Designing an Audit Vault for AI Governance
How tamper-evident records, policy results, and verification workflows make AI calls reviewable.
The Audit Vault is AITracer's governance center. It gives compliance, product, engineering, and security teams a shared record of what happened during an AI execution.
What belongs in the vault
A useful AI execution record should capture:
- request and response metadata,
- model and workflow identifiers,
- input and output token counts,
- estimated request cost,
- latency and P95 health,
- policy results and high-risk heuristics,
- SHA-256 hashes for later verification.
Why verification matters
If a record changes after capture, the recalculated hash will no longer match the original proof. That turns trace logs into tamper-evident governance artifacts rather than best-effort notes.
The result is a workflow where teams can investigate an incident, export an audit window, and prove that the stored record still matches what was originally captured.