What topics are covered across the four course days?
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.
Related Information
- Day 1: AI risk management foundations.
- Day 2: context, governance, identification.
- Day 3: analysis, evaluation, treatment.
- Day 4: monitoring, reporting, awareness, improvement.
- Course uses exercises and quizzes aligned to exam style.
Expert Insight
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.”
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View courseRelated Answers
How is the PECB Lead AI Risk Manager exam structured?
The exam is domain-based, covering AI risk concepts and regulations, governance, identification and analysis, evaluation/treatment/monitoring, and organizational learning and performance improvement.
byAlexis HIRSCHHORN
What is AI risk management in practice?
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.
byHenri HAENNI
How do NIST AI RMF and the EU AI Act relate to the course?
They provide recognized structures for governing AI risk, defining controls, and demonstrating compliance and ethical AI use in organizational settings.
byTania POSTIL
More from Lead AI Risk Manager
How do I identify AI-specific risks like bias, drift, and adversarial threats?
Identify AI risks through lifecycle analysis: data risks (bias, quality), model risks (drift, overfitting), deployment risks (adversarial attacks, misuse), and operational risks (feedback loops, unintended impacts).
What makes AI risk management different from traditional IT risk?
AI risks are dynamic, probabilistic, and context-dependent. Unlike static IT systems, AI models degrade over time, produce unexpected outputs, and fail in ways difficult to predict or test comprehensively.
How is the PECB Lead AI Risk Manager exam structured?
The exam is domain-based, covering AI risk concepts and regulations, governance, identification and analysis, evaluation/treatment/monitoring, and organizational learning and performance improvement.
Who should take the Lead AI Risk Manager course?
It's designed for risk owners, IT/security teams, data and AI engineers, consultants, legal/ethical advisors, leaders overseeing AI deployments, and executives needing strategic oversight of AI risk.
Further reading
ISO 27001 to ISO 42001: How to Extend Your ISMS into an AI Management System (2026)
ISO 27001 gives you a head start on ISO 42001, not a free pass. Here is what carries over, what is new, and how to extend your ISMS to an AIMS, step by step.
EU AI Act: The Complete Compliance Guide for the August 2026 Deadline
Regulation (EU) 2024/1689 is the EU's first comprehensive risk-based horizontal AI law, applying in stages from 2025 to 2027 (with Article 6(1) deferred to 2027). Complete guide.
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