AI audits are accelerating. Regulators, customers, and internal assurance teams increasingly expect evidence that AI systems are controlled, monitored, and operated safely. Passing audits requires more than policies; it requires traceable evidence.
Build an evidence pack
Audit readiness improves when evidence is pre-assembled:
- Use-case register and risk tiering (see risk appetite).
- Model cards and lineage records (see model cards).
- Evaluation results and red team findings (see evaluation loops).
- Incident logs and remediation actions (see incident response).
Show traceability, not just documentation
Auditors will ask: can you trace an answer back to the versioned system that produced it? That requires:
- Logging prompt and policy versions.
- Logging retrieval source IDs for grounded answers.
- Logging tool authorization decisions for agent actions (see tool authorization).
Use governance artefacts as audit scaffolding
A small set of governance artefacts reduces audit burden (see governance artefacts). Keep them current, and audits become a review of evidence rather than a discovery exercise.
Compliance readiness is not a last-minute exercise. It is a byproduct of strong operating discipline.