Day 1 covers AI risk fundamentals; Day 2 covers context, governance, and risk identification; Day 3 covers analysis, evaluation, and treatment; Day 4 covers monitoring, reporting, awareness, and continual improvement.
Day 1 introduces AI risk management concepts and why AI creates distinctive risk categories beyond traditional risk programs.
Day 2 focuses on organizational context, AI risk governance, and risk identification—covering bias, security vulnerabilities, transparency limits, ethical concerns, and compliance exposure.
Day 3 covers analysis, evaluation, and treatment: prioritization, risk acceptance criteria, mitigation planning, and incident response measures.
Day 4 addresses monitoring and reporting, training and awareness, and optimizing AI risk performance through organizational learning and continual improvement.
Track outputs as you learn: a risk register, control plan, monitoring metrics, and escalation paths. Those artifacts map directly to exam domains and real work.
“The course follows a full risk lifecycle: identify, treat, monitor, improve.”
Expert Trainer
Expert Trainer
The exam is domain-based, covering AI risk concepts and regulations, governance, identification and analysis, evaluation/treatment/monitoring, and organizational learning and performance improvement.
AI risk management is the structured way to identify, assess, treat, and monitor AI risks—such as bias, security threats, transparency gaps, and compliance exposure—through governance, controls, and evidence.
In practice, it means building a structured cybersecurity program with clear ownership, risk-based controls, and repeatable processes for prevention, response, and improvement.
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