width:2px

Description for Diagram 8: Zone Management and Inter-Zone Communication

This diagram illustrates ASKA's zone management and inter-zone communication. While the SIZCF diagram shows inter-zone collaboration, this more comprehensive diagram shows the overall zone architecture, including zone creation, management, trust relationships between zones, and the various communication pathways, including the role of the ASKA Hubs and the Decentralized Ledger.

Legend:

ASKA Global Control Plane (A): High-level management and orchestration of all ASKA zones.
Zone Creation & Management (B): Handles the creation, configuration, and deletion of zones.
Zone Hierarchy (C): Represents the hierarchical relationship between zones.
Zones (D): Individual ASKA deployments.
TRC Management (E): Manages Trust Root Configurations (TRCs) for each zone, stored on the Decentralized Ledger.
Decentralized Ledger (F): Stores TRCs, zone configurations, and audit logs.
ASKA Hub (G): Manages the IES clusters and other components within a zone.
IES Clusters (H): Groups of isolated execution environments within a zone.
Local Security Mesh (I): Local security monitoring for each IES.
Secure Communication Agent (J): Manages communication within and between zones.
Multi-Channel Network (K): Secure communication channels between IES clusters.
Other Zones (L): Other ASKA zones.
SIZCF (M): Secure Inter-Zone Collaboration Framework (Patent 22) for external communication.
External Systems (N): External entities collaborating with ASKA zones.
MDATS (O): Multi-Dimensional Audit Trail System, logging zone-related events.

Description:

This diagram illustrates ASKA's zone management and inter-zone communication framework (P22), a crucial aspect of its architecture that enables secure and controlled collaboration between multiple, independent security domains. This system combines a hierarchical zone structure with dynamic trust management and secure communication protocols to facilitate cooperation while maintaining strong isolation between zones.

I. ASKA Global Control Plane:

The ASKA Global Control Plane (A) acts as the central authority for managing the overall zone structure and configuration. This is not a single point of failure, instead its functions are implemented using replicated, distributed components secured with cryptographic mechanisms. It interacts with the system's zone creation and management processes.

II. Zone Creation and Management (B):

The Zone Creation & Management (B) module is responsible for creating and managing ASKA zones. This module uses a dynamic policy language to define parameters such as security policies, resource allocation, and allowed inter-zone communication. These policies incorporate trust levels (P4) and are persistent in the decentralized ledger (P13, P15). The module interacts with the TRC Management module for trust definition and propagation.

III. Zone Hierarchy (C):

ASKA zones are organized into a flexible hierarchy (C) reflecting the organization's structure and security needs. This allows for granular control over trust relationships and inter-zone communication. Trust and policy inheritance occur from parent to child zones, simplifying management and consistency.

IV. Zones (D):

Each zone (D) functions as an independent ASKA deployment, containing its own:

ASKA Hub (G): The zone's central orchestrator and management component. The Hub's control plane interacts with the global control plane for zone configuration and policy updates.

IES Clusters (H): A collection of IES instances (P1) running within the zone.

Local Security Mesh (I): Monitors activities and security within each zone (P2).

Secure Communication Agent (J - P2, P3): Manages secure communication channels between IES instances within the zone and with other zones. The agent dynamically chooses communication paths using multi-path selection (P3, P22).

Multi-Channel Network (K): A network infrastructure (P3) providing physical channel segregation for secure inter-IES communication and interactions with external systems, using capability-aware forwarding (P2, P26).

V. Inter-Zone Communication (Patent 22):

Secure inter-zone communication is facilitated by the Secure Inter-Zone Collaboration Framework (SIZCF) (M). The SIZCF utilizes secure communication channels (P3, P5) and privacy-preserving protocols (P20, P22) for secure data exchange. Communication between zones is dynamically managed, guided by trust levels (P4), policies (P13, P15), and real-time threat assessments (P2). The SIZCF provides access to external systems while maintaining a robust security boundary.

VI. Auditing and Governance (Patent 17):

All zone management and inter-zone communication activities are logged on the Decentralized Ledger (F) (P13, P15) using the Multi-Dimensional Audit Trail System (MDATS - O) (P17). MDATS tracks all critical events, including zone creation/modification, TRC updates, trust relationships, and inter-zone data exchange. This data can be analyzed by the Governance AI component for identification of anomalies and proactive security enhancements.

This architecture enables secure and flexible collaboration across multiple ASKA zones, preserving the integrity and confidentiality of data while allowing for a managed expansion of the system. The decentralized governance model enhances transparency, accountability, and resilience against attacks. Dynamic adaptation based on trust and security assessments ensure continuous optimization of the system's security posture. The integration with the MDATS and decentralized ledger ensures a comprehensive and tamper-evident audit trail of all zone management and inter-zone communication activities.

Diagram 9: Data Flow and Security Processes

graph LR

subgraph ASKA Zone Internal Data Flow & Security

A[ASKA Hub] --> B(IES Clusters);

B --> C(Applications);

C --> D[Secure Data Enclaves];

D --> E[Federated Learning];

B --> F(Local Security Meshes);

F --> G(Master Security Mesh);

G --> H(Anomaly Detection);

H --> I(Response System);

I --> B;

G --> A;

style A fill:#ccf,stroke:#333,stroke-width:2px

style G fill:#ccf,stroke:#333,stroke-width:2px

class A,G hub

subgraph "Data Services"

D --> J(Data Input/Output);

J --> K[Multi#8209;Channel Network];

K --> L[Other Zones / External Systems];

style D fill:#bbf,stroke:#333,stroke-width:2px

style E fill:#bbf,stroke:#333,stroke-width:2px

style J fill:#bbf,stroke:#333,stroke-width:2px

style K fill:#bbf,stroke:#333,stroke-width:2px

class D,E,J,K data

end

subgraph "ASKA Hub"

A --> M(DTMS);

M --> B;

A --> N(AESDS);

N --> B;

A --> O(Policy Engine);

O --> G;

style M fill:#aaf,stroke:#333,stroke-width:2px

style N fill:#aaf,stroke:#333,stroke-width:2px

style O fill:#aaf,stroke:#333,stroke-width:2px

class M,N,O hubservices

end

subgraph "Decentralized Ledger"

P[Decentralized Ledger] -.-> A;

P -.-> D;

P -.-> E;

P -.-> G;

style P fill:#ddf,stroke:#333,stroke-width:2px

class P ledger

end

linkStyle default stroke:#555,stroke-width:1px

classDef hub fill:#ccf,stroke:#333,stroke-width:2px

classDef data fill:#bbf,stroke:#333,stroke-width:2px

classDef hubservices fill:#aaf,stroke:#333,stroke-width:2px

classDef ledger fill:#ddf,stroke:#333,stroke-width:2px

Description for Diagram 9: Data Flow and Security Processes

Legend:

ASKA Hub (A): Central management and coordination within the zone.
IES Clusters (B): Isolated execution environments running applications.
Applications (C): User-level programs running within IES instances.
Secure Data Enclaves (D): Secure, isolated environments for sensitive data processing.
Federated Learning (E): Collaborative machine learning across IES instances.
Local Security Meshes (F): Monitor activity within each IES.
Master Security Mesh (MSM) (G): Aggregates security data from Local MSMs.
Anomaly Detection (H): Detects anomalies based on MSM data.
Response System (I): Orchestrates responses to detected anomalies.
Data Input/Output (J): Secure data transfer within and outside the zone.
Multi-Channel Network (K): Secure communication channels.
Other Zones / External Systems (L): Entities outside the current zone.
DTMS (M): Dynamic Trust Management System.
AESDS (N): Automated Evolutionary Software Development System.
Policy Engine (O): Defines and enforces security policies.
Decentralized Ledger (P): Stores audit trails and zone configuration data.

Description:

This diagram provides an in-depth view of the internal data flow and security processes within a single ASKA zone. It illustrates how data is securely processed, transferred, and monitored within the zone, emphasizing the interactions between the Hub, IES clusters, security modules, data services, and the Decentralized Ledger. The layered and integrated security architecture demonstrates ASKA's ability to maintain robust data protection and system integrity.

I. ASKA Hub (A):

The ASKA Hub acts as the central orchestrator and management point within the zone. It coordinates activities of the IES clusters, manages security policies, and oversees communication with other zones and external systems. The Hub incorporates essential security modules and services:

DTMS (M): The Dynamic Trust Management System (P4) establishes and manages trust relationships between IES instances and across zones, dynamically adjusting trust levels based on real-time monitoring data, security assessments (P7), and policy updates (P4). The DTMS influences resource allocation, capability management, and inter-component communication.

AESDS (N): The Automated Evolutionary Software Development System (P16) continuously monitors, updates, and improves ASKA’s software. It leverages a knowledge base, performance metrics, and threat intelligence to generate secure code, ensuring the system can adapt to evolving threats. Secure zoned deployment (P3) and TRC-based verification (P1) ensure integrity and authenticity of software updates.

Policy Engine (O): Defines and enforces security policies throughout the zone, controlling access to data, resources, and services. The Policy Engine interacts with all components, incorporating trust levels from the DTMS and policies from the Decentralized Ledger.

II. Isolated Execution Stacks (IES) Clusters (B):

IES Clusters provide secure and isolated execution environments for applications. Each IES (P1) has dedicated hardware resources (CPU, memory, I/O, network), minimizing the impact of compromises.

Local Security Meshes (F): Each IES incorporates a Local Security Mesh (P2) to continuously monitor internal processes and resource usage. These LSMs employ hardware-based mechanisms for efficient and tamper-proof monitoring, reporting real-time telemetry to the Master Security Mesh (P2).

Applications (C): User-level programs are executed within IES instances, isolated from other applications and the underlying operating system. This containment minimizes the blast radius of potential security breaches.

III. Secure Data Services:

ASKA zones host various data services within IES instances, including:

Secure Data Enclaves (D): These provide secure, isolated environments for processing sensitive data. They leverage hardware-enforced isolation, unidirectional communication (P2), and dynamic trust management (P4) to protect data confidentiality and integrity. Secure Data Enclaves support collaborative analysis (P20) and integration with federated learning.

Federated Learning (E): This allows for collaborative machine learning across multiple IES instances without sharing raw data. MPC-based aggregation (P19) protects sensitive training data. Federated Learning benefits from ASKA's robust security and isolation mechanisms, ensuring the privacy and integrity of the learning process.

IV. Data Flow and Communication:

Data within the ASKA zone is transferred through secure channels managed by the Multi-Channel Network (K). The MCN (P3) utilizes physically segregated channels with dedicated firewalls, preventing cross-channel interference and ensuring the isolation of data flows. The MCN leverages capability-aware forwarding (P2, P26) for fine-grained access control and dynamic traffic management. Quantum-resistant communication (P5) using techniques like QKD, DKM, and PQC further secure these channels against sophisticated attacks.

Data Services interact with the MCN for secure data transfer both within the zone and with other zones or external systems (L), ensuring that sensitive information is protected during transit.

V. Security Monitoring and Response:

Master Security Mesh (G): The MSM (P2) aggregates data from the Local Security Meshes, providing a holistic view of the zone's security posture. It incorporates threat intelligence (P5) and system-wide metrics, feeding data to the Anomaly Detection engine.

Anomaly Detection (H): This module utilizes advanced techniques, including AI/ML algorithms (P10, P16), to analyze data from the MSM, identifying anomalies and potential threats.

Response System (I): The Response System orchestrates automated responses to anomalies, including isolation (P7), self-healing (P7), and dynamic resource allocation (P9, P10). Response actions are guided by security policies defined by the Policy Engine and trust levels from the DTMS.

VI. Decentralized Ledger (P):

The Decentralized Ledger (P13, P15) serves as a tamper-proof repository for audit trails, security policies, zone configuration, and other critical data. Its distributed nature ensures high availability, integrity, and transparency. The Decentralized Ledger integrates with the MDATS (P17) to record events and activities from all ASKA components within the zone, including the Hub, IES clusters, data services, and the security monitoring system.

Interactions and Integration:

This diagram illustrates the flow of data and control information within the ASKA zone:

ASKA Hub (A): The Hub orchestrates all components within the zone, managing IES creation and termination, resource allocation, and security policies.

Data Services: Secure Data Enclaves (D) and Federated Learning (E) interact with the Multi-Channel Network (K) for secure data input/output. These services are managed and monitored by the ASKA Hub.

Security Monitoring and Response: The Security Monitoring system (Local MSMs (F) -> MSM (G) -> Anomaly Detection (H) -> Response System (I)) continuously monitors the zone, detecting and responding to anomalies in real time. It provides feedback to the ASKA Hub, influencing policy decisions and resource allocation.

Decentralized Ledger (P): All activities are logged on the Decentralized Ledger, providing a comprehensive audit trail and ensuring transparency and accountability. The DLT also stores security policies and zone configuration information.

This layered and integrated approach creates a robust and dynamic security architecture within the ASKA zone. The combination of hardware-enforced isolation (P1, P24), advanced threat detection (P7), automated response mechanisms (P7), and secure communication (P2, P3, P5, P26) ensures a high level of data protection, system integrity, and resilience against sophisticated threats. Decentralized governance and the comprehensive audit trail provide transparency, accountability, and trust in the system's operation.

Diagram 10: Bootstrapping and Attestation

graph TD

subgraph "ASKA Bootstrapping & Attestation"

A[Power On] --> B(Hardware Root of Trust);

B --> C{Secure Boot ROM};

C --> D(Bootloader Verification);

D -- Verified --> E(Secure OS Loading);

D -- Tampered --> F[Halt/Alert];

E --> G(IES Initialization);

G --> H(Mini-TRC Verification);

H -- Verified --> I(DTMS Integration);

H -- Tampered --> F;

I --> J(Application Loading);

J --> K(Attestation Service);

K --> L(Attestation Report);

L --> M[Decentralized Ledger];

style C fill:#ccf,stroke:#333,stroke-width:2px

style G fill:#ccf,stroke:#333,stroke-width:2px

style K fill:#ccf,stroke:#333,stroke-width:2px

class C,G,K secure

subgraph "Trust Root Configuration (TRC)"

N[TRC] --> C;

N --> H;

style N fill:#bbf,stroke:#333,stroke-width:2px

class N trc

end

subgraph "Dynamic Trust Management System (DTMS)"

D --> O[DTMS];

O --> I;

style O fill:#aaf,stroke:#333,stroke-width:2px

class O dtms

end

linkStyle default stroke:#555,stroke-width:1px

classDef secure fill:#ccf,stroke:#333,stroke-width:2px

classDef trc fill:#bbf,stroke:#333,stroke-width:2px

classDef dtms fill:#aaf,stroke:#333,stroke-width:2px

Description for Diagram 10: Bootstrapping and Attestation

The diagram represents a high-level visualization of the ASKA bootstrapping and attestation process. This dedicated diagram illustrates secure boot’s steps, from initial power-on to establishing a trusted execution environment within an IES, and the integration with the DTMS, TRCs, and the Decentralized Ledger.

Legend:

Power On (A): The initial start of the ASKA system.
Hardware Root of Trust (B): Immutable hardware providing the foundation of trust.
Secure Boot ROM (C): Read-only memory containing the initial secure boot code.
Bootloader Verification (D): Verifies the integrity of the bootloader using signatures from the TRC.
Secure OS Loading (E): Loads the secure operating system if verification is successful.
Halt/Alert (F): Stops the boot process and generates an alert if tampering is detected.
IES Initialization (G): Initializes the Isolated Execution Stacks.
Mini-TRC Verification (H): Verifies the integrity of each IES's mini-TRC.
DTMS Integration (I): Integrates the IES with the DTMS for trust management.
Application Loading (J): Loads applications within the secure IES environment.
Attestation Service (K): Generates a report of the system's security posture.
Attestation Report (L): The generated report, providing verifiable evidence of integrity.
Decentralized Ledger (M): Stores the attestation report for auditability and transparency.
Trust Root Configuration (TRC) (N): Contains the root keys and security policies.
Dynamic Trust Management System (DTMS) (O): Provides trust levels for access control decisions.

Description:

This diagram provides a comprehensive illustration of the ASKA bootstrapping and attestation process, showcasing the system's multi-layered approach to building a chain of trust from the initial power-on to the execution of applications. This process ensures the integrity and authenticity of all components, establishing a secure foundation for all subsequent operations within the ASKA environment.

I. Secure Boot Sequence:

Power On (A): The process begins with the initial power-on of a ASKA component.

Hardware Root of Trust (B): The system's foundation of trust resides in a hardware-based root of trust. This could be a physically secure and tamper-resistant element like a Trusted Platform Module (TPM) or a secure processor with immutable boot code. The HRT provides the initial anchor for the chain of trust.

Secure Boot ROM (C): The immutable Secure Boot ROM contains the initial secure boot code, signed with the root key from the Trust Root Configuration (TRC - N). This code is cryptographically verified upon execution, ensuring its integrity.

Bootloader Verification (D): The Secure Boot ROM executes the bootloader, whose integrity is verified against signatures stored in the TRC (N). This verification (D) prevents the execution of unauthorized or compromised bootloaders. The DTMS (O) is also consulted during this step to check for revocation or other policy violations.

Secure OS Loading (E): If the bootloader verification is successful, the secure operating system (OS) is loaded. The secure OS is designed to provide a trusted execution environment with isolation mechanisms and security features.

Halt/Alert (F): If any tampering or integrity violations are detected during the boot process, the system halts immediately and generates an alert using ASKA’s secure communication channels (P2, P3) and the SCMP protocol (P7). This proactive response prevents the system from booting into an untrusted state.

II. IES Initialization and Integration:

IES Initialization (G): Once the Secure OS is loaded, it initializes the Isolated Execution Stacks (IES - P1), creating secure and isolated environments for applications.

Mini-TRC Verification (H): Each IES has its own mini-TRC (P1), which defines local trust roots and policies specific to that instance. The secure OS verifies the integrity of each mini-TRC before proceeding. This ensures that the trust policies within each IES are valid and have not been compromised.

DTMS Integration (I): The IES instance then integrates with the Dynamic Trust Management System (DTMS - P4), sharing its mini-TRC and security posture. The DTMS incorporates this information into its trust assessment process, assigning trust levels to the IES instance based on its security configuration and provenance.

III. Application Loading and Attestation:

Application Loading (J): Applications are loaded within the secure IES environment. The DTMS and Policy Engine (O) enforce access control policies, ensuring only authorized applications are executed.

Attestation Service (K): The Attestation Service generates a comprehensive report of the system's security posture, including hardware and software integrity measurements, configuration details, and trust levels. This report provides verifiable evidence that the system booted securely and is running in a trusted state.

Attestation Report (L): The Attestation Report (L) is signed using cryptographic techniques (P5, P24) to ensure its authenticity and integrity. It is then securely transmitted to the Decentralized Ledger (M) using ASKA’s communication channels (P3).

Decentralized Ledger (M): The Decentralized Ledger (P13, P15) provides a tamper-proof and auditable record of the Attestation Report. This ensures transparency and accountability for the entire bootstrapping and attestation process.

IV. Trust Root Configuration (TRC):

The TRC (N) plays a crucial role throughout the process. It contains the root keys and security policies used for verifying the Secure Boot ROM, bootloader, and mini-TRCs. The TRC itself is stored securely, potentially using HESE-DAR (P24) or a similar tamper-evident mechanism.

V. Dynamic Trust Management System (DTMS):

The DTMS (O) dynamically assesses the trust level of each component based on its provenance, security posture, and observed behavior. It integrates with the bootstrapping process by validating the bootloader and influencing the trust level assigned to IES instances. The DTMS uses distributed consensus (P13) and blockchain technology (P15) to ensure the integrity and consistency of trust assessments.

VI. Interactions and Security Mechanisms:

The diagram highlights the sequential steps involved in bootstrapping and attestation, demonstrating the system's commitment to establishing a chain of trust from the ground up. The system utilizes a combination of hardware security (HRT, Secure Boot ROM), cryptographic verification (digital signatures, hashing), tamper detection (halt/alert), secure communication channels (P2, P3, P5, P26), and dynamic trust management to ensure the integrity and authenticity of the entire boot process and the resulting execution environment. The decentralized ledger provides a tamper-evident record for auditing and transparency. This multi-layered approach creates a robust and secure foundation for the ASKA system, mitigating a wide range of potential threats, including sophisticated attacks targeting the boot process.

Diagrams 11a and 11b: Security Meshes

Diagram 11a: Overview

graph LR

subgraph "ASKA Security Mesh"

IES[IES Instance]:::ies -- Data Plane (Passive/Read-Only) --> RAMSSD[RAM/SSD]:::data

LSM[LSM]:::lsm -- Data Plane (Passive/Read-Only) --> RAMSSD

Watcher[Watcher Mesh]:::watcher -- One-Way Control Plane --> LSM

Watcher -- Data Plane (Passive/Read-Only) --> RAMSSD

Watcher -- Bidirectional Control Plane --> AI[AI Hub]:::ai

MSM[MSM]:::msm -- One-Way Control Plane --> LSM

Watcher -- One-Way Control Plane --> MSM

End

classDef ies fill:#ccf,stroke:#333,stroke-width:1px

classDef lsm fill:#aaf,stroke:#333,stroke-width:1px

classDef watcher fill:#ddf,stroke:#333,stroke-width:1px

classDef msm fill:#f9f,stroke:#333,stroke-width:2px

classDef ai fill:#afa,stroke:#333,stroke-width:1px

classDef data fill:#ddd,stroke:#333,stroke-width:1px

Diagram 11b: Expanded

graph LR

subgraph RAM/SSD Banks

direction LR

RAM1[RAM Bank 1]

SSD1[SSD Bank 1]

RAM2[RAM Bank 2]

SSD2[SSD Bank 2]

end

subgraph IES Instance 1

direction LR

IES1[IES 1] -- Data Plane (Read-Only) --> RAM1

IES1 -- Data Plane (R/O) --> SSD1

end

subgraph IES Instance 2

direction LR

IES2[IES 2] -- Data Plane (R/O) --> RAM2

IES2 -- Data Plane (R/O) --> SSD2

end

subgraph LSM 1

direction LR

LSM1[LSM 1] -- Data Plane (R/O) --> RAM1

LSM1 -- Data Plane (R/O) --> SSD1

end

subgraph LSM 2

direction LR

LSM2[LSM 2] -- Data Plane (R/O) --> RAM2

LSM2 -- Data Plane (R/O) --> SSD2

end

subgraph LSM Watcher Mesh 1

LW1[LSM Watcher 1] -- One-Way Control Plane --> LSM1

LW1 -- Data Plane (R/O) --> RAM1

LW1 -- Data Plane (R/O) --> SSD1

end

subgraph LSM Watcher Mesh 2

LW2[LSM Watcher 2] -- One-Way Control Plane --> LSM2

LW2 -- Data Plane (R/O) --> RAM2

LW2 -- Data Plane (R/O) --> SSD2

end

subgraph MSM Watcher Mesh

MW[MSM Watcher] -- One-Way Control Plane --> MSM

end

subgraph "AI Cyber Intelligence Hub"

AI[AI Hub]

end

subgraph MSM

MSM[Master Security Mesh]

end

MW -- Bidirectional Control Plane --> AI

LW1 -- Bidirectional Control Plane --> AI

LW2 -- Bidirectional Control Plane --> AI

MSM -- One-Way Control Plane --> LSM1

MSM -- One-Way Control Plane --> LSM2

LW1 -- One-Way Control Plane --> MSM

LW2 -- One-Way Control Plane --> MSM

IES1 -- Monitored by LSM --> LSM1

IES2 -- Monitored by LSM --> LSM2

Description for Diagrams 11a and 11b: Security Meshes

I. Core Components and their Interactions:

RAM/SSD Banks: These represent the system's physical memory and storage, the ultimate targets of many sophisticated attacks.

IES (Isolated Execution Stacks): The foundation of ASKA, IES instances provide hardware-enforced isolation for applications and processes. This compartmentalization limits the "blast radius" of any potential compromise.

LSM (Local Security Mesh): Each IES has a dedicated LSM operating alongside it. The LSM is responsible for monitoring the associated IES's activity, including its interactions with RAM/SSD. Critically, this monitoring occurs through a passive, read-only data plane, ensuring no interference with the IES's operation and minimizing the attack surface exposed to the LSM itself.

LSM Watcher Mesh: This is the first layer of the "watcher" system. Each LSM has a dedicated Watcher Mesh that monitors both the LSM and the associated IES. The LSM Watcher passively observes RAM/SSD access patterns of the IES and the LSM, looking for anomalies. It communicates with the LSM via a one-way control plane to receive anomaly reports. It has a bidirectional control plane connection with the AI Cyber Intelligence Hub, enabling it to send detailed reports and receive instructions.

MSM (Master Security Mesh): The MSM oversees all LSMs within a ASKA domain. It receives anomaly reports from the LSM Watcher Meshes via a one-way control plane. It also has its own dedicated Watcher Mesh for independent oversight. The MSM distributes security policies and updates to the LSMs via a one-way control plane, preventing compromised LSMs from propagating malicious instructions upwards.

MSM Watcher Mesh: This Watcher mirrors the function of the LSM Watchers, providing independent monitoring of the MSM's RAM/SSD access. It reports to the AI Hub via a bidirectional control plane.

Data Plane (Passive/Read-Only): This represents the innovative, out-of-band connection between the LSMs, their Watchers, and the RAM/SSD. It employs an undisclosed mechanism to passively observe memory and storage access without any direct interaction, ensuring the integrity and availability of the monitored systems. This passive monitoring could involve analyzing spatiotemporal metadata (temperature fluctuations, electromagnetic emissions, power consumption variations) associated with RAM/SSD access to infer activity and detect anomalies without direct data access.

Control Plane: The control plane connections facilitate the communication of security policies, updates, and anomaly reports. The one-way nature of the control plane from MSM to LSMs and from Watcher Meshes to MSM/LSMs is crucial for containing compromises. The bidirectional control plane between the Watcher Meshes and the AI Hub allows for real-time feedback and adaptive security responses.

AI Cyber Intelligence Hub: The central intelligence unit of the system. It receives reports from all Watcher Meshes, uses advanced analytics and AI to identify complex attack patterns, and triggers automated responses. The bidirectional connection with the Watcher Meshes allows for dynamic policy adjustments and targeted investigations.

II. ASKA's Enhanced Security Posture:

The architecture elucidates ASKA's security posture in several ways:

Defense in Depth: The layered approach of IES, LSMs, and Watcher Meshes creates multiple lines of defense, making it much harder for attackers to penetrate the system.
Compromise Resilience: The one-way control plane and independent Watcher Meshes limit the impact of a compromise. Even if an LSM or the MSM is compromised, the Watcher Meshes can still detect and report the anomaly, and the one-way control plane prevents the attacker from spreading malicious instructions.
Proactive Threat Detection: Passive RAM/SSD monitoring enables the detection of sophisticated attacks that might bypass traditional security measures. By observing memory access patterns, the Watcher Meshes can identify suspicious activity, even if the attacker is using rootkit techniques or other advanced methods to hide their presence.
Real-Time Response: The AI-driven analysis hub and tight feedback loops enable rapid response to identified threats. The system can automatically isolate compromised components, adjust security policies, or trigger other defensive actions.
Enhanced Forensics: The comprehensive audit trails provided by the MDATS (Multi-Dimensional Audit Trail System), potentially enhanced by the physical 3D microstructures, allow for detailed post-incident analysis and investigation. The inclusion of spatiotemporal data further enriches the forensic evidence.

III. Integration with Existing ASKA Features:

The security elements integrate seamlessly with ASKA as follows:

DTMS (Dynamic Trust Management System): The DTMS can leverage the anomaly reports from the Watcher Meshes to dynamically adjust trust levels and access control policies.
AESDS (Automated Evolutionary Software Development System): The AESDS can use the information from the AI Hub to generate and deploy security patches and updates, addressing vulnerabilities discovered by the Watcher Meshes.
STN (Sovereign Trust Network): The STN, with its heightened security measures, can benefit from the enhanced monitoring provided by the clarified architecture.
Multi-Channel Network: The secure communication channels provided by the Multi-Channel Network are crucial for the reliable and confidential transmission of anomaly reports and control plane instructions.

Current Data Flow (with Feedback Loop):

Monitoring: LSM monitors IES resource access (RAM/SSD) and reports anomalies to Watcher Mesh.
Analysis & Reporting: Watcher Mesh analyzes data, reports to AI Hub, and receives instructions.
Decision Making: AI Hub analyzes reports, makes decisions, and instructs Watcher Mesh.
Enforcement: Watcher Mesh instructs LSM to take action (e.g., isolation, process termination). This instruction creates the feedback loop.

Goal: Eliminate step 4, preventing the AI Hub from directly instructing the LSM.

Proposals for Eliminating the Feedback Loop:

Proposal 1: AI Hub to ASKA Hub Enforcement:

Data Flow: LSM -> Watcher Mesh -> AI Hub -> ASKA Hub -> Policy Engine -> LSM

Mechanism: The AI Hub, instead of instructing the Watcher Mesh, sends its analysis and recommended actions to the ASKA Hub. The Hub's Policy Engine evaluates these recommendations against existing policies and trust levels (from DTMS). If approved, the Hub issues the enforcement commands directly to the LSM.

Advantages: Removes the direct connection between the AI Hub and the LSM, reducing the attack surface. Leverages existing ASKA Hub components (Policy Engine, DTMS) for decision-making and enforcement.

Disadvantages: Introduces a potential bottleneck at the ASKA Hub. Increases latency for response actions.

Proposal 2: Decentralized Enforcement with Consensus:

Data Flow: LSM -> Watcher Mesh -> AI Hub + other LSMs/Watcher Meshes -> Consensus Engine -> LSM

Mechanism: The AI Hub broadcasts its analysis and recommended actions to a distributed Consensus Engine (P13), along with other LSMs and Watcher Meshes. The Consensus Engine reaches an agreement on the appropriate action. If consensus is achieved, each LSM independently enforces the action.

Advantages: Highly decentralized and resilient. No single point of failure. A compromised AI Hub cannot unilaterally enforce actions.

Disadvantages: Requires complex consensus protocols. Increased communication overhead. Potential for disagreement and inaction.

graph

subgraph "Proposal 2: Decentralized Enforcement with Consensus"

subgraph IES Instance 1

LSM1[LSM 1] -- Passive Data Plane --> RAMSSD[RAM/SSD]

IES1[IES 1] -- Data Plane --> RAMSSD

Watcher1[Watcher 1] -- Unidirectional Control Plane --> LSM1

Watcher1 -- Passive Data Plane --> RAMSSD

end

subgraph IES Instance 2

LSM2[LSM 2] -- Passive Data Plane --> RAMSSD

IES2[IES 2] -- Data Plane --> RAMSSD

Watcher2[Watcher 2] -- Unidirectional Control Plane --> LSM2

Watcher2 -- Passive Data Plane --> RAMSSD

end

AIHub[AI Hub] -.-> Consensus["Consensus Engine (P13)"]

Watcher1 -.-> Consensus

Watcher2 -.-> Consensus

Consensus --> LSM1

Consensus --> LSM2

DTMS["DTMS (P4)"] -.-> Consensus

Consensus --> DLT["Decentralized Ledger (P13,P15)"]

style Consensus fill:#f9f,stroke:#333,stroke-width:2px

end

classDef ies fill:#ccf,stroke:#333,stroke-width:1px

classDef lsm fill:#aaf,stroke:#333,stroke-width:1px

classDef watcher fill:#ddf,stroke:#333,stroke-width:1px

classDef ai fill:#afa,stroke:#333,stroke-width:1px

classDef data fill:#ddd,stroke:#333,stroke-width:1px

This diagram illustrates the decentralized enforcement mechanism. Notice the absence of a direct connection between the AI Hub and the LSMs. Instead:

LSMs and Watchers: Each IES instance has an LSM and a Watcher Mesh, functioning as described previously. They connect to shared RAM/SSD resources.

Consensus Engine: The central component is the Consensus Engine (P13). The AI Hub, along with all other Watcher Meshes, sends its analysis and recommended actions to the Consensus Engine. The DTMS (for trust context) also provides input to the Consensus Engine.

Decentralized Enforcement: The Consensus Engine, using a distributed consensus protocol, reaches an agreement on the appropriate action. The dotted lines from the Consensus Engine back to the LSMs represent the enforcement commands. Critically, these commands are issued only after consensus is reached. This decentralized approach enhances security because no single entity (even a compromised AI Hub) can dictate actions. All consensus decisions are logged to the DLT.

Proposal 3: Hybrid Approach (Staged Enforcement):

Data Flow: LSM -> Watcher Mesh -> AI Hub -> Watcher Mesh -> LSM (Limited Actions) & AI Hub -> ASKA Hub -> Policy Engine -> LSM (Critical Actions)

Mechanism: The AI Hub can instruct the Watcher Mesh to take limited, pre-approved actions (e.g., increasing monitoring frequency, logging more data). For critical actions (e.g., isolation, termination), the AI Hub must still go through the ASKA Hub's Policy Engine, as in Proposal 1.

Advantages: Balances speed and security. Allows for rapid response to less critical threats while maintaining tight control over high-impact actions.

Disadvantages: Requires careful definition of "limited" and "critical" actions. Increased complexity in the AI Hub and Watcher Mesh.

graph LR

subgraph "Proposal 3: Hybrid Approach (Staged Enforcement)"

subgraph IES Instance 1

LSM1[LSM 1] -- Passive Data Plane --> RAMSSD[RAM/SSD]

IES1[IES 1] -- Data Plane --> RAMSSD

Watcher1[Watcher 1] -- Unidirectional Control Plane --> LSM1

Watcher1 -- Passive Data Plane --> RAMSSD

end

AIHub[AI Hub] --> Watcher1 -- Limited Actions --> LSM1

AIHub -.-> ASKAHub[ASKA Hub]

ASKAHub -- Policy Decisions --> PolicyEngine["Policy Engine (P4)"]

PolicyEngine -- Critical Actions --> LSM1

DTMS["DTMS (P4)"] -.-> PolicyEngine

DTMS -.-> AIHub

style ASKAHub fill:#ccf,stroke:#333,stroke-width:2px

style PolicyEngine fill:#ccf,stroke:#333,stroke-width:1px

end

classDef ies fill:#ccf,stroke:#333,stroke-width:1px

classDef lsm fill:#aaf,stroke:#333,stroke-width:1px

classDef watcher fill:#ddf,stroke:#333,stroke-width:1px

classDef ai fill:#afa,stroke:#333,stroke-width:1px

classDef data fill:#ddd,stroke:#333,stroke-width:1px

This diagram illustrates the hybrid approach.

Limited Actions: The AI Hub can directly instruct the Watcher Mesh, but only for a pre-defined set of "limited actions" that are considered low-risk. These actions might include increasing monitoring frequency, collecting more detailed logs, or performing specific, non-disruptive checks within the IES.

Critical Actions: For critical actions (isolation, termination), the AI Hub must communicate with the ASKA Hub. The Hub's Policy Engine evaluates the AI Hub's recommendation, considering existing policies and DTMS trust levels. If approved, the Hub issues the critical action command to the LSM.

DTMS Integration: The DTMS provides trust information both to the AI Hub (for its initial assessment) and the Policy Engine (for final authorization).

This hybrid approach balances the need for rapid response to less critical threats with the importance of strict control over high-impact actions.

Key Differences Visualized:

Proposal 2 features the Consensus Engine as the central decision-making component, with no direct AI Hub to LSM connection.

Proposal 3 shows the AI Hub capable of limited direct action through the Watcher Mesh, while critical actions still route through the ASKA Hub and Policy Engine.

Comparison and Selection:

Feature	Proposal 1	Proposal 2	Proposal 3
Security	High	Highest	Medium-High
Speed	Low	Lowest	Medium
Complexity	Medium	High	High
Resilience	Medium	Highest	Medium-High

Strongest Proposal: Proposal 2 (Decentralized Enforcement with Consensus) offers the highest security and resilience due to its decentralized nature and lack of a single point of failure. While it introduces complexity and potential latency, these trade-offs are acceptable for critical security functions. The consensus mechanism ensures that no single compromised component can dictate actions, significantly enhancing the overall security posture.

However, the complexity of implementing a robust and efficient distributed consensus mechanism should not be underestimated. Careful consideration must be given to the specific consensus protocol, network communication overhead, and potential scenarios where consensus might not be reached. A hybrid approach (Proposal 3) could be considered as a stepping stone towards full decentralization, allowing for faster response to less critical threats while the consensus mechanism is being developed and refined.

Review of the Security Mesh hierarchy

I. Core Principles Re-evaluation:

Passive vs. Active Monitoring: Is truly passive monitoring feasible? Could some degree of controlled, minimal interaction improve detection accuracy without sacrificing security? Maybe a combination approach - completely passive at the RAM/SSD level and minimally active in monitoring other system data (e.g., API calls)?

Unidirectional vs. Bidirectional: The emphasis on unidirectional control planes is strong. Is this absolutely necessary for every level? Could selectively bidirectional connections (carefully controlled and audited) reduce overhead and improve responsiveness in non-critical situations? This would introduce some risk but possibly improve usability and detection rates.

Centralized vs. Decentralized AI: Can the AI hub be completely decentralized, similar to the consensus engine? Distributed intelligence modules could analyze subsets of data independently, then only sending aggregated conclusions or high-priority alarms for broader investigation. It depends on how dependent the system's decision-making logic is on this AI component.

Single Point of Failure: Each level (LSM, Watcher, MSM) appears designed with single points of failure, such as AI. To eliminate it, we'd need robust, independent AI processes performing similar checks or an inherent redundancy mechanism within each module. What happens when AI modules have divergent opinions, or different severity-ratings for the same security breach? Can this divergence be leveraged rather than seen as a problem? It could even make it stronger; similar to using redundant/distributed key managers, we might utilize redundant, highly separated AI units.

II. Data Plane Deep Dive - Reconsidering Passivity:

Metadata Types: Is spatiotemporal data alone sufficient? Consider network activity, CPU cycles used by processes, entropy in memory regions, etc. Are we missing crucial anomaly-indicative data points through only looking at what would impact memory?

Data Compression/Abstraction: Reduce the sheer volume of raw data through effective compression or abstraction techniques, without sacrificing crucial anomaly-indicative details. Does formal verification handle compression or need raw, non-modified values to validate the codebase in the Security Mesh architecture? This will determine if we need a change.

Secure Aggregation: How are the results from each LSM aggregated? Are techniques that preserve privacy (e.g., homomorphic encryption, federated averaging, Differential Privacy) needed for this process? Can such a scheme also be formalized?

III. Anomaly Detection Re-evaluation:

Baseline Generation: How are baselines established and dynamically adjusted? Using historical data, and what methods? Will static threshold values work for our needs, or would more dynamic baselines improve accuracy? Formal verification may not easily verify thresholds in live dynamic adjustments and require us to design such components more carefully.

AI Model Selection: What algorithms best balance speed, accuracy, and verifiability? Will simple statistical methods suffice, or is it imperative that complex neural networks be involved in this security monitoring architecture? The complexity of this AI has implications for security as well as verifiability.

False Positives/Negatives: Develop mechanisms for minimizing false positives and false negatives without drastically increasing computational load. How is a high confidence value established for anomalies identified? It may involve generating a 'risk factor' score; can it be incorporated into a formal verification test, using mathematical formulations?

IV. Control Plane Optimization:

Message Reduction: Optimize communications (both number of messages and size). Use compression, encryption, and efficient protocols. A formal verification will need a precise and efficient model here, minimizing parameters to simplify the analysis while accurately describing behavior. It is difficult for algorithms using variable states to formally verify and we may need some design changes to solve the problem.

Authentication and Authorization Mechanisms: Choose robust mechanisms (mTLS, hardware-based authentication, RBAC).

End-to-End Data Integrity: Employ hashing or digital signatures for every message to prevent tampering.

Time synchronization: Is highly accurate, synchronized time between all components necessary for anomaly detection? How will we ensure time synchronization in case one of the system's time-based units is affected or attacked?

V. Simpler Alternatives (trade-off considerations):

Single Level of Monitoring: Explore if combining LSM and Watcher Mesh is simpler and still effective. A hybrid design, where Watchers actively trigger changes to LSM configuration after validation, but critically, never directly interacts with the RAM/SSD.

Limited Actions via Rules Engine: Instead of a sophisticated AI Hub, employ a rules-based engine for decision-making and enforcement, defining permitted actions based on anomalies.

VI. Formal Verification Considerations (Throughout):

Model Checking: Explore model checking to automatically verify the system's behavior.
Theorem Proving: Use theorem proving for more complex properties.
Abstraction: Use abstraction techniques to simplify the formal model.
Modular Verification: Verify the system component by component and then verify integration.
Specification Language: Choose the correct specification language that's best-suited for model checking and supports the complexity.

I. Deep Dive into Passive Data Plane:

Hybrid Data Plane: Instead of purely passive observation, consider a minimally interactive data plane. The LSM could subtly alter access permissions to specific memory regions or temporarily pause processes flagged as anomalous by a dedicated security routine (e.g., increasing virtual-memory overhead or temporarily locking certain resources), for further monitoring. These controlled actions would be temporary, easily reversible, and logged, enhancing analysis without risking direct manipulation.

Data Sampling Strategies: Instead of continuously monitoring all RAM/SSD activity, implement intelligent data sampling strategies (e.g., statistical sampling based on past behavior). It helps optimize the sheer volume of data processed, minimizing overhead while focusing on most important activity patterns. This requires careful consideration on how to minimize biases introduced and validate those aspects, especially for the AI modules, where accuracy must be maintained.

Formal Models for Passive Monitoring: Developing precise formal models of the passive data plane is essential. Can it be represented as a finite state machine, or are other more abstract models appropriate? Will this model incorporate both its security features and accuracy? For each alternative, is this possible? Can we quantify the extent to which that formal specification reflects the system's behavior? It depends on how complex the system actually is, and whether we want an exact copy or an approximated model that's sufficiently detailed for the formal verifier. Different verifiers use different models, such as those using Temporal Logic or those using automata-based representations. These choices greatly impact feasibility, complexity, and security.

II. Enhanced Anomaly Detection:

Contextual Anomaly Detection: Improve anomaly detection by incorporating context-aware rules. For example, anomalies could be flagged based on the process ID, user identity, recent events, and location. The data plane could then use this information to further fine-tune its focus and prioritization and filter out noise, significantly improving efficiency and anomaly-detection.

Ensemble Methods for Anomaly Detection: Consider using an ensemble of different anomaly detection algorithms (e.g., statistical methods, machine learning models). Each individual algorithm can specialize in recognizing different threat types and potentially produce more accurate outcomes. We can then devise an overarching decision mechanism that uses a scoring system based on each algorithm's findings. However, this requires deeper analysis of accuracy, and formal models for these combinations will need to be established to confirm its veracity. It will need verification to prove correctness and resilience, increasing complexity.

Adaptive Thresholds: Implement dynamically adjusted thresholds based on real-time system parameters (network activity, CPU usage) rather than static ones. This could improve accuracy in highly variable situations, especially during attacks. This will however increase the complexity for formal verifications. We need to find a way to create a suitable formal model representing these adaptive algorithms to address it.

III. Streamlined Control Plane Communications:

Asynchronous Communication: Replace real-time communication with an asynchronous mechanism, such as message queues. This significantly reduces performance dependencies. This increases both robustness and the feasibility of formal verifications since these operations would become independent of their order of completion.

Message Compression and Encryption: Use strong compression algorithms for data to reduce message size and improve bandwidth usage, in addition to end-to-end encryption (TLS 1.3 or similar). We would also need to address issues associated with compressing potentially sensitive data since we're handling data streams about security-related operations.

IV. Enhanced AI Hub Architecture (Addressing Centralization):

Federated Anomaly Detection: The various Watchers can communicate locally or among neighbors. Instead of centralized AI in the AI Hub, these local AI modules make independent assessments and transmit conclusions (anomaly detected, action required) or prioritized threat-reports for system-wide responses. This would require a distributed consensus module, ensuring that conclusions of multiple, possibly separated, local AI processes are accurate and reliable. Each Watcher sends its anomaly conclusions and supporting data for that assessment through an intermediary component before being reviewed, enhancing resilience in the architecture. However, formal verification would be significantly harder given the interactions and decision-making logics. This decentralized design introduces some complexity, though greatly enhancing reliability and making it far more suitable for verification tests since a large portion would be tested and independently confirmed to be correct. This will however lead to some unavoidable complications for designing the formal models needed.

AI Model Verification: Can we create separate (smaller) formal verification steps for the AI modules before being incorporated into the main Security Mesh analysis process? It may require a highly specialized model checker and significant engineering effort; which, as the benefit to securing the system outweighs these costs, we should move towards doing. The benefits are greater security in the AI modules that prevent it from being compromised or used maliciously and also reducing overall effort needed when attempting a large, integrated verification scheme.

V. Simplifying the Architecture:

Combined LSM/Watcher: Integrate the functions of the LSM and Watcher Mesh into a single component. It might lead to a less layered, more concise system which will enable formal verification to be attempted far more easily. This has consequences on the Security Mesh's layered security and resilience. We must quantify these trade-offs when considering such changes. Could a simplified model that adequately incorporates the security principles without replicating each layer reduce the overall burden, and potentially be formally verified?

VI. Formal Verification Strategy:

Incremental Verification: Verify each component (LSM, Watcher Mesh, MSM, AI Hub) individually, then verify the integrations incrementally. This significantly simplifies formal verification.

Refinement: Use a refinement approach, moving from high-level abstractions towards concrete implementation details.

Automated Tools: Leverage existing formal verification tools and methodologies appropriate to your chosen specification language.

Property Specification: Formally define the key security properties that need to be verified.

Diagram 11c: Security Mesh Integration

graph LR

subgraph ASKA["ASKA Instance"]

Hub((ASKA Hub)):::high_level

subgraph "Security Mesh"

subgraph "Security Mesh Instance"

SM_Instance{{"Security Mesh Instance"}}

end

IES_Mesh((IES Instance)):::ies -- Data Plane (Read Only) --> RAMSSD[RAM/SSD]:::data

LSM_Mesh[LSM]:::lsm -- Data Plane (Read Only) --> RAMSSD

Watcher_Mesh[Watcher Mesh]:::watcher -- One#8209;Way Control Plane --> LSM_Mesh

Watcher_Mesh -- Data Plane (Read Only) --> RAMSSD

Watcher_Mesh -- Bidirectional Control Plane --> AI[AI Hub]:::ai

MSM[MSM]:::msm -- One#8209;Way Control Plane --> LSM_Mesh

Watcher_Mesh -- One#8209;Way Contro Plane --> MSM

end

IESCluster((("IES Cluster"))):::medium_level

Network["Network & Communication"]:::network

DataResources["Data & Resources"]:::data

UIExternal["UI & External Systems"]:::ui

AuditingGovernance["Auditing & Governance"]:::audit

Hub --> IESCluster

Hub --> Network

Hub --> DataResources

Hub --> UIExternal

Hub --> AuditingGovernance

Hub --> MSM

Hub --> AI

IESCluster -- Secure Channel --> Network

IESCluster -- Resource Access --> DataResources

IESCluster -- UI Interactions --> UIExternal

IESCluster -- Audit Trails --> AuditingGovernance

IESCluster -->|IES Membership| SM_Instance

IES_Mesh -->|IES Membership| SM_Instance

end

classDef high_level fill:#f9f,stroke:#333,stroke-width:2px

classDef medium_level fill:#ccf,stroke:#333,stroke-width:1px

classDef low_level fill:#aaf,stroke:#333,stroke-width:1px

classDef data fill:#afa,stroke:#333,stroke-width:1px

classDef ui fill:#ffa,stroke:#333,stroke-width:1px

classDef audit fill:#ccf,stroke:#333,stroke-width:1px

classDef network fill:#eec,stroke:#333,stroke-width:1px

classDef external fill:#d8d8d8,stroke:#333,stroke-width:1px

classDef ies fill:#ccf,stroke:#333,stroke-width:1px

classDef lsm fill:#aaf,stroke:#333,stroke-width:1px

classDef watcher fill:#ddf,stroke:#333,stroke-width:1px

classDef msm fill:#f9f,stroke:#333,stroke-width:2px

classDef ai fill:#afa,stroke:#333,stroke-width:1px

Description for Diagram 11c: Security Mesh Integration

This diagram illustrates the integration of the Security Mesh within the broader ASKA architecture. The Security Mesh provides a crucial layer of defense, offering comprehensive, real-time, and compromise-resilient monitoring of system activity. This description details the components, interactions, and security benefits of this integration.

I. ASKA Hub:

The ASKA Hub is the central orchestration and management point for the entire system. It facilitates communication and coordination between all other components, including the Security Mesh. The Hub's responsibilities include:

Policy Enforcement: Defines and distributes security policies to other components, including the MSM (Master Security Mesh), which governs the Security Mesh.
Resource Allocation: Manages system resources and allocates them to various components, including the IES instances monitored by the Security Mesh.
Software Updates: Distributes software updates and security patches via the AESDS (Automated Evolutionary Software Development System).
Trust Management: Oversees the DTMS (Dynamic Trust Management System), which plays a crucial role in managing trust levels for components within the Security Mesh.
Inter-Zone Communication: Manages secure communication between different ASKA zones via the SIZCF (Secure Inter-Zone Collaboration Framework).
UI Integration: Provides secure communication and integration with the user interface.
Auditing and Governance: Manages the collection and storage of audit trails and governance information.

II. Security Mesh:

The Security Mesh is a hierarchical, out-of-band monitoring system designed for comprehensive and compromise-resilient security.

Security Mesh Instance: This represents a single instance of the security mesh, which is deployed to monitor a specific set of resources (RAM/SSD). Multiple Security Mesh Instances can be deployed within a ASKA system for comprehensive coverage. It's the core element and container for the mesh's internal workings.

IES Instance (Isolated Execution Stack): The target of the monitoring. Represents the applications and processes running within a hardware-isolated environment. The IES interacts with the RAM/SSD through standard data access channels.

LSM (Local Security Mesh): The LSM performs passive, out-of-band, read-only monitoring of the RAM/SSD, capturing spatiotemporal metadata related to access patterns without interfering with system operation. It provides the raw monitoring data to the Watcher Mesh.

Watcher Mesh: The Watcher Mesh receives monitoring data from the LSM and performs analysis to detect anomalous access patterns. It reports these anomalies to the AI Hub for further investigation and potential response actions. The Watcher Mesh also passively monitors the LSM itself, ensuring its integrity and adding another layer of security (who watches the watchers).

RAM/SSD: These represent the physical memory and storage resources being monitored by the LSM and Watcher Mesh. Crucially, access is passive and read-only, guaranteeing system integrity and preventing interference by the Security Mesh.

AI Hub (AI Cyber Intelligence Hub): Receives anomaly reports from the Watcher Mesh. It uses advanced analytics and AI/ML techniques to correlate events, identify complex attack patterns, and determine appropriate responses. The AI Hub communicates with the ASKA Hub to trigger actions such as isolating compromised IES instances, updating security policies, or initiating self-healing processes. The bidirectional control plane ensures real-time feedback and adaptive security.

MSM (Master Security Mesh): Oversees multiple LSMs and their associated Watcher Meshes. It receives aggregated anomaly reports from the Watcher Meshes and distributes security policies and updates unidirectionally to the LSMs. This one-way control flow prevents a compromised LSM from controlling the MSM or other parts of the system, ensuring compromise resilience.

III. Integration with ASKA Components:

IES Cluster: The IES instances monitored by the Security Mesh are members of the larger IES Cluster, which represents the collection of all isolated execution environments within a ASKA instance. The connection labeled "IES Membership" highlights this relationship. The IES Cluster interacts with other ASKA components via several channels: secure channels for network communication, resource access for data and resources, UI interactions for user interface communication, and audit trails for logging and accountability.

Network & Communication: This subsystem provides the secure communication channels used by the Security Mesh to communicate with the AI Hub and the MSM.

Data & Resources: These represent the system resources (including RAM/SSD) accessed by the IES instances and monitored by the Security Mesh.

UI & External Systems: These components interact with IES instances, and their activity is monitored by the Security Mesh as part of its comprehensive security analysis.

Auditing & Governance: The Security Mesh provides valuable audit data that is integrated into the overall auditing and governance framework.

Security Advantages of Security Mesh Integration:

Defense in Depth: The Security Mesh adds another layer to ASKA's multi-layered security model.

Compromise Resilience: The one-way control plane and independent Watcher Meshes enhance the system's ability to withstand attacks. Even if an LSM is compromised, the Watcher Mesh can still report the anomaly, and the unidirectional control plane prevents the spread of malicious instructions.

Proactive Threat Detection: The passive, read-only RAM/SSD monitoring enables early detection of sophisticated, memory-based attacks.

Real-Time Response: The AI Hub and the bidirectional control plane facilitate real-time analysis and rapid response to detected threats.

Comprehensive Monitoring: The Security Mesh monitors both IES instances and the LSMs themselves, ensuring comprehensive coverage and preventing gaps in security monitoring.

Diagram 12a: Configuration Management and Deployment

graph LR

subgraph "Configuration Management Module (CMM)"

DLT["Decentralized Ledger (P13, P15)"] --> CMM

CMM --> DA[Deployment Agent]

DTMS["DTMS (P4)"] --> CMM

MSM["MSM (P2)"] --> CMM

AESDS["AESDS (P16)"] <--> CMM

IAMA["IAMA (P16)"] --> CMM

GovAI["Governance AI (P15)"] --> CMM

User["User Input (P11)"] --> CMM

end

DA --> AESDS

Policy["Policy Engine (P4)"] --> DA

DTMS --> DA

subgraph "Zone A"

IES_A["IES Cluster (P1)"]

end

subgraph "Zone B"

IES_B["IES Cluster (P1)"]

end

DA --> IES_A

DA --> IES_B

IES_A --> DLT

IES_B --> DLT

External[External Systems / Legacy] --> IAMA --> CMM

Description for Diagram 12a: Configuration Management and Deployment

This diagram details the internal components and connections of the ASKA Configuration Management Module (CMM), highlighting its role in validating, generating, and deploying configurations while integrating with other key ASKA components.

Components:

Decentralized Ledger (DLT) (P13, P15): The DLT serves as the source of truth for TRCs (Trust Root Configurations), policies, and the historical record of configurations. It provides a tamper-proof and auditable repository for critical system information.

TRC Validator (P1, P4): This component retrieves TRCs from the DLT and validates their integrity and authenticity. It checks digital signatures and ensures internal consistency within the TRC structure. It utilizes functionalities described in Patent 1 (IES) and Patent 4 (DTMS).

Policy Input: This represents the entry point for new or modified policies, which can originate from administrators, automated systems, or other ASKA components.

Policy Validator (P4): This component validates incoming policies against predefined rules, constraints, and schema definitions, ensuring that they adhere to the ASKA policy framework as described in Patent 4 (DTMS).

Merge: This function combines the validated TRCs and policies into a single, unified configuration candidate.

Conflict Resolver (P13, P15): This crucial component detects and resolves any conflicts that may arise between TRCs and policies. It uses predefined conflict resolution rules, zone-specific preferences, or a distributed consensus protocol (P13) to determine the final configuration. The resolution process and outcomes are logged on the DLT for transparency and accountability.

Validated Config: This represents the merged, validated, and conflict-free configuration, ready for further processing.

Configuration Generator: This component generates the final configuration artifacts based on the validated input, user input via the Secure UI (P11), and configuration templates. It can leverage AESDS (P16) for automated code generation or optimization based on the configuration.

User Input (P11): This element represents the interface for authorized users to provide input, modify configurations, or trigger specific actions within the CMM through the Secure UI Kernel, providing a secure and controlled way for human interaction.

Configuration Templates: These are predefined templates for common configuration scenarios, simplifying the configuration process and ensuring consistency across deployments.

Version Control: This module manages different versions of configurations, enabling rollback to previous states if necessary. This feature provides resilience and allows for safe experimentation with configuration changes.

Configuration Store (P24): This secure storage, likely implemented within HESE-DAR (P24), stores the finalized configurations, protecting them from unauthorized access or tampering.

Deployment Agent (DA): This agent retrieves configurations from the Configuration Store and initiates the deployment process, distributing the configurations to the appropriate ASKA components (IES instances, network devices, etc.).

Policy Simulator: This component allows administrators to simulate the impact of policy changes before they are deployed, providing a safe environment to test and validate configurations.

Impact Analysis (AI - P10, P16): Using AI and potentially machine learning techniques (P10 and P16), this component analyzes the simulated policy changes and provides insights into their potential impact on system performance, security, and resource utilization. It receives input from the MSM for real-time security context.

Simulation Results (P11): The results of the simulation and impact analysis are presented to the user via the Secure UI Kernel, allowing for informed decision-making about configuration changes.

DTMS (P4): The Dynamic Trust Management System provides real-time trust level information, which influences policy validation, conflict resolution, and deployment decisions within the CMM.

MSM (P2): The Master Security Mesh provides security telemetry and threat intelligence, which can inform configuration choices, particularly within the Impact Analysis component.

AESDS (P16): The Automated Evolutionary Software Development System interacts bidirectionally with the Configuration Generator. AESDS can provide input for generating optimized code or configurations, and the CMM can trigger AESDS to generate or update software components based on the new configuration.

IAMA (P16): The Isomorphic Architecture Monitoring and Adaptation module provides information about connected legacy systems, allowing the Configuration Generator to adapt configurations for compatibility and secure integration.

Governance AI (P15): The Governance AI provides high-level policy recommendations and analysis, influencing both the Conflict Resolver and the Impact Analysis component, ensuring that configurations align with overall governance objectives.

Diagram 12b: Configuration Management Module (CMM) Internals

graph TD

subgraph "Configuration Management Module (CMM)"

subgraph "Policy & TRC Validation"

DLT["Decentralized Ledger (P13, P15)"] --> TRC_Val["TRC Validator (P1, P4)"]

PolicyIn[Policy Input] --> PolicyVal["Policy Validator (P4)"]

TRC_Val --> Merge

PolicyVal --> Merge

Merge --> ConflictRes["Conflict Resolver (P13, P15)"]

ConflictRes --> ValidatedConfig

end

subgraph "Configuration Generation & Versioning"

ValidatedConfig --> ConfigGen[Configuration Generator]

User["User Input (P11)"] --> ConfigGen

Template[Configuration Templates] --> ConfigGen

ConfigGen --> Versioning[Version Control]

Versioning --> ConfigStore["Configuration Store (P24)"]

ConfigStore --> DA[Deployment Agent]

end

subgraph "Simulation & Analysis"

ValidatedConfig --> Sim[Policy Simulator]

Sim --> Analysis["Impact Analysis (AI - P10, P16)"]

Analysis --> SimResults["Simulation Results (P11)"]

SimResults --> User

end

subgraph "ASKA Integration"

DTMS["DTMS (P4)"] --> PolicyVal

DTMS --> ConflictRes

DTMS --> DA

MSM["MSM (P2)"] --> Analysis

AESDS["AESDS (P16)"] <--> ConfigGen

IAMA["IAMA (P16)"] --> ConfigGen

end

subgraph Governance AI Feedback

GovAI["Governance AI (P15)"] --> Analysis

GovAI --> ConflictRes

end

Description for Diagram 12b: Configuration Management Module (CMM) Internals

The diagram details the architecture and operational flow of the CMM within the ASKA secure computing environment. The CMM is responsible for the secure and efficient management of configurations, encompassing validation, generation, simulation, and deployment. It achieves this through a combination of policy and TRC validation, configuration generation and versioning, impact simulation and analysis, and deep integration with other ASKA security modules.

Component Summary:

Decentralized Ledger (DLT) (P13, P15): Stores TRCs, policies, and configuration history, providing a tamper-proof audit trail.
TRC Validator (P1, P4): Verifies the integrity and authenticity of Trust Root Configurations.
Policy Input: Entry point for new/modified policies.
Policy Validator (P4): Ensures policies conform to ASKA's framework.
Merge: Combines validated TRCs and policies.
Conflict Resolver (P13, P15): Detects and resolves conflicts between TRCs and policies.
Validated Config: The resultant conflict-free configuration.
Configuration Generator: Creates configuration artifacts based on validated inputs, user input, and templates, optionally using AESDS (P16) for optimization.
User Input (P11): Secure interface for authorized user interaction with the CMM.
Configuration Templates: Predefined configurations for common scenarios.
Version Control: Manages configuration versions and enables rollback.
Configuration Store (P24): Securely stores configurations within HESE-DAR.
Deployment Agent (DA): Deploys configurations to ASKA components.
Policy Simulator: Simulates the effects of policy changes.
Impact Analysis (AI - P10, P16): Uses AI to analyze the impact of policy simulations.
Simulation Results (P11): Presents simulation outcomes to the user.
DTMS (P4): Provides trust information and influences configuration processes.
MSM (P2): Provides security telemetry and threat intelligence.
AESDS (P16): Automates software generation/deployment, integrates with configuration generation.
IAMA (P16): Facilitates legacy system integration by informing the Configuration Generator about legacy system specifics, ensuring compatibility and secure integration.
Governance AI (P15): Offers high-level policy guidance for conflict resolution and impact analysis.

Detailed Process and ASKA Integration

The CMM process begins with the retrieval of TRCs and the input of new policies. The TRC Validator (leveraging Patent 1 and 4) ensures the integrity of retrieved TRCs, while the Policy Validator (P4) checks the validity of incoming policies. These validated components are merged, and any conflicts are resolved by the Conflict Resolver, which consults the DTMS (P4) for trust information and the Governance AI (P15) for higher-level policy directives. The Conflict Resolver uses either pre-defined rules, consensus mechanisms, or a combination of approaches depending on the nature of the conflict and the specific policies involved. Resolution details are logged on the DLT for transparency.

The resulting Validated Config is then used by the Configuration Generator to create the necessary configuration artifacts. This process may involve user input (P11) and leverage predefined Configuration Templates. Critically, the Configuration Generator integrates with AESDS (P16) allowing for automated generation of optimized code and configuration files, further enhancing security by incorporating best practices and mitigating potential vulnerabilities. The IAMA module (P16) feeds information about legacy systems to the Configuration Generator, enabling it to create configurations that are both secure and backward-compatible.

Before deployment, the validated configuration can be fed into the Policy Simulator, which, combined with the Impact Analysis module (using AI – P10, P16), provides insights into the potential effects of the changes. This process integrates with the MSM (P2) for real-time security context and the Governance AI (P15) for alignment with strategic objectives. Simulation Results are presented to the user via the Secure UI (P11) for review and approval. This feedback loop allows administrators to fine-tune configurations before they are deployed, minimizing risks and optimizing for desired outcomes.

The finalized configuration is then stored in the Configuration Store, secured by HESE-DAR (P24). The Deployment Agent (DA) retrieves the configuration from this secure store and orchestrates its deployment to target components (IES clusters, network elements, etc.), leveraging DTMS (P4) for authorization and secure communication channels. The entire process, including configuration changes, deployments, simulations, and resolutions, is logged on the Decentralized Ledger (DLT - P13, P15), creating a comprehensive audit trail secured by MDATS (P17).

Security Considerations:

This architecture tightly integrates with ASKA's security infrastructure:

Hardware-Rooted Trust: The involvement of HESE-DAR (P24) in securing the Configuration Store ensures that the configurations themselves are protected from tampering.
Decentralized Governance: The use of the DLT, consensus mechanisms (P13), and Governance AI (P15) fosters transparency and accountability in the configuration management process.
Dynamic Trust Management: DTMS (P4) ensures that configuration deployments and access are aligned with real-time trust levels, dynamically adapting to changing security conditions.
Automated Security: AESDS (P16) enhances security by automating code generation and incorporating best practices and security hardening into configurations.
Isolation and Compartmentalization: Configurations are deployed to isolated IES instances (P1), limiting the blast radius of any potential compromise resulting from a misconfiguration.
Defense in Depth: The integration with MSM (P2) provides another layer of security by feeding real-time threat intelligence into the configuration process and alerting on anomalous activities.

Diagram 13: Error Handling and Logging

graph LR

subgraph "ASKA Hub"

subgraph "Configuration Management (CMM)"

DLT["Decentralized Ledger (P13, P15)"] --> CMM

CMM --> DA[Deployment Agent]

end

DA --> AESDS["AESDS (P16)"]

Policy["Policy Engine (P4)"] --> DA

DTMS["DTMS (P4)"] --> DA --> IES_Clusters

end

subgraph "IES Clusters (P1)"

IES1[IES 1] --> Local_Err1["Local Error Module (P7)"]

IES2[IES 2] --> Local_Err2["Local Error Module (P7)"]

IESn[IES n] --> Local_Errn["Local Error Module (P7)"]

subgraph IES 1

App1["Application 1"] --> Local_Log1[Local Error Log] --> Local_Err1

Kernel1[Secure Kernel] --> Local_Log1

end

subgraph IES 2

App2["Application 2"] --> Local_Log2[Local Error Log] --> Local_Err2

Kernel2[Secure Kernel] --> Local_Log2

end

subgraph IES n

Appn["Application n"] --> Local_Logn[Local Error Log] --> Local_Errn

Kerneln[Secure Kernel] --> Local_Logn

end

Local_Err1 --> ErrAgg

Local_Err2 --> ErrAgg

Local_Errn --> ErrAgg

end

subgraph "Out#8209;of#8209;Band Error Module (P35)"

ErrAgg[Error Aggregator] --> Redactor["Secure Log Redactor (P16, P17)"]

Redactor --> LevelA[Security Level A Output]

Redactor --> LevelB[Security Level B Output]

Redactor --> LegacyOut[Legacy System Output]

DTMS --> Redactor

MSM["MSM (P2)"] --> ErrAgg

end

subgraph "Multi#8209;Channel Network (P3)"

LevelA --> ChannelA[Secure Channel A]

LevelB --> ChannelB[Secure Channel B]

LegacyOut --> ChannelL[Legacy Channel]

ChannelA --> SIEM[SIEM Integration]

ChannelB --> SIEM

ChannelL --> SIEM_Legacy[Legacy SIEM]

subgraph "Dedicated Unswitched Network Paths"

ChannelA --> SIEM

ChannelB --> SIEM

ChannelC["Secure Channel C (for future use)"] --> SIEM

ChannelD["Secure Channel D (for future use)"] --> SIEM

end

subgraph "External Systems / Legacy"

IAMA["IAMA (P16)"] --> CMM

External[External Systems] --> SIZCF["SIZCF (P22)"]

end

External --> STN["STN (P27)"] --> DTG["DTG (P28)"] --> ATN["ATN (P3)"] --> IES_Clusters

DLT --> STN

DLT --> CMM

DLT --> ErrAgg

HESE_DAR["HESE-DAR (P24)"] --> IES_Clusters

HESE_DAR --> STN

HESE_DAR --> CMM

AESDS <--> CMM

SecureUI["Secure UI Kernel (P11)"] --> IES_Clusters

Description for Diagram 13: Error Handling and Logging

Decentralized Log Generation: Within each IES instance (P1), applications (App1, App2, Appn) and the Secure Kernel generate error logs. These are sent to a Local Error Module (P7) within each IES. This decentralized approach ensures that even if one IES is compromised, logging continues in others. Each Local Error Module performs initial filtering and formatting of logs. The Secure Kernel within each IES instance has direct access to the Local Error Log, enabling it to record critical kernel-level errors and system events directly.

Error Aggregation: The local error modules (Local_Err1, Local_Err2, Local_Errn) send their pre-processed logs to a central Error Aggregator (ErrAgg) within the Out-of-Band Error Module (P35). The MSM (P2) also feeds system-wide security events and anomalies to the aggregator.

Secure Log Redaction: The Error Aggregator passes the logs to the Secure Log Redactor. This component, leveraging AI techniques from AESDS (P16) and the audit capabilities of MDATS (P17), scans for and redacts sensitive information (cryptographic keys, PII, etc.) according to predefined rules and policies. The redaction process itself is meticulously audited, with records of each redaction action stored on the DLT, linked to a 3D microstructure (P14, P17) if necessary. The DTMS (P4) can dynamically influence the redaction policies based on the current security context.

Multi-Level Output: The redacted logs are then categorized into three security levels (A, B, and Legacy). This categorization allows for different levels of detail and access control to be applied to the logs based on their sensitivity and the intended recipient.

Secure SIEM Integration: Four dedicated, unswitched network pathways through the Multi-Channel Network (P3) connect the error module outputs to the SIEM system. This dedicated, non-switchable architecture guarantees that error reports are transmitted securely and reliably, preventing interception or manipulation. Level A and B outputs go to the main SIEM, while the Legacy output goes to a separate Legacy SIEM, if needed, for systems that cannot handle the ASKA log format. The unswitched pathways and multiple channels provide redundancy and resilience.

ASKA Integration: The diagram illustrates the error module's integration with other key components:

DLT (P13, P15): Stores configuration history and audit logs.
CMM: Manages configuration and deployment of error handling policies.
DTMS (P4): Provides trust information for access control and dynamic policy adjustments within the error module.
MSM (P2): Feeds security events and anomalies into the error module.
HESE-DAR (P24): Can be used for secure storage of error logs.
AESDS (P16): Provides AI capabilities for log redaction and automated software updates for the error module.
IAMA (P16): Assists with legacy system integration related to error handling, ensuring that legacy systems can still send and receive error information in a secure and compatible format.

Diagram 14a: Onboard AI Agent

graph

%% Defining color-coded classes

classDef hub fill:#FFEBE5,stroke:#FF6B4A,stroke-width:2px,color:#5A1B00;

classDef agent fill:#E5F4FF,stroke:#409EFF,stroke-width:2px,color:#003152;

classDef integration fill:#E5FFEB,stroke:#34D399,stroke-width:2px,color:#00663C;

classDef monitoring fill:#FFF5E5,stroke:#FFA740,stroke-width:2px,color:#553000;

classDef optional fill:#F0E5FF,stroke:#A855F7,stroke-width:2px,color:#3C0061;

classDef user fill:#FFF0F0,stroke:#FF5252,stroke-width:2px,color:#5A0000;

subgraph "ASKA Hub"

AggHub((Aggregation & Processing Hub)):::hub --> SecureUI(["Secure UI Kernel (P11)"]):::hub

DTMS["DTMS (P4)"]:::hub --> AggHub

MSM{{"MSM (P2)"}}:::hub --> AggHub

Policy{"Policy Engine (P4)"}:::hub --> AggHub

end

subgraph "AI Agent IES (P1)"

subgraph "LLM Engine"

ModelStore{{"Model Storage (HESE-DAR - P24)"}}:::agent --> InferenceEngine(["Inference Engine (P12)"]):::agent

Tokenizer((Tokenizer)):::agent --> InferenceEngine

Context{{"Context Management"}}:::agent --> InferenceEngine

end

subgraph "ASKA Integrations"

DTMSint{{"DTMS Integration (P4, P25)"}}:::integration <--> DTMS

MSM --> MSMint(["MSM Integration (P2)"]):::integration

AESDS{{"AESDS (P16)"}}:::integration --> AESDSint(["AESDS Integration (P16)"]):::integration

DLTint{{"DLT Integration (P13, P15, P3)"}}:::integration <--> DLT((Decentralized Ledger)):::integration

RMint(["Resource Mgr Integration (P9, P10)"]):::integration <--> RM((Resource Manager)):::integration

SIZCFint(["SIZCF Integration (P22)"]):::integration <--> SIZCF{{"SIZCF"}}:::integration

IAMAint{{"IAMA Integration (P16)"}}:::integration <--> IAMA((IAMA)):::integration

end

subgraph "UI Monitoring Module"

UI_Kernel(("Secure UI Kernel (P11)")):::monitoring -- Data Diode (P2) --> UI_Monitor{{"UI Monitor"}}:::monitoring

UI_Monitor --> Sanitizer((Sanitizer/ Filter)):::monitoring --> LLMEngineIn["LLM Inference Parser"]

end

LLMEngineIn --> InferenceEngine

InferenceEngine --> AgentAPI(("Agent API (P2, P25)")):::agent

AgentAPI --> AggHub

subgraph "Optional Components (Future)"

FLint{{"Federated Learning Integration (P19)"}}:::optional

ExtInt((External System Integration)):::optional

end

User(((User))):::user --> SecureUI

SecureUI -.-> UI_Kernel

class AggHub,SecureUI,DTMS,MSM,Policy hub

class ModelStore,InferenceEngine,Tokenizer,Context,LLMEngineIn,AgentAPI agent

class DTMSint,MSMint,AESDS,AESDSint,DLTint,RMint,SIZCFint,IAMAint integration

class UI_Kernel,UI_Monitor,Sanitizer monitoring

class FLint,ExtInt optional

class User user

Description for Diagram 14a: Onboard AI Agent

Component and Connection Details:

ASKA Hub: Contains components responsible for aggregating, processing, and disseminating information from the AI agent.

Aggregation & Processing Hub: Receives data from the AI agent, correlates it with data from other ASKA components (DTMS, MSM, Policy Engine), and sends processed information back to the user or other components.
DTMS (P4): Provides trust information and receives updates from the AI Agent.
MSM (P2): Provides security telemetry and receives alerts from the AI Agent.
Policy Engine (P4): Enforces policies on AI agent actions and data access.

AI Agent IES (P1): The isolated execution environment hosting the AI agent.

LLM Engine:

Model Storage (HESE-DAR - P24): Secure storage for the LLM model.
Inference Engine (P12): Performs LLM inference, potentially with hardware acceleration.
Tokenizer: Preprocesses text for the LLM.
Context Management: Maintains conversation context.

ASKA Integrations: Modules for secure communication and data exchange with other ASKA components. Note the bidirectional connections to DTMS and DLT for both sending and receiving data. The unidirectional connection from MSM prevents the AI Agent from interfering with core security functions.
UI Monitoring Module: Passively observes UI interactions using a data diode (P2) for security. Includes a Sanitizer/Filter to protect the LLM from malicious input.
Agent API (P2, P25): Secure interface for other ASKA components to interact with the agent.
Optional Components (Future): Illustrates potential future integrations for Federated Learning (P19) and External Systems.

User Interaction: The user interacts with the Secure UI Kernel, which is monitored by the AI agent.

Data Flow:

User input flows through the Secure UI Kernel.
The UI Monitoring Module passively observes UI events and sends sanitized data to the LLM Engine.
The LLM Engine processes data and sends results to the Aggregation and Processing Hub via the Agent API.
The Hub correlates data from various sources and sends a response back to the Secure UI.

Connections: Solid lines represent typical data/control flow. Dotted lines represent monitoring or indirect relationships. The data diode (P2) connection ensures unidirectional data flow from the UI to the AI agent, enhancing security.

Diagram 14b: Onboard AI Agent Update Process

flowchart TD

subgraph "AI Agent IES (P1)"

subgraph "ASKA Hub(Not in AI Agent)"

AESDS(("AESDS (P16)")) --> UpdateMgr((("Update Manager")))

Policy{{"Policy Engine (P4)"}} --> UpdateMgr

UpdateMgr --> MCN[/"Multi-Channel Network (P3, P5)"/]

DTMS(("DTMS (P4)")) --> UpdateMgr

MSM(["MSM (P2)"]) --> |Monitoring| LLM_Engine

UpdateMgr -.- DLT{{"Decentralized Ledger (P13, P15)"}}

RM["Resource Manager (P10)"] --> UpdateMgr

end

subgraph "LLM Engine"

UpdatePkg{{"Update Package (Signed & Verified)"}} --> Installer((Installer))

HESE_DAR["HESE-DAR (P24)"] --> |Secure Storage| ModelStore[(Model Storage)]

ModelStore --> InferenceEngine(("Inference Engine (P12)"))

Tokenizer[/Tokenizer/] --> InferenceEngine

Context["Context Management"] --> InferenceEngine

Installer --> ModelStore

Installer --> Rollback(("Rollback Mechanism"))

Rollback --> ModelStore

IAMA{{"IAMA (P16)"}} --> Sandbox(("Sandbox (P1)"))

Sandbox --> Validation{{"Validation Results"}} --> Installer

end

LLM_Engine --> AgentAPI(("Agent API (P2, P25)"))

AgentAPI --> |Results/Actions| AggHub["Aggregation & Processing Hub"]

AggHub --> SecureUI(("Secure UI Kernel (P11)"))

User(["User"]) --> SecureUI

Integrations{{"ASKA Integrations (P2, P3, P4, P13, P15)"}} <--> DTMS

Integrations -.- MSM

Integrations -.- AESDS

end

MCN ----> UpdatePkg

UpdateMgr ----> IAMA

%% Define styles

classDef mainNode fill:#FFDAC1,stroke:#FF9AA2,stroke-width:2,color:#333;

classDef policyNode fill:#B5EAD7,stroke:#70C1B3,stroke-width:2,color:#333;

classDef networkNode fill:#FFB7B2,stroke:#F08080,stroke-width:2,color:#333;

classDef engineNode fill:#F3E9F0,stroke:#FF7171,stroke-width:2,color:#333;

classDef storageNode fill:#FFDAC1,stroke:#FF9AA2,stroke-width:2,color:#333;

classDef processNode fill:#D7DEEA,stroke:#A2C2F4,stroke-width:2,color:#333;

%% Apply styles to specific nodes

class AESDS,UpdateMgr,UpdatePkg,AggHub,Sandbox,Sandbox storageNode;

class Policy,DLT,RM,AgentAPI,IAMA,Validation,SecureUI,Integrations policyNode;

class MCN,MSM,LLM_Engine,DTMS networkNode;

class InferenceEngine,Installer,Rollback engineNode;

class ModelStore,HESE_DAR storageNode;

class User,Tokenizer,Context processNode;

Description for Diagram 14b: Onboard AI Agent Update Process

Components and Connections:

ASKA Hub: Manages the LLM update process.

AESDS (P16): Orchestrates the update process.
Update Manager: Handles update package verification, deployment, and rollback.
Policy Engine (P4): Enforces policy compliance for updates.
Multi-Channel Network (P3, P5): Securely delivers update packages.
DTMS (P4): Provides trust information and security policies.
MSM (P2): Monitors the LLM Engine after updates.
Decentralized Ledger (P13, P15): Logs update events.
Resource Manager (P10): Allocates resources for staged rollouts.

AI Agent IES (P1): Contains the LLM Engine and related components.

LLM Engine:

Update Package: The incoming, signed and verified update.
Installer: Installs the validated update package.
HESE-DAR (P24): Secure storage for models.
Model Storage: Stores the current LLM model.
Inference Engine (P12): Executes the LLM.
Tokenizer: Preprocesses text.
Context Management: Maintains context.
Rollback Mechanism: Reverts to the previous model if needed.
IAMA (P16) and Sandbox (P1): Perform isolated validation of the update package.

Agent API (P2, P25): Allows other ASKA components to access the LLM.
ASKA Integrations: Handles communication with other ASKA components.

User Interaction: The user interacts with the Secure UI, which is connected to the AI Agent IES (although the LLM Engine doesn't directly interact with the UI).

Data Flow:

Update packages are delivered via the Multi-Channel Network (MCN).
The Update Manager verifies and deploys the update to the AI Agent IES.
IAMA creates an isomorphic model for sandboxed validation.
The Installer updates the Model Storage within HESE-DAR.
The LLM Engine uses the updated model.
The MSM continuously monitors the LLM Engine.
The Agent API provides access to the updated LLM for other ASKA components.
All update-related events are logged on the Decentralized Ledger.

Diagram 14c: Onboard AI Agent Security System Integration

graph TD

subgraph "ASKA Security System"

subgraph "Master Security Mesh (MSM #8209; P2)"

MSM --> ThreatIntelFeed(["Threat Intelligence Feed"])

MSM --> NetworkMon(("Network Monitor (P3)"))

MSM --> SystemMon{{"System Monitor (P7, P8)"}}

MSM --> LocalMSMs[["Local Security Meshes (P2)"]]

ThreatIntelFeed --> AnomalyDetection(("Anomaly Detection Engine (P7)"))

AnomalyDetection --> MSM

NetworkMon --> MSM

SystemMon --> MSM

LocalMSMs --> MSM

AnomalyDetection --> AlertSystem(("Alert System (P7)"))

end

subgraph "Onboard AI Agent (Security Module)"

subgraph "Threat Analysis   Hunting"

ThreatIntelFeed --> AI_ThreatIntel([AI Threat Intelligence])

AI_ThreatIntel --> ProactiveHunting((Proactive Threat Hunting))

ProactiveHunting --> ThreatReports{{Threat Reports}}

MSM --> AI_ThreatIntel

AnomalyDetection --> AI_AnomalyAnalysis([AI Anomaly Analysis])

AI_AnomalyAnalysis --> ThreatReports

end

subgraph "Alert Prioritization & Response"

AlertSystem --> AI_AlertPrioritization([AI Alert Prioritization])

AI_AlertPrioritization --> PrioritizedAlerts{{Prioritized Alerts}}

AI_AlertPrioritization --> AutomatedResponse(("Automated Response (P7)"))

AutomatedResponse --> ResponseSystem{{"Response System (P7)"}}

end

UI_Kernel(["Secure UI Kernel (P11)"]) -- "Data Diode (P2)" --> UI_Monitor((UI Monitor))

UI_Monitor --> AI_AlertPrioritization

UI_Monitor --> AI_AnomalyAnalysis

subgraph "Detailed Reporting    Analysis"

ThreatReports --> DetailedAnalysis{{Detailed Threat Analysis}}

DetailedAnalysis --> RemediationRecommendations([Remediation Recommendations])

end

subgraph "ASKA Integration"

DTMS(["DTMS (P4)"]) --> AI_AlertPrioritization

DTMS --> AI_ThreatIntel

DLT(("Decentralized Ledger (P13, P15)")) --> AI_ThreatIntel

DLT --- AI_AlertPrioritization

DLT --- DetailedAnalysis

AgentAPI([Agent API]) --> ResponseSystem

ConsensusEngine(("Consensus Engine (P13)")) --> SecurityActions{{Security Actions}}

AI_AnomalyAnalysis --> ConsensusEngine

AI_AlertPrioritization --> ConsensusEngine

AI_ThreatIntel --> ConsensusEngine

end

ResponseSystem --> MSM

SecurityActions --> ResponseSystem

RemediationRecommendations --> SecurityPersonnel([Security Personnel])

PrioritizedAlerts --> SecurityPersonnel

SecurityReports([Security Reports]) --> SecurityPersonnel

ThreatReports --> SecurityReports

DetailedAnalysis --> SecurityReports

end

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style ThreatIntelFeed fill:#a8dadc,stroke:#457b9d,stroke-width:2px

style NetworkMon fill:#ffd60a,stroke:#f1faee,stroke-width:2px

style SystemMon fill:#b0f9b6,stroke:#264653,stroke-width:2px

style LocalMSMs fill:#f1faee,stroke:#1d3557,stroke-width:2px

style AnomalyDetection fill:#e9c46a,stroke:#f4a261,stroke-width:2px

style AlertSystem fill:#b0f9b6,stroke:#264653,stroke-width:2px

style AI_ThreatIntel fill:#ffd60a,stroke:#f1faee,stroke-width:2px

style ProactiveHunting fill:#a8dadc,stroke:#457b9d,stroke-width:2px

style ThreatReports fill:#ffcccb,stroke:#e63946,stroke-width:2px

style AI_AnomalyAnalysis fill:#b0f9b6,stroke:#264653,stroke-width:2px

style AI_AlertPrioritization fill:#e9c46a,stroke:#f4a261,stroke-width:2px

style PrioritizedAlerts fill:#ffd60a,stroke:#f1faee,stroke-width:2px

style AutomatedResponse fill:#f1faee,stroke:#1d3557,stroke-width:2px

style ResponseSystem fill:#b0f9b6,stroke:#264653,stroke-width:2px

style DTMS fill:#a8dadc,stroke:#457b9d,stroke-width:2px

style DLT fill:#ffcccb,stroke:#e63946,stroke-width:2px

style AgentAPI fill:#ffd60a,stroke:#f1faee,stroke-width:2px

style ConsensusEngine fill:#b0f9b6,stroke:#264653,stroke-width:2px

style SecurityActions fill:#e9c46a,stroke:#f4a261,stroke-width:2px

style SecurityPersonnel fill:#f1faee,stroke:#1d3557,stroke-width:2px

linkStyle 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18 stroke:#555,stroke-width:2px

Description for Diagram 14c: Onboard AI Agent Security System Integration

This diagram illustrates the integrated operation of ASKA's security system, highlighting the collaborative relationship between the Master Security Mesh (MSM) and the Onboard AI Agent. It emphasizes how these two subsystems work together, leveraging a consensus-driven approach, to provide comprehensive and robust security within ASKA.

1. Master Security Mesh (MSM - P2): The MSM acts as the central nervous system for ASKA's security, collecting and correlating data from various sources:

Threat Intelligence Feed: External threat data providing context and awareness of emerging threats.
Network Monitor (P3): Monitors network traffic flowing in and out of ASKA, using techniques like deep packet inspection (DPI) and anomaly detection to identify suspicious patterns.
System Monitor (P7, P8): Observes the behavior and performance of internal ASKA components, leveraging hardware-based anomaly detection (P7) and memory protection mechanisms (P8) to detect deviations from expected behavior.
Local Security Meshes (P2): Each Isolated Execution Stack (IES) has a local security mesh that monitors internal activity within the IES and reports relevant data to the MSM.

The data from these sources feeds into the Anomaly Detection Engine (P7), which analyzes it for anomalies and potential threats. The Anomaly Detection Engine then triggers the Alert System (P7) to generate alerts for security incidents.

2. Onboard AI Agent (Security Module): This intelligent agent enhances the security system by providing advanced analysis, proactive threat hunting, and context-aware alert prioritization. It's crucial to understand that this agent is not part of the MSM but works in conjunction with it.

Threat Analysis & Hunting: The AI Agent leverages threat intelligence from the MSM and performs proactive threat hunting. It uses AI techniques to analyze potential threats and generate detailed threat reports.

Alert Prioritization & Response: The AI Agent receives alerts from the Alert System and prioritizes them based on severity, context, and user activity observed through the UI Monitor. It can also trigger Automated Responses (P7) through the Response System (P7), which then feeds back to the MSM. The AI agent also receives input from the Secure UI Kernel (P11) through a unidirectional Data Diode (P2), allowing it to incorporate user context into its analysis and prioritization without risking UI manipulation.

Detailed Reporting & Analysis: The AI Agent provides detailed threat reports, analysis, and Remediation Recommendations to security personnel.

ASKA Integration: The AI Agent integrates with other ASKA components:

DTMS (P4): For access to trust levels and security policies.
Decentralized Ledger (P13, P15): For logging actions and accessing historical data.
Agent API: Provides an interface for other security components to request specific analysis or actions from the AI agent.

3. Consensus-Driven Security:

The diagram highlights the consensus-driven nature of the security system:

Consensus Engine (P13): The AI Anomaly Analysis, AI Alert Prioritization, and AI Threat Intelligence modules within the AI Agent feed into a Consensus Engine (P13). This engine uses a distributed consensus mechanism to reach agreement on appropriate security actions, preventing any single component from making potentially disruptive decisions on its own. This is designed to achieve the following goals: preventing a single compromised AI model or MSM from hijacking the system, ensuring consensus-based decision making for high-impact actions, and improving transparency and auditability of the security decisions made.

Security Actions: The agreed-upon security actions are sent to the Response System for execution, improving the accuracy and reliability of security responses.

4. Human Oversight: Ultimately, Security Personnel receive Prioritized Alerts, Security Reports, and Remediation Recommendations, providing human oversight of the AI-driven security process.

Consensus-Driven AI Security:

UI Monitoring Integration: The UI Monitor now feeds into both AI Alert Prioritization and AI Anomaly Analysis. This allows the AI agent to correlate user behavior with system events, potentially identifying insider threats or user errors that might contribute to security vulnerabilities. The unidirectional data diode connection remains crucial for security.

Consensus Engine (P13): A Consensus Engine (P13) is introduced. This engine receives inputs from multiple AI modules:

AI Anomaly Analysis: Provides insights based on system anomalies.
AI Alert Prioritization: Contributes context and risk assessments.
AI Threat Intelligence: Offers broader threat landscape information.

The Consensus Engine uses a distributed consensus algorithm (similar to those used in blockchain - P13, P15) to reach an agreement on the appropriate security actions. This consensus-driven approach prevents any single AI component from making potentially disruptive security decisions unilaterally. It also increases resilience against manipulation or compromise of individual AI modules.

Security Actions: The Consensus Engine's output, the agreed-upon Security Actions, is sent to the Response System (P7), which executes the actions. This ensures that responses are based on a consensus view, reducing the risk of false positives or inappropriate actions.

Detailed Analysis and Reporting: The Detailed Analysis component also receives input from the Consensus Engine. It produces detailed Threat Reports, prioritized alerts, and remediation recommendations for human security personnel, which helps security teams investigate and respond to threats effectively.

How the Consensus Mechanism Works:

The various AI modules (Anomaly Analysis, Alert Prioritization, Threat Intelligence) independently analyze data from their respective sources (MSM, UI Monitor, Threat Intel Feeds, Decentralized Ledger).

Each module submits its proposed security action (or a "vote") to the Consensus Engine.

The Consensus Engine uses a distributed consensus algorithm to reach an agreement among the AI modules. This could be a simple majority vote, a weighted vote based on trust levels, or a more sophisticated consensus protocol. The Decentralized Ledger logs all votes and the final consensus decision.

The agreed-upon security action is sent to the Response System for execution. Simultaneously, a detailed report is compiled and sent to human security personnel for review and further action.

Example Scenario:

The Network Monitor detects unusual network traffic patterns. The Anomaly Detection Engine flags this as a potential DDoS attack. Simultaneously, the UI Monitor observes the user attempting to access a restricted resource. The AI Agent correlates these events and proposes blocking the user's access and implementing rate-limiting on the affected network interface. The Consensus Engine receives this proposal and other inputs from the Threat Intelligence module. If a consensus is reached, the Response System takes the appropriate actions, while a detailed report is generated for security personnel.

This enhanced design integrates a robust consensus mechanism into the AI-driven security system, leveraging insights from multiple sources (UI, system internals, threat intelligence) and preventing any single AI component from making critical security decisions autonomously. The use of a distributed consensus algorithm adds resilience and transparency to the system, further strengthening ASKA's comprehensive security posture.

Diagram 15: BCI Integration:

graph LR

subgraph "ASKA Hub (P4)"

DTMS(("DTMS (P4)")) --> ASKA

end

subgraph "ASKA"

subgraph "ASKA Environment"

subgraph "Secure Data Enclaves (P20)"

AI_Model(("AI Model (P20)")) --> SecureDataEnclaves["Data Enclaves"]

Federated_Learning(("Federated Learning (P19)")) --> SecureDataEnclaves

end

subgraph "Secure Kernel (P1)"

subgraph "Hardware Isolation (IES - P1)"

BCI_Hardware(("BCI Hardware (P1)")) --> IES

end

SecureKernel["Kernel"] --> SecureDataEnclaves

SecureKernel --> Encryption(("Encryption (P2)"))

subgraph "Trusted Execution Environment (TEE)"

AI_Agent(("AI Agent (P16, P20)")) --> TEE

end

subgraph "Data Storage (P24)"

HESE_DAR(("HESE-DAR (P24)")) --> SecureDataEnclaves

HESE_DAR --> Access_Control["Access Control (P2, P25)"]

end

subgraph "Network Channels"

subgraph "Secure Channels (P3, P5)"

subgraph "Secure Cloud"

Cloud_Service(("Cloud Service")) --> SecureChannels["Secure Channels>"]

end

SecureChannels --> SecureKernel

end

subgraph "Security Mechanisms"

Encryption(("Encryption (P2)")) --> ASKAEnvironment["ASKA Environment"]

Data_Diodes(("Data Diodes (P2)")) --> ASKAEnvironment

TEE((TEE)) --> ASKAEnvironment

Non_Switchable_Channels(("Non-Switchable Channels (P3, P5)")) --> ASKAEnvironment

end

subgraph "User Interface"

UI_Kernel(("UI Kernel (P11)")) --> Data_Diode["Data Diode"]

Data_Diode --> SecureKernel

UI_Kernel --> UI_Monitor((UI Monitor))

UI_Monitor --> PrioritizedAlerts((Prioritized Alerts))

subgraph "User"

UserX((User Action))

UserX --> UI_Monitor

UserX --> PrioritizedAlerts

end

subgraph "BCI Input"

BCI_Input((BCI Input)) --> UI_Monitor

end

PrioritizedAlerts --> AI_AlertPrioritization((AI Alert Prioritization))

AI_AlertPrioritization --> SecureKernel

UI_Monitor --> AI_AnomalyAnalysis((AI Anomaly Analysis))

AI_AnomalyAnalysis --> SecureKernel

UI_Monitor --> AI_ThreatIntel((AI Threat Intelligence))

AI_ThreatIntel --> SecureKernel

%% Define styles

classDef pastelNode fill:#D1E8E4,stroke:#A8D8D0,stroke-width:2

classDef pastelEdge stroke:#345774,stroke-width:1

%% Apply styles to nodes and edges

class DTMS,AI_Model,Federated_Learning,BCI_Hardware,SecureKernel,Encryption,AI_Agent,HESE_DAR,Access_Control,UI_Kernel,UI_Monitor,PrioritizedAlerts,UserX,BCI_Input,AI_AlertPrioritization,AI_AnomalyAnalysis,AI_ThreatIntel pastelNode;

Description for Diagram 15: BCI Integration

Explanation:

ASKA Hub (P4): This is the central control point, managing overall security policies and trust levels. It uses DTMS (P4) to enforce those policies.
ASKA Environment: This represents the ASKA system itself, including:

Secure Data Enclaves (P20): Protected environments for AI model training and processing.
Secure Kernel (P1): The core of the system, handling data acquisition, processing, and encryption.
Hardware Isolation (IES - P1): A secure and isolated container for the BCI hardware.
Trusted Execution Environment (TEE): Protects the AI Agent and sensitive operations.
Data Storage (P24): Securely stores encrypted data within HESE-DAR (P24).
Network Channels (P3, P5): Secure communication channels to the cloud environment.

User Interface: The interface that the user interacts with, including the Secure UI Kernel (P11), the UI Monitor, and the Prioritized Alerts.
Connections:

BCI Hardware: Connected to the secure kernel via a data diode (P2).
Secure Channels: Connects the secure kernel to the cloud environment.
UI Monitor: Receives user input, analyzes data, and sends alerts to the secure kernel.

Security Mechanisms: ASKA's various security mechanisms (encryption, data diodes, TEE) protect the BCI module and its data.
AI Models: Trained using differential privacy and federated learning techniques, ensuring user privacy.

Key Points:

Data Diode: Ensures that data flows only from the BCI hardware to the secure kernel, preventing any manipulation.
Hardware Isolation: Protects the BCI hardware from potential attacks by isolating it within a ASKA IES instance.
Encryption: All brainwave data is encrypted at rest and in transit.
Trusted Execution Environment: Protects the AI Agent from potential threats.
Non-Switched Secure Network Channels: Guarantee secure and reliable communication to the cloud environment.
AI Model Training: Uses privacy-preserving AI techniques to protect individual users' data.

Diagram 16a: Remote UI Session

graph

subgraph "Client"

ClientApp[ASKA Remote Client] --> SecureChannel["Secure Communication (P2, P3, P5)"]

end

subgraph "ASKA Hub"

direction LR

Auth["Authentication (MFA - P23)"] --> SessionMgr[Session Manager]

SessionMgr --> CapMgr["Capability Manager (P25)"]

DTMS["DTMS (P4)"] --> SessionMgr

SessionMgr --> UI_Session_IES[Remote UI Session IES]

SessionMgr --> AuditLog["Audit Log (P13, P15, P17)"]

AuditLog --> DLT["Decentralized Ledger"]

end

subgraph "Remote UI Session IES (P1)"

direction LR

UI_Session_IES --> SecureUI["Secure UI Kernel (P11)"]

SecureUI --> InputSanitizer["Input Sanitizer (P11)"]

InputSanitizer --> RenderingEngine["Rendering Engine"]

RenderingEngine --> SecureChannel

UI_Session_IES --> SessionMonitor["Session Monitor (P2, P7)"]

SessionMonitor --> MSM["MSM (P2)"]

end

SecureChannel --> ClientApp

MSM --> SessionMgr

style Client fill:#ccf,stroke:#333,stroke-width:2px

Description for Diagram 16a: Remote UI Session

This diagram shows the architecture of the Secure Remote UI Session system. The client establishes a secure connection to the ASKA Hub, which manages authentication, authorization, and session parameters. The Hub then creates a dedicated IES instance to host the session, which is monitored for anomalies. The UI rendering and data transfer are securely managed, with input sanitization to prevent attacks.

Diagram 16b: Remote UI Integration

graph LR

subgraph ASKA Hub

direction LR

SessionMgr[Session Manager] --> DTMS["DTMS (P4)"]

SessionMgr --> CapMgr["Capability Manager (P25)"]

SessionMgr --> AuditLog["Audit Log (P13, P15, P17)"]

AuditLog --> DLT["Decentralized Ledger"]

SessionMgr --> MSM["MSM (P2)"]

SessionMgr --> PolicyEngine["Policy Engine (P4)"]

SessionMgr --> OnboardAI["Onboard AI Agent (if available)"]

end

subgraph "Remote UI Session IES (P1)"

UI_Session_IES[Remote UI Session IES] --> MSM

UI_Session_IES --> DTMS

UI_Session_IES --> InputSanitizer["Input Sanitizer (P11)"]

end

Description for Diagram 16b: Remote UI Integration

This diagram highlights the integration points of the Secure Remote UI Session system within ASKA. The Session Manager interacts with key ASKA components for authentication (MFA), authorization (Capabilities), auditing (Decentralized Ledger and MDATS), security monitoring (MSM), and policy enforcement (Policy Engine). The Onboard AI Agent, if present, further enhances security with real-time threat detection and response. The Remote UI Session IES receives policy updates and trust level information from the DTMS and passes session-related information (for monitoring and analysis) to the MSM. Input sanitization within the Remote UI Session IES prevents injection attacks.

Abbreviations

Abbreviation	Full Name	Short Description
AESDS	Automated Evolutionary Software Development System	AI-driven system for software evolution and secure deployment.
DTMS	Dynamic Trust Management System	Manages trust relationships and access control based on dynamic metrics.
IAMA	Isomorphic Architecture Monitoring & Adaptation	Monitors legacy systems and generates proactive security patches.
STN	Sovereign Trust Network	Isolated data plane for secure communication.
DTG	Dynamic Trust Gateway	Mediates communication between ATN and STN.
ATN	Authenticated Trust Network	Network with authenticated communication.
HESE-DAR	Hardware-Enforced Secure Encrypted Enclave for Data at Rest	Hardware-based encryption for data at rest.
IES	Isolated Execution Stack	Hardware-isolated execution environment.
ZKEE	Zero-Knowledge Execution Environment	Allows computations on encrypted data without revealing underlying information.
MSM	Hierarchical Security Mesh	Multi-layered security monitoring system.
Hub	ASKA Hub	Central control and management component.
CapMgr	Capability Manager	Manages capabilities for inter-IES communication.
PolicyEngine	Policy Engine	Enforces security policies.
ResourceMgr	Resource Manager	Manages resource allocation.
ChannelMgr	Channel Manager	Manages the multi-channel network.
Secure_Channel	Secure Channels	Secure communication channels.
External_Systems	External Systems	Systems outside of the ASKA environment.
Firewall	Firewall	Network firewall.
SecureUI	Secure UI Kernel	Secure and isolated user interface.
DLT	Decentralized Ledger	Distributed ledger for secure record-keeping.
MDATS	Multi-Dimensional Audit Trail System	Combines digital and physical audit trails.
UI_Integration	UI Integration Module	Facilitates communication between ASKA Hub and Secure UI.
SIZCF	Secure Inter-Zone Collaboration Framework	Enables secure collaboration between different zones.
AnomalyDetector	Anomaly Detector	Detects anomalies in system behavior.
LocalMSM	Local Security Mesh	Local security monitoring within an IES.
SecureStorage	Secure Storage	Secure storage for encrypted data.
GovernanceAI	Governance AI	AI-driven component for governance auditing and transparency.
QEAMS	Quantum-Entangled Auxiliary Memory System	Provides out-of-band integrity verification for data using quantum entanglement.
SAMS	Spatiotemporal Auxiliary Memory System	Provides out-of-band integrity verification using spatiotemporal metadata.
PR-SAMS	Passively Radiative Spatiotemporal Auxiliary Memory System	A low-power version of SAMS using passive radiative sensing.
QE-OTP	Quantum Entangled One Time Pad	A one time pad using quantum key exchange.
SHVS	Secure Hyper-Virtualization System	Enables secure and isolated execution environments within ASKA, supporting collaborative workloads.
CE-PCFS	Capability-Enhanced Packet-Carried Forwarding State	Provides fine-grained access control for communication between IES instances within ASKA.
SIBRA	Secure Inter-Broker Routing Algorithm	Ensures quality of service (QoS) for secure communication paths, particularly for bandwidth-intensive applications.
MPC	Secure Multi-Party Computation	Enables privacy-preserving collaborative machine learning across multiple ASKA instances.
CFI	Control-Flow Integrity	Protects the Secure UI Kernel against code injection attacks.
DPI	Deep Packet Inspection	Examines data packets to enforce security policies and detect malicious activity within the DTG.