AI Governance & Risk Management I

The vocabulary and mental model the rest of the certification rests on: how AI governance, risk management and compliance differ, risk versus harm, the actors in an AI value chain, the characteristics of trustworthy AI, and the types of governance instrument available.

How AI risk is managed in practice: the govern, map, measure and manage cycle, the identify-analyze-evaluate-treat process, characterizing and treating risk, and the concrete risks of the AI era - generative-model risks, agentic risks, security risks, model drift, and human oversight as a control.

The instruments that govern AI and how they relate to each other: the EU AI Act risk tiers and obligation types, ISO/IEC 42001 and its companion standards, the NIST AI Risk Management Framework, and the pattern emerging in other jurisdictions. Covered as structure and obligations rather than as legal advice.

Governance across the AI lifecycle: who is accountable at each stage, AI system impact assessment, technical documentation and provenance, human oversight of systems in production, incident response and post-market monitoring, third-party and procured AI, responsible retirement, and accountability in agentic workflows.

The human and organizational layer: the principles of responsible AI, fairness, transparency and privacy as duties owed to affected people, and how a working governance function is built and sustained through roles, policy, AI literacy and culture.