How do I prepare for an AI governance audit and what evidence do auditors expect?

To prepare for an AI governance audit, an organization must establish and maintain a documented AI Management System (AIMS) and be able to provide evidence of its operational effectiveness and continuous improvement. Auditors expect to see clear documentation of policies, assigned responsibilities, risk assessments, and robust monitoring and logging capabilities.

Auditors will look for evidence of top management's commitment to AI governance, including a documented AI policy and clearly assigned roles, responsibilities, and authorities for AI governance, as outlined in ISO/IEC 42001 Cl.5 Leadership and NIST-GOVERN-2.1. This includes having a named risk owner / accountable executive for each deployed AI/agent system.

Organizations should demonstrate a structured approach to identifying and managing AI risks, including an AI risk assessment and AI impact assessment process (ISO/IEC 42001 Cl.6 Planning). This also involves understanding and documenting legal and regulatory requirements for AI, and maintaining an acceptable-use policy for AI/agent systems (NIST-GOVERN-1.1).

Evidence of operational control is crucial, meaning that AI risk and impact assessments are implemented, and AI systems operate under defined controls (ISO/IEC 42001 Cl.8 Operation). This includes policies defining how humans oversee AI, including override authority and the boundary of agent autonomy (NIST-GOVERN-3.2).

Auditors will also expect to see monitoring, measurement, analysis, and evaluation of the AIMS, along with internal audits and management reviews (ISO/IEC 42001 Cl.9 Performance evaluation). This requires immutable, queryable records that preserve decision context, not just transaction logs, to reconstruct the full sequence of events when something goes wrong. This also cross-maps to NIST-MEASURE-3.1 and OWASP LLM10 (abuse/consumption monitoring).

Finally, organizations must demonstrate a commitment to continual improvement and corrective action, addressing nonconformities and improving the AIMS over time (ISO/IEC 42001 Cl.10 Improvement). This includes treating AI risks as first-class engineering concerns through practices like secure-by-design and threat modeling (NIST-GOVERN-4.1).