How to Build an AI Governance Evidence Repository

Structuring Runtime Policy Enforcement and Agent Activity Records

Runtime enforcement records are the category most likely to be incomplete if evidence is gathered manually. At minimum, each enforcement record should capture agent identity, the requested action, the specific policy rule that was matched, the decision outcome, and a timestamp.

Agent activity logs should reference the permission grant that authorized a given tool call, so that a reviewer can trace a single action back through the specific grant and policy version that made it possible. This is what allows a governance team to answer not just "what did the agent do" but "what authorized it to do that."

Because these records are generated automatically at the point of decision, they reduce dependence on after-the-fact reconstruction, which aligns with the continuous-monitoring intent behind NIST AI RMF's Manage function and the automatic-logging obligation in EU AI Act Article 12 for high-risk systems. Trussed AI's runtime policy enforcement and agent identity capabilities generate this class of record directly at the point of tool invocation, rather than requiring it to be assembled from application logs after the fact.

Retention, Versioning, and Access Control

• Set retention per category, not uniformly: EU AI Act guidance ties retention to intended purpose rather than a single fixed period, so policy records, tool-call logs, and incident records may warrant different retention windows.
• Separate immutable from mutable records: Runtime enforcement and tool-call logs should be append-only; policy documents and risk assessments are expected to change and need standard version control instead.
• Use consistent cross-reference identifiers: Agent ID, session ID, and policy version ID should appear across all evidence categories so a timeline can be reconstructed from any starting point.
• Apply least-privilege access to the repository itself: Access to governance evidence should be logged and restricted, consistent with audit and accountability control expectations for information systems generally.
• Record approval history for every policy change: Who approved a change and when is itself evidence, needed to satisfy documented-information requirements under management-system standards.
• Test retrieval before you need it: Periodic internal audit simulations confirm evidence is complete and retrievable, rather than discovering gaps during an actual regulatory request.

Reconciling Framework Requirements Across Jurisdictions

Organizations operating in multiple jurisdictions face a structural tension: NIST AI RMF is voluntary and defines functional objectives without mandating a technical schema, while EU AI Act Article 12 creates a binding logging obligation for high-risk AI systems, requiring automatic event recording and lifecycle traceability. ISO/IEC 42001 sits between the two, framing evidence as documented information tied to an operating management system rather than as standalone runtime logs.

A repository designed to satisfy all three treats governance evidence and technical runtime evidence as linked rather than separate: policy and risk-assessment documentation on one side, system-generated enforcement and activity logs on the other, connected through shared identifiers. NIST AI 600-1 adds generative-AI-specific expectations, such as documenting content provenance and incident disclosure practices, that extend this same model rather than replacing it. The practical implication is that a repository built for one framework's objectives will generally satisfy the others, provided the underlying evidence categories and cross-referencing are structured consistently from the start.