Anubis Security System
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The Anubis Security System is a post-quantum security framework designed for the TAMV MD-X4™ ecosystem. It implements the DEKATEOTL 11-layer protection model, providing a comprehensive defense-in-depth strategy that spans from digital identity verification to self-healing system architectures. Within the project, it serves as the primary sentinel for monitoring threat levels, blocking malicious activities, and maintaining system health through continuous auditing and quantum-resistant encryption.
The system is integrated deeply into the TAMV OS, communicating with the Unified API to provide real-time status updates and automated scanning. It functions as a singleton service that manages user trust scores, reputation scores, and dignity scores while monitoring for behavioral anomalies and synthetic media clones.
Sources: src/systems/AnubisSecuritySystem.ts:1-10, README_TAMV_COMPLETO.md:144-158, supabase/functions/tamv-unified-api/index.ts:175-186
DEKATEOTL 11-Layer Protection Model
The core of Anubis is the DEKATEOTL configuration, which consists of 11 distinct security layers. Each layer has specific sensitivity settings and operational goals ranging from identity analysis to distributed consensus.
Layer Definitions
| Layer ID | Name | Description | Sensitivity |
|---|---|---|---|
identity | ID-NVIDA Identity Analysis | Digital fingerprint verification and emotional profile matching | 0.9 |
behavior | Behavioral Pattern Analysis | AI-powered user behavior monitoring and anomaly detection | 0.85 |
quantum-anomaly | Quantum Anomaly Detection | Detection of quantum computing-based attack patterns | 0.95 |
post-quantum-crypto | Post-Quantum Cryptography | Quantum-resistant encryption (CRYSTALS-Kyber, Dilithium) | 1.0 |
emotional-biometric | Emotional Biometric | Unique emotional signature verification via EOCT analysis | 0.8 |
blockchain-reputation | Blockchain Reputation | Immutable reputation tracking on distributed ledger | 0.75 |
identity-bifurcation | Identity Bifurcation | Multi-factor identity verification preventing account splitting | 0.9 |
deepfake-detection | Deepfake Detection | AI-powered detection of synthetic media and voice cloning | 0.95 |
continuous-audit | Continuous Audit Trail | Real-time logging and audit of all system actions | 0.7 |
distributed-consensus | Distributed Consensus | Multi-node verification for critical operations | 0.85 |
self-healing | Self-Healing Architecture | Automatic threat mitigation and system recovery | 0.8 |
Sources: src/systems/AnubisSecuritySystem.ts:67-124, README_TAMV_COMPLETO.md:144-156
Operational Logic
The system maintains a continuous monitoring loop that triggers scans every 30 seconds. During a scan, each active layer evaluates potential threats based on its sensitivity threshold. If the probability of a threat exceeds the defined safety margin, a SecurityEvent is generated with a calculated severity level (Critical, High, Medium, Low, or None).
Sources: src/systems/AnubisSecuritySystem.ts:153-193
System Architecture and Components
Anubis is structured as a class-based singleton that coordinates between local state and persistent storage.
The diagram above illustrates the internal execution flow of the Anubis system, from initialization to continuous threat mitigation.
Sources: src/systems/AnubisSecuritySystem.ts:133-150, src/systems/AnubisSecuritySystem.ts:251-285
Data Structures
Security Metrics
Anubis tracks the real-time health of the ecosystem through several key indicators:
threatsBlocked: Total number of successfully mitigated threats.protectionLevel: Percentage representing the current efficiency of active shields.systemHealth: Overall health score affected by unresolved critical threats.quantumShield: Status of post-quantum cryptographic defenses.
Sources: src/systems/AnubisSecuritySystem.ts:40-48
User Security Profile
Each user in the TAMV ecosystem is assigned a profile that tracks their trustworthiness and risk:
trustScore: Base metric for user reliability.reputationScore: Immutable score derived from blockchain activity.dignityScore: Unique TAMV metric for civilizatory behavior.anomalies: List of detected suspicious patterns associated with the user.
Sources: src/systems/AnubisSecuritySystem.ts:50-60
Threat Detection and Mitigation
Threat detection is handled through behavioral pattern matching and anomaly detection algorithms.
Anomaly Detection Process
The system monitors user actions for specific suspicious patterns such as "rapid", "unusual", "bulk", or "automated" activity. If a user's trustScore falls below 30, the system increases the probability of flagging actions as anomalous.
Sources: src/systems/AnubisSecuritySystem.ts:327-360
The sequence above details how user actions are intercepted and verified by the behavior analysis layer.
Sources: src/systems/AnubisSecuritySystem.ts:327-353
Mitigation Strategies
- Blocking: Immediate resolution of a threat event, incrementing the
threatsBlockedmetric. - Self-Healing: An automated process that attempts to recover system state after a detected breach or corruption.
- Audit Trail: All actions are persisted to local storage and the Unified API via the MSR (Multiple Storage Registry).
Sources: src/systems/AnubisSecuritySystem.ts:251-285, supabase/functions/tamv-unified-api/index.ts:161-173
API Integration
The Anubis system exposes its status through the tamv-unified-api Sentinel module. This allows external tools and dashboards to query the current threat landscape.
Sentinel Endpoints
| Endpoint | Method | Description | Return Data |
|---|---|---|---|
/sentinel/status | GET | Returns global system security status | Threat level, active shields, last scan time |
/msr/log | POST | Logs a security event to the MSR | Event confirmation and evidence hash |
Sources: supabase/functions/tamv-unified-api/index.ts:175-186, supabase/functions/tamv-unified-api/index.ts:161-173
Database Schema (Supabase)
Security data is persisted in the security_scans and msr_events tables to ensure long-term auditability.
| Field | Type | Constraint | Description |
|---|---|---|---|
id | UUID | PRIMARY KEY | Unique event identifier |
user_id | UUID | REFERENCES auth.users | The user associated with the security event |
scan_type | TEXT | NOT NULL | Type of scan performed |
threat_level | TEXT | CHECK (none, low, medium, high, critical) | Severity of the detected threat |
threat_score | INTEGER | - | Numerical risk assessment |
timestamp | TIMESTAMPTZ | DEFAULT NOW() | When the event was recorded |
Sources: DEPLOYMENT_GUIDE.md:112-124
Conclusion
The Anubis Security System represents the defensive backbone of the TAMV MD-X4™ ecosystem. By implementing 11 layers of protection and maintaining a constant state of self-audit and automated healing, it ensures that civilizatory interaction remains secure against both traditional and post-quantum threats. Its integration with user reputation and dignity scores creates a security environment where behavior directly impacts accessibility and trust.
Sources: src/systems/AnubisSecuritySystem.ts:377-393, TASKS-TAMV-MODULAR.md:104-108