AI audit evidence insights.
Articles, checklists, and operating guidance in this category.
What AI audit evidence should teams collect?
AI audit evidence should show what the system is, why it was approved, how it is controlled, what changed, and what happened in production.
How to prove AI governance controls are working
To prove AI governance controls work, teams need design evidence, operating evidence, exceptions, incidents, and review records tied to each system.
AI audit logs: what to capture for compliance
AI audit logs should capture system context, user action, model interaction, tool use, policy decisions, human review, and incident links without over-collecting sensitive content.
Evidence checklist for EU AI Act readiness
EU AI Act readiness evidence should connect system classification, risk management, documentation, oversight, monitoring, and change records.
How to prepare for an AI governance audit
Preparing for an AI governance audit means organizing system records, control evidence, risk decisions, change history, and open issues before the request arrives.