Lilly Tech Systems
AI Risk Management
Master the NIST AI Risk Management Framework and learn to identify, assess, mitigate, and govern AI risks. Understand risk taxonomy, impact assessments, the MAP/MEASURE/MANAGE/GOVERN functions, and documentation requirements for responsible AI deployment.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why AI risk management matters, categories of AI risk, and the evolving regulatory landscape driving risk frameworks.
2. NIST AI RMF
Deep dive into the NIST AI Risk Management Framework, its four core functions: MAP, MEASURE, MANAGE, and GOVERN.
3. Risk Assessment
AI risk taxonomy, likelihood and impact analysis, risk scoring methodologies, and stakeholder impact mapping.
4. Risk Mitigation
Technical and organizational controls for AI risks, risk transfer strategies, acceptance criteria, and residual risk management.
5. Documentation
Risk registers, impact assessments, model cards, system documentation, and reporting requirements for AI governance.
6. Best Practices
Building a risk management culture, continuous monitoring, integrating risk into MLOps, and scaling risk processes.
What You'll Learn
By the end of this course, you'll be able to:
Apply NIST AI RMF
Implement the four core functions of the NIST AI Risk Management Framework within your organization's AI development lifecycle.
Assess AI Risks
Systematically identify, categorize, and score AI risks using established taxonomies and impact assessment methodologies.
Mitigate Risks
Design and implement technical and organizational controls to reduce AI risks to acceptable levels.
Document Governance
Create comprehensive risk documentation including risk registers, impact assessments, and governance reports for stakeholders.