AI Audit Log
AI Audit Log
Audit logs make LLM behavior traceable: what was asked, what sources were used, which versions ran, and what approvals exist.
Enterprise logs are protected, access-controlled, and retention-aware.
See also
Governance & Auditability Audit Trail Policy & Data Boundaries (Playbook)FAQ
What should be in an AI audit log?
Prompt/request metadata, versions, sources, routing, outcome signals, and approvals for high-risk actions.
How do we protect audit logs?
Access controls, encryption, retention policies, and redaction rules.
How does this help investigations?
It provides traceability and evidence for root cause analysis and compliance review.
What’s a common failure mode?
Logging too much sensitive data or logging too little to be useful.
What’s the first improvement?
Log versions + minimal metadata + outcome signals, then iterate with policy guidance.