A practical guide to AI governance for audit, risk,
and compliance teams, covering governance gaps, AI inventories, operating
models, maturity assessments, audit approaches, and reporting.
Bedrijfsprestaties en ESG
10 september, 2026
Implementing AI governance: A practical guide for internal audit, risk, and compliance teams
Belangrijkste inzichten
- AI governance is an execution challenge, not a policy challenge. Many organizations are adopting AI faster than they can govern it. Effective governance requires operational processes, accountability, monitoring, and board oversight, not just written policies.
- An AI inventory is the foundation of governance. Organizations cannot govern AI systems they cannot identify. Maintaining a comprehensive inventory of AI tools, use cases, owners, and risk ratings is critical for risk management and audit planning.
- Successful governance relies on structured oversight throughout the AI lifecycle. Organizations should establish documented approval gates, clearly defined responsibilities, cross-functional governance committees, and continuous monitoring to ensure AI systems remain compliant and effective after deployment.
- Internal audit plays a critical role in assessing AI governance maturity. Auditors should evaluate governance controls, ownership, approval processes, monitoring activities, and board reporting while helping leadership identify and remediate governance gaps.
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