Implementing AI governance: A practical guide for internal audit, risk, and compliance teams

Key Takeaways

• 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.