AI Governance and Risk Management for Internal Audit

Overview

Internal auditors play a critical role in ensuring ethical, compliant AI adoption by promoting strong governance, clear accountability, and lifecycle monitoring. Nick Ajmera, internal audit leader at an electrical utility, outlines board-level oversight, cross-functional committees, and unified policies aligned to frameworks like ISO/IEC 42001 and NIST. He highlights key risks’bias, drift, privacy, and explainability’and recommends KPIs, vendor controls, and monitoring tools to sustain effective AI governance.

Highlights

Prerequisites

Work experience in auditing, or an introductory course in auditing.

Objectives

• Identify key governance structures, roles, and policies required to support ethical and compliant AI adoption within internal audit and financial services organizations.
• Recognize significant AI-related risks'including bias, drift, privacy, and explainability'and their implications for audit, risk, and compliance functions.
• Distinguish how frameworks such as NIST AI RMF, COSO ERM, and ISO/IEC 42001 support the assessment, prioritization, and oversight of AI risks across the AI lifecycle.
• Select appropriate controls, KPIs, and monitoring practices to evaluate AI governance effectiveness, including third-party and post-implementation oversight.