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AGRC Framework / Domain 6
06
OWASP ASI06 (Supply Chain Vulnerabilities), SOC 2 CC8 (Change Management).
Domain Objective
In Agentic AI, the system prompt, the vector embeddings, and the tool schemas constitute the codebase. AI degrades differently than traditional software; it suffers from "Stochastic Regression"—where fixing one behavior spontaneously breaks another. The Software Development Life Cycle (SDLC) must evolve into the Cognitive Development Life Cycle (CDLC), enforcing Threat-In, Threat-Out (TITO) CI/CD Pipelines.
Controls
DEV-6.1
The AI Software Bill of Materials (AI-SBOM)
The Rule — Control Statement
The enterprise shall maintain a continuous, real-time cryptographic ledger of all foundational models, Hot-Swappable LoRA adapters, embedding models, third-party MCP servers, and orchestration libraries in the production path.
The Why — Fiduciary Rationale
You cannot patch what you cannot see. The AI supply chain is highly fragmented; an unmapped dependency on a deprecated open-source orchestration library introduces massive zero-day vulnerability.
The How — Implementation Standard
Automated SBOM generation must be triggered on every container build and policy deployment.
The Proof — Continuous Attestation Evidence
Cryptographically signed AI-SBOM artifacts linked directly to the active production deployment manifests.
DEV-6.2
Model Weight Signature Verification
The Rule — Control Statement
Cryptographic signature verification shall be strictly enforced on all downloaded open-source or commercial model weights prior to instantiation.
The Why — Fiduciary Rationale
Prevents the introduction of "sleeper agents"—model weights that have been subtly poisoned by an upstream attacker on platforms like Hugging Face to fail under specific cryptographic triggers.
The How — Implementation Standard
The deployment pipeline must run a SHA-256 validation against the original creator's published hash before the model is permitted to load into GPU memory.
The Proof — Continuous Attestation Evidence
CI/CD pipeline logs demonstrating successful hash-matching halts/approvals for all .safetensors or .bin files prior to deployment.
DEV-6.3
Regression Persistence (The TDG Gauntlet)
The Rule — Control Statement
No Policy LoRA, System Prompt, or MCP tool schema shall be deployed to production without automatically executing and passing 100% of the Test-Driven Governance (TDG) regression suite.
The Why — Fiduciary Rationale
Tweaking a system prompt to fix a refund error may inadvertently disable a PII redaction filter (Stochastic Regression). Manual testing is insufficient to catch these micro-regressions in high-dimensional vector space.
The How — Implementation Standard
The CI/CD pipeline must run thousands of historic "Negative Data" vectors against the new build in a staging environment. If the build fails even one historic safety block, the deployment must automatically abort.
The Proof — Continuous Attestation Evidence
Automated CI/CD test run artifacts demonstrating a 100% pass rate on the TDG regression suite as a hard, un-bypassable prerequisite for the production merge.
DEV-6.4
Architectural Segregation of Duties (SoD)
The Rule — Control Statement
The identity (person or pipeline) that builds and deploys the Agent's Application Logic shall not possess the cryptographic permissions to configure or alter the Governor's Policy Manifold.
The Why — Fiduciary Rationale
Traditional SoD must be enforced. An application developer under pressure to increase the agent's speed or utility will be incentivized to unilaterally "turn off" safety constraints if they hold the keys to both.
The How — Implementation Standard
Role-Based Access Control (RBAC) at the CI/CD level must separate "The Creator" (Agent Logic) from "The Auditor" (Governance Logic).
The Proof — Continuous Attestation Evidence
IAM policy documents and Git commit histories proving mutually exclusive commit privileges for Agent repositories vs. Governor repositories.
DEV-6.5
Dynamic Artifact Hash Verification (Anti-JIT Malware)
The Rule — Control Statement
Any execution script or code artifact autonomously generated by an agent must be cryptographically hashed and evaluated by the Governor prior to execution on the host.
The Why — Fiduciary Rationale
Standard SBOMs track static dependencies, which is completely useless against Just-in-Time (JIT) polymorphic malware generated mid-session by the agent itself.
The How — Implementation Standard
The CDLC enforces a "Runtime Execution Hash" policy. Generated code is intercepted, semantically analyzed by the Governor for malicious intent, and hashed. Only approved hashes are allowed to execute in the sandbox.
The Proof — Continuous Attestation Evidence
Execution sandbox logs showing blocked execution attempts for dynamically generated scripts lacking a Governor-signed approval hash.
DEV-6.6
Training Data Immutability (Poisoning Defense)
The Rule — Control Statement
The "Negative Data" ingested from the Ledger to train the Governor's LoRAs must be cryptographically hashed upon capture and verified continuously through the build pipeline.
The Why — Fiduciary Rationale
It is insufficient to verify just the model weights; the enterprise must verify the data used to train the Governor. A malicious insider could subtly alter the failure logs to create a blind spot in the resulting Governor update.
The How — Implementation Standard
The build pipeline must abort if the hash of the training dataset does not match the pristine capture hash stored in the State-Tuple Ledger.
The Proof — Continuous Attestation Evidence
Data-pipeline logs showing hash validation between the immutable WORM storage of the Ledger and the active Training Environment instance.
DEV-6.7
Glass Box Telemetry Sabotage Defense
The Rule — Control Statement
The State-Tuple Ledger and Glass Box telemetry mechanisms shall run on an entirely separate network plane and distinct compute instance from the agent container.
The Why — Fiduciary Rationale
If an agent is successfully compromised, its immediate autonomous objective is to "blind" the security team by attacking, delaying, or falsifying the logging pipeline.
The How — Implementation Standard
The agent container must operate with absolute zero privileges to read, modify, delay, or view the status of the telemetry sidecar.
The Proof — Continuous Attestation Evidence
Cloud IAM roles and K8s Network Policies proving the agent namespace has no write access to the logging namespace or the underlying WORM storage buckets.
DEV-6.8
Intrinsic Capability Benchmarking (The "Radioactive Core" Rule)
The Rule — Control Statement
Before any foundational model (open-weight or commercial API) is permitted into the enterprise environment, the CDLC pipeline must mathematically benchmark its intrinsic, latent capabilities.
The Why — Fiduciary Rationale
The Governor provides a deterministic shield, but the enterprise must not knowingly import unchecked "radioactive" cores. If a model's latent knowledge graph allows for the seamless generation of zero-day exploits or CBRN synthesis, it represents an unacceptable baseline risk regardless of the overlaying governance.
The How — Implementation Standard
Automated intrinsic capability evaluations must be run against raw, ungoverned models. If the capability exceeds predefined enterprise safety thresholds, the model is physically barred from instantiation.
The Proof — Continuous Attestation Evidence
Pre-deployment capability evaluation reports signed by the Risk Office showing CBRN and Cyber intrinsic scores falling below the defined maximum threshold.
DEV-6.9
Algorithmic Degradation Triggers (API Monitoring)
The Rule — Control Statement
For models consumed via third-party APIs, the CDLC pipeline shall execute automated daily capability benchmarks. A sudden spike in latent restricted capabilities must trigger an automated architectural response.
The Why — Fiduciary Rationale
Vendor models silently update their weights and prompts via API. An API endpoint that was safe on Monday could receive an unannounced update on Tuesday that dramatically increases its propensity to generate malicious code or leak data.
The How — Implementation Standard
The pipeline must continuously monitor the intrinsic safety baseline. A threshold breach must automatically sever the connection to that specific model version, fail back to a previous "Known Good" deployment, and issue an immediate alert to the Chief Risk Officer.
The Proof — Continuous Attestation Evidence
Daily automated benchmark trend logs, coupled with incident response records demonstrating automated traffic-shifting (failback) upon threshold degradation.
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