AI Risk Management and AI Auditing TBIGS AI Governance Academy – Course 3

AI Risk Management and AI Auditing | TBIGS AI Governance Academy – Course 3 In this course, Dr. Adamou Musa explores the essential practices of AI Risk Management and AI Auditing, equipping professionals with the knowledge and tools needed to evaluate, monitor, and assure the safe, ethical, and effective use of AI systems. Participants learn how to identify and prioritize AI risks, assess governance structures, audit data quality, validate AI performance, test security controls, evaluate human oversight, manage third-party AI risks, and build evidence-based audit reports. The course integrates leading frameworks including NIST AI RMF, ISO/IEC 42001, GAO AI Accountability Framework, and IIA AI Auditing Guidance to provide a practical approach for AI assurance and governance. Course Objectives By the end of this course, participants will be able to: ✅ Understand the differences between AI risk management, validation, and auditing. ✅ Develop an AI risk register and assess inherent and residual risks. ✅ Define audit scope, criteria, objectives, and boundaries for AI systems. ✅ Evaluate AI governance, accountability, and control effectiveness. ✅ Audit AI data quality, privacy, security, and lineage. ✅ Validate AI model performance against intended business use. ✅ Assess AI security threats, resilience, and misuse risks. ✅ Review human oversight mechanisms and AI impact management. ✅ Evaluate third-party AI vendors and supplier risks. ✅ Monitor AI system drift, change management, and retirement processes. ✅ Rate audit findings using evidence and risk-based methodologies. ✅ Produce a complete, defensible AI audit workpaper and assurance report.