AI Risk Management Using NIST and ISO Frameworks

Planned learning outcomes

1. Explain core risk concepts as they apply to AI, including the AI lifecycle, inherent and residual risk, risk appetite and risk ownership
2. Apply the Govern, Map, Measure and Manage functions of the NIST AI Risk Management Framework to a real AI system
3. Integrate ISO/IEC 23894 and ISO 31000 processes with an organisation's existing risk and management systems
4. Identify AI risks including bias, privacy, cybersecurity, hallucination, unsafe decisions, vendor dependence and operational failure, and write clear risk statements
5. Analyse and evaluate AI risks using likelihood, consequence, exposure and impact assessment against documented criteria
6. Select technical, administrative, contractual and human oversight controls and design a monitoring regime with risk indicators
7. Produce a defensible AI risk register, impact assessment, treatment plan and management report