Chief Artificial Intelligence Officer and Generative Artificial Intelligence Leadership 5: AI Governance, Risk and Assurance
About This Course
This two-day course equips participants to design governance, risk, and assurance structures for responsible Artificial Intelligence (AI) transformation. It reframes governance as an accelerator, not a brake, helping organisations scale AI with trust, accountability, and evidence. Participants will examine Principled Performance and key frameworks including ISO/IEC 42001, ISO/IEC 5338, ISO/IEC 27701, the European Union (EU) AI Act, Singapore's Model AI Governance Framework for Generative AI, and the National Institute of Standards and Technology (NIST) AI Risk Management Framework. By the end of the course, they will learn how to classify AI risks, define human oversight, establish assurance checkpoints, manage incidents, and create audit-ready evidence for trustworthy AI scaling.
What You'll Learn
1. Explain how Artificial Intelligence (AI) governance, risk, and assurance support responsible innovation, trustworthy scaling, and Principled Performance.
2. Compare key AI governance and regulatory frameworks, including ISO/IEC 42001, ISO/IEC 5338, ISO/IEC 27701, the European Union (EU) AI Act, Singapore's Model AI Governance Frameworks and the National Institute of Standards and Technology (NIST) AI Risk Management Framework.
3. Classify AI risks using practical risk-based approaches, including responsible AI principles, EU AI Act risk categories, and NIST AI Risk Management Framework (RMF) concepts.
4. Design AI governance structures such as governance committees, accountability matrices, human oversight models, escalation pathways, incident response processes, and review checkpoints.
5. Apply lifecycle assurance thinking to define evidence, testing, monitoring, documentation, auditability, and assurance requirements across AI system stages.
6. Identify privacy, security, accountability, human oversight, transparency, and ethical issues that may arise in AI systems and Generative Artificial Intelligence (GenAI) -enabled workflows.
7. Develop an AI Governance, Risk and Assurance Framework that supports responsible AI adoption, evidence-based oversight, continuous monitoring, and trustworthy scaling.
Entry Requirements
1. Participants should preferably be a degree holder or a diploma holder with at least 3 years of relevant managerial or professional experience.
2. Completion of Courses 1 (Strategic AI Transformation) and 5 (AI Governance, Risk and Assurance) is recommended for foundational continuity before Course 6 (CAIO Playbook), although all is possible to be completed as standalone courses.