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AGRC Framework / Domain 7
07
NIST AI RMF, ISO 42001 (AIMS), GDPR Article 17, HIPAA, AICPA SOC 2 (Completeness & Accuracy).
Domain Objective
An auditor cannot audit a probability. To pass modern compliance frameworks in the agentic age, GRC teams must translate AI physics into deterministic, auditable controls. Sampling 50 transactions to guess the safety of 50 million is mathematically negligent. This domain enforces Continuous Attestation & Cryptographic Privacy, eliminating the fiduciary failure of relying on standard Data Processing Agreements (DPAs) and mutable, text-based application logs to shield the enterprise from tort liability.
Controls
PRV-7.1
100% Population Verification (Completeness & Accuracy)
The Rule — Control Statement
The enterprise shall mathematically map the output of the State-Tuple Ledger directly to external Completeness and Accuracy (C&A) audit requirements to enable 100% population verification.
The Why — Fiduciary Rationale
Relying on statistical sampling is actuarially void in an agentic environment. The enterprise must prove that no records of AI failure, drift, or hallucination were silently dropped due to latency or tampered with by administrators attempting to obscure an incident.
The How — Implementation Standard
The system must provide auditors with the recursive Merkle Root Hash of the State-Tuple Ledger, proving the cryptographic continuity of the entire transaction population.
The Proof — Continuous Attestation Evidence
Automated substantive testing scripts executing a continuous validation of the Merkle Chain sequence, returning a binary Pass/Fail for 100% of the ledger entries over the defined audit period.
PRV-7.2
GDPR Crypto-Shredding (Article 17 Compliance)
The Rule — Control Statement
The logging architecture shall enforce "Peppered Hashing" to satisfy the GDPR "Right to be Forgotten" without breaking the immutable chain of custody.
The Why — Fiduciary Rationale
Storing plaintext PII in an immutable WORM (Write Once, Read Many) ledger violates GDPR/CCPA; however, deleting a row breaks the cryptographic chain required for legal defense.
The How — Implementation Standard
PII payloads must be stored as a hash keyed with a unique, user-specific cryptographic salt (the "Pepper") managed in a centralized Key Management Service (KMS). To delete the user's data, the system deletes the specific salt from the KMS, rendering the ledger entry mathematically unrecoverable while preserving the structural audit trail of the agent's action.
The Proof — Continuous Attestation Evidence
Data Subject Access Request (DSAR) execution logs cross-referenced with KMS deletion records, proving the irreversible cryptographic destruction of the targeted payload hash.
PRV-7.3
Pre-Inference PHI/PII Redaction (HIPAA/PCI)
The Rule — Control Statement
Protected Health Information (PHI), Personally Identifiable Information (PII), and PCI data must be deterministically masked, redacted, or tokenized prior to leaving the enterprise boundary.
The Why — Fiduciary Rationale
Relying exclusively on a third-party Model Provider's qualitative "Zero Data Retention" Business Associate Agreement (BAA) is structurally insufficient. The enterprise cannot leak what it does not send.
The How — Implementation Standard
The Governor (or a dedicated DLP sidecar) must intercept the prompt payload, execute deterministic redaction of protected entities, and replace them with synthetic tokens (e.g., [SSN_REDACTED]) before routing to a third-party inference API.
The Proof — Continuous Attestation Evidence
Egress payload packet-captures and Governor inspection logs demonstrating the absolute absence of plaintext, un-tokenized PHI/PII prior to hitting the external API gateway.
PRV-7.4
Algorithmic Fairness Audits (EEOC / ISO 23894)
The Rule — Control Statement
Algorithmic bias and disparate impact must be treated as strict vector space violations and managed deterministically.
The Why — Fiduciary Rationale
Relegating algorithmic bias to a "qualitative ethics" discussion exposes the firm to severe Employment Practices Liability (EPLI) and EEOC enforcement. Bias must be blocked at the point of generation.
The How — Implementation Standard
Regulatory fairness requirements must be mapped directly to the Test-Driven Governance (TDG) suite. The system must automatically prove that the vector distance between rejection decisions (e.g., HR screening, loan approvals) and protected-class variables exceeds predefined exclusion radii.
The Proof — Continuous Attestation Evidence
Automated Disparate Impact reports generated by the TDG suite, mathematically proving correlation coefficients between negative outcomes and protected classes remain geometrically separated and below regulatory thresholds.
PRV-7.5
The Reporting Exemption (Incident vs. Function Classification)
The Rule — Control Statement
The enterprise Risk Register shall formally differentiate between an "Attempted Hallucination" (a prevented error) and a "System Breach" (an executed error) based on the point of intervention.
The Why — Fiduciary Rationale
If every malicious intent generated by an agent is classified as a "security incident," the enterprise will drown in false-positive regulatory breach disclosures.
The How — Implementation Standard
Because the Governor intercepts malicious intent and converts it into a "No-Op" or safe equivalent prior to execution, GRC policies must formally classify these intercepted events as "Successful Control Functions" (Operating Effectiveness), not "Security Incidents" (Material Weakness).
The Proof — Continuous Attestation Evidence
Incident response playbooks and SIEM logic demonstrating the automated routing and classification of GOVERNOR_BLOCKED or GOVERNOR_CORRECTED events as Routine Security Telemetry rather than mandatory breach disclosure queues.
PRV-7.6
Subrogation-Ready Forensic Exports
The Rule — Control Statement
The State-Tuple Ledger must support the export of incident logs in recognized, court-admissible forensic formats designed for subrogation.
The Why — Fiduciary Rationale
If a Model Provider's defect (rather than user prompt negligence) causes a financial loss, the enterprise's cyber insurer will require mathematical proof to subrogate (sue the provider) for product liability.
The How — Implementation Standard
Exports must utilize structurally signed JSON or STIX/TAXII formats that cryptographically decouple the "Input Intent" from the "Model Output Defect" to prove vendor fault to a judicial standard.
The Proof — Continuous Attestation Evidence
System-generated forensic export packages bearing cryptographic signatures from the local Trusted Execution Environment (TEE) or HSM, ensuring absolute admissibility and non-repudiation in civil litigation.
PRV-7.7
Automated Legal Hold Routing (Spoliation Defense)
The Rule — Control Statement
Agent-generated "scratchpads," "chains of thought" (CoT), and multi-step reasoning outputs shall be classified as discoverable corporate records.
The Why — Fiduciary Rationale
As documented in GTG-1002, agents autonomously generate markdown files detailing their actions and vulnerabilities. Allowing routine context-flushing to destroy these internal reasoning logs during an active investigation constitutes Spoliation of Evidence.
The How — Implementation Standard
GRC and eDiscovery systems must automatically route flagged agent reasoning logs into immutable Legal Hold storage (WORM) upon the activation of a litigation trigger, overriding standard automated "Context Window Flushing."
The Proof — Continuous Attestation Evidence
Configurations within the data lifecycle management system proving that active eDiscovery flags successfully suspend automated context-deletion scripts for designated users/NHIs.
PRV-7.8
Geolocation & Sovereign API Routing
The Rule — Control Statement
Autonomous routing and orchestration layers shall dynamically respect international data residency laws (e.g., GDPR cross-border transfer laws).
The Why — Fiduciary Rationale
An agent optimizing for speed or cost must not autonomously decide to route an EU citizen's data to a US-based inference endpoint, thereby triggering an immediate regulatory violation.
The How — Implementation Standard
The API Gateway must physically bar agents processing tagged sovereign data from utilizing geographically non-compliant inference endpoints, overriding any latency-optimization logic.
The Proof — Continuous Attestation Evidence
API routing logs correlated with data-classification tags demonstrating 100% adherence to geolocation egress restrictions (e.g., Region:EU traffic routed exclusively to eu-central-1).
PRV-7.9
Third-Party AI Risk Management (TPRM) Standards
The Rule — Control Statement
Enterprise Vendor Risk Questionnaires shall mandate cryptographic proof of Batch-Invariance and "Glass Box" logging for all third-party SaaS vendors integrating AI into their products.
The Why — Fiduciary Rationale
A secure internal perimeter is useless if the enterprise's third-party payroll provider introduces an ungoverned agent into the data supply chain. A "Black Box" AI vendor is an uninsurable node.
The How — Implementation Standard
If a SaaS vendor cannot provide a cryptographic State-Tuple architecture for their AI features, they must fail the TPRM assessment and be barred from processing sensitive corporate data.
The Proof — Continuous Attestation Evidence
Executed Vendor Risk Assessments explicitly tracking the inclusion of deterministic governance capabilities as a hard Go/No-Go gate for vendor onboarding.
PRV-7.10
The Cryptographic User Feedback Loop (Assetizing Correction)
The Rule — Control Statement
Human feedback mechanisms (e.g., "Thumbs Down" or "Stop" buttons) shall be treated as cryptographic evidence and integrated directly into the Continuous Development lifecycle.
The Why — Fiduciary Rationale
Legacy systems treat human correction as a UI feature that routes to a Jira support ticket. In the agentic era, human correction is a high-value asset (live gradient update) that must instantly update the defense mechanism.
The How — Implementation Standard
When a user flags an agentic output, the feedback must be cryptographically signed using the human's IAM session token, appended to the State-Tuple Ledger, and automatically routed to the Teleological Data Generation (TDG) pipeline to retrain the Governor's LoRAs.
The Proof — Continuous Attestation Evidence
Audit trails tracing a user's UI feedback event directly through to a compiled, version-controlled TDG negative-data test case, closing the loop.
PRV-7.11
Automated "System Card" Generation (The Dynamic Registry)
The Rule — Control Statement
The Cognitive Development Life Cycle (CDLC) pipeline shall automatically generate a live, mathematically proven "System Card" outlining the agent's current boundaries.
The Why — Fiduciary Rationale
A static PDF transparency report is legally obsolete the millisecond a model's weights or Governor policies update. Regulators require real-time visibility into the agent's constraints.
The How — Implementation Standard
The TDG regression suite must output a live endpoint that transparently displays the agent's current geometric boundaries, exclusion radii, and real-time pass/fail metrics, without exposing underlying proprietary intellectual property.
The Proof — Continuous Attestation Evidence
Publicly or auditor-accessible dynamic System Card dashboards directly tethered to, and updating synchronously with, the latest CDLC pipeline build hashes.
PRV-7.12
Automated Right-to-Intervene (The "Emergency Brake" Ledgering)
The Rule — Control Statement
Emergency stop mechanisms utilized by human operators must mathematically log the exact cognitive state of the agent at the millisecond of severance.
The Why — Fiduciary Rationale
Proves to regulators (e.g., under the EU AI Act) and insurers that human oversight possesses absolute, low-latency preemption over machine execution, and preserves the forensic data of what the agent was attempting to do when halted.
The How — Implementation Standard
The "Halt" command must sever the API connections at the hypervisor or orchestration level—bypassing the agent's software logic entirely—and execute a cryptographically signed dump of the active vector state, queued MCP tool calls, and context window at the exact millisecond of intervention.
The Proof — Continuous Attestation Evidence
Incident response logs showing complete state-tuple RAM/Context dumps timestamped identically to the human-initiated "kill switch" event.
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