Training programme overview
Establish auditable AI governance: inventory systems, risk classification, policies, alignment with NIST AI RMF and regulatory readiness, documentation and incident reporting.
Inventory and mapping of AI systems in use and their data flows.
Risk classification frameworks and assessment of operational impacts.
Drafting and implementing auditable policies and procedures.
Practical application of the NIST AI RMF and regulatory readiness steps.
Documentation, logging mechanisms and incident management processes.
Training · Advanced
Courses included in this training programme
Responsible AI Foundations
Introduction to trustworthy AI principles: framework, core concepts and practical takeaways. The course breaks down governance, responsibilities and key considerations for designing, evaluating and overseeing systems that meet ethical and regulatory expectations.
4 lessons · 24 activities · 13 exercises · 3 videos
NIST AI RMF and GenAI Profile
Explores the NIST AI RMF and the GenAI Profile: governance, risk mapping, measurement and continuous management. The module identifies generative-AI-specific risks and outlines recommended controls and evidence practices.
4 lessons · 20 activities · 0 exercises · 3 videos
EU AI Act Readiness
Preparation for compliance with the EU AI Act regime: risk-based classification, transparency obligations and high-risk system requirements. The module also covers documentation, logging and expected human oversight practices.
4 lessons · 20 activities · 0 exercises · 3 videos
Audit and Incident Response
AI audit and incident response: system registry, vendor and model risk management, incident reporting and post-market monitoring. Also provides key elements for executive reporting and board-facing packs.
4 lessons · 20 activities · 0 exercises · 3 videos
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