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NIST AI RMF: AI Governance & Risk Management Training
72 students
NIST AI RMF: AI Governance & Risk Management Training
NIST AI RMF: GOVERN, MAP, MEASURE & MANAGE : AI Risk & Governance Training
Created byVarinder K
Last updated 3/2026
English
What you'll learn
- Explain what a Risk Management Framework is and why AI systems require a dedicated framework separate from traditional IT risk approaches
- Describe the purpose, structure, and key principles of the NIST AI Risk Management Framework (AI RMF) and how it supports trustworthy AI development
- Identify the types of risks and harms that can arise from AI system deployment including bias, opacity, fairness failures, and unintended consequences
- Understand risk tolerance in the context of AI systems and how organizations set acceptable risk thresholds for AI use cases
- Apply risk prioritization techniques to determine which AI risks require immediate attention versus ongoing monitoring
- Explain the relationship between all 4 core functions : GOVERN, MAP, MEASURE, and MANAGE - and how they work together as an integrated framework
- Walk through all 6 sub-categories of the GOVERN function — covering AI governance policies, organizational accountability, culture, and oversight structures
- Apply the MAP function across all 5 sub-categories to categorize AI system context, identify stakeholder impacts, and define risk categories for specific use ca
- Use the MEASURE function across all 4 sub-categories to evaluate AI system trustworthiness characteristics — including fairness, explainability, robustness, and
- Implement the MANAGE function across all 4 sub-categories to respond to identified AI risks, handle residual risk, and build incident response into AI operation
- Analyze a real-world case study of the GOVERN function applied to AI driven recruitment system identifying governance failures and correct framework application
- Examine AI use in hiring processes as a high-risk AI deployment scenario and understand what risk controls and governance structures are required
- Understand NIST's broader efforts toward safe, secure, and trustworthy AI and how AI RMF connects to emerging regulations including the EU AI Act & ISO 42001
- Recognize the unique risk management challenges posed by AI systems including model drift, algorithmic bias, lack of explainability
- Synthesize all framework functions into a complete organizational AI risk management approach applicable to AI development, procurement, and deployment decision
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Course content
7 sections • 38 lectures • 3h 11m total length
Requirements
- No prior knowledge of NIST or the AI Risk Management Framework required as course begins from absolute basics
- Basic awareness of AI or machine learning concepts is helpful but not mandatory key terms are explained throughout
- Suitable for both technical roles (data scientists, ML engineers, IT security) and non-technical roles (governance, legal, policy, risk management)
Description
Foundations of AI Risk Management
What a Risk Management Framework is and why AI systems need one
Introduction to NIST's AI RMF — its purpose, structure, and key principles
NIST's broader efforts toward building safe, secure, and trustworthy AI
Types of risks and harms that arise from AI system deployment
AI RMF Core Concepts
Understanding risk tolerance in the context of AI systems
Key risk management challenges unique to AI — bias, opacity, drift, and scale
How to prioritize AI risks effectively across an organization
How the NIST AI RMF enables effective, structured risk management
GOVERN Function (6 Sub-categories)
What the GOVERN function covers and why it comes first
GOVERN 1 through GOVERN 6 — policies, accountability, culture, and oversight
Case Study: Implementing GOVERN in a real AI-driven recruitment system
How Governance connects to and enables MAP, MEASURE, and MANAGE
MAP Function (5 Sub-categories)
What the MAP function does — categorizing AI risks in context
MAP 1 through MAP 5 — identifying AI system context, impacts, and risk categories
How to map risks to specific AI use cases in your organization
MEASURE Function (4 Sub-categories)
What the MEASURE function covers — quantifying and analyzing AI risks
MEASURE 1 through MEASURE 4 — metrics, evaluation, and AI system testing
How to assess trustworthiness characteristics: fairness, explainability, robustness
MANAGE Function (4 Sub-categories)
What the MANAGE function covers — responding to and treating AI risks
MANAGE 1 through MANAGE 4 — risk response, residual risk, and incident handling
How to build ongoing risk management into your AI development lifecycle
Real-World Application
AI in hiring processes — risks, governance failures, and framework application
Key concepts synthesis — connecting all 4 functions into a complete picture
Best practices for organizations at any stage of AI adoption
Course Structure at a Glance
Section 1 — Risk Management Basics & NIST AI RMF Introduction
Section 2 — Foundational AI Risk Concepts: Tolerance, Prioritization & Challenges
Section 3 — GOVERN Function: All 6 sub-categories + Recruitment Case Study
Section 4 — MAP Function: All 5 sub-categories
Section 5 — MEASURE Function: All 4 sub-categories
Section 6 — MANAGE Function: All 4 sub-categories
Section 7 — Real-World Examples, Knowledge Check Quiz & Conclusion
Why This Matters Right Now
The EU AI Act is now in force wherein organizations need AI governance frameworks immediately
NIST AI RMF is the most widely referenced AI risk standard in the US and globally
Companies deploying AI in hiring, lending, healthcare, and public services face growing regulatory scrutiny
Demand for professionals with AI governance and risk management skills is growing faster than supply
The NIST AI RMF directly informs ISO 42001 the new AI management system standard
Who this course is for:
- AI Governance & Compliance Officers
- Data Scientists & ML Engineers
- CISOs & IT Managers
- Project Managers
- Policy & Legal Professionals