An auditor should look for objective evidence that AI governance processes are defined, implemented, monitored, and improved across the AI lifecycle.
Auditing an AI management system is fundamentally about evidence, not aspirations. Practical evidence typically includes defined roles and responsibilities for AI governance, documented processes that control how AI is designed, deployed, and operated, and records showing those processes are followed in practice.
Strong AIMS evidence also includes how risks are identified and treated, how changes are controlled, and how oversight is maintained over time. The audit perspective focuses on whether requirements are translated into repeatable controls and whether the organization can demonstrate consistent execution through records, metrics, reviews, and corrective actions.
The most common audit gap is that AI policies exist, but operational records are missing. Evidence should show decisions, approvals, monitoring outputs, and improvement actions.
“Governance is real only when it produces evidence.”
Expert Trainer
Expert Trainer
An audit readiness review is worth doing when AI governance exists but evidence and consistency across teams are uncertain or untested.
An AIMS helps an organization govern how AI is planned, implemented, operated, and improved so AI initiatives remain controlled, consistent, and auditable.
A Statement of Applicability documents which controls are selected for the AIMS and why they apply, creating traceability between risks, requirements, and controls.
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