An AI audit answers the question your board, regulator, or largest customer will eventually ask: can you demonstrate that your AI systems are governed, documented, and behaving within stated limits? It is an assurance exercise rather than a technical test, and it produces evidence you can hand over.
I audit AI governance structures, model inventories and ownership, approval and change-management workflows, documentation such as model and system cards, data provenance and licensing, human-oversight and escalation paths, bias and fairness evaluation practices, incident-response readiness for AI failures, vendor and sub-processor due diligence, and the transparency notices shown to users.
Controls are assessed against ISO/IEC 42001, the NIST AI Risk Management Framework, EU AI Act obligations where in scope, and India's DPDP Act requirements for automated processing of personal data. The deliverable is a formal audit report with findings, severity, evidence gaps, and a corrective-action plan — plus a reusable control set so subsequent audits become routine rather than disruptive.
Why it matters
- Regulatory pressure on AI is rising quickly across jurisdictions
- Documented governance is now a procurement requirement in enterprise deals
- Surfaces shadow AI usage and ungoverned models across the business
- Creates a repeatable control set that shortens every future audit
My approach
How the engagement runs
Typical engagement: AI inventory and scoping, control mapping to ISO/IEC 42001 and NIST AI RMF, evidence review and stakeholder interviews, formal audit report with findings, then a corrective-action plan and readiness re-review.