AI Risk Management & In-Depth Implementation (NIST AI RMF)

Gain expertise in AI governance, risk assessment, NIST AI RMF 1.0, trustworthy AI, and the execution of practical risk management strategies.

  • All Levels
  • 10 sections · 155 lessons · 17h 53m total
  • Online, self-paced

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AI Risk Management & In-Depth Implementation (NIST AI RMF)

What you'll learn

  • Utilize NIST AI RMF 1.0 in practical AI governance, AI risk management, risk assessment, and organizational decision-making.
  • Execute the GOVERN, MAP, MEASURE, and MANAGE Functions in a cohesive and ongoing AI risk management framework.
  • Define AI governance roles, policies, accountabilities, risk tolerance, stakeholder engagement, and oversight of third-party AI.
  • Illustrate AI systems, lifecycle context, stakeholders, impacts, dependencies, advantages, drawbacks, and risks to facilitate informed decision-making.
  • Assess and evaluate trustworthy AI through metrics, TEVV, evidence collection, monitoring, uncertainty factors, and assessments of human-AI performance.
  • Rank AI risks, choose appropriate risk treatments, document remaining risks, manage incidents, and create continuous AI risk monitoring processes.
  • Create and implement AI RMF Profiles, risk registers, impact assessments, measurement strategy, governance tools, and implementation documentation.
  • Incorporate NIST AI RMF ideas into generative AI, novel AI risks, third-party systems, and changing organizational AI applications.

Course outline

10 sections · 155 lessons · 17h 53m total

AI Risk and the NIST AI RMF 13 lessons · 1h 35m
  1. Getting Started: The Module Toolkits Free Preview On Udemy 05:57
  2. Why AI Needs Risk Management Free Preview On Udemy 08:02
  3. Understanding AI Systems 06:25
  4. How AI Risk Differs from Traditional Software Risk 06:49
  5. Risks, Impacts, and Harms 08:13
  6. The Challenges of Managing AI Risk 06:34
  7. Risk Tolerance and AI 06:43
  8. Risk Prioritization 05:40
  9. Integrating AI Risk with Enterprise Risk Management 07:10
  10. What Is the NIST AI RMF? 05:49
  11. Structure of AI RMF 1.0 07:06
  12. Govern, Map, Measure, and Mangage 06:29
  13. Module 1 Synthesis: One System, Revisited 14:12
AI Actors, Lifecycles, and Trustworthy AI 15 lessons · 1h 30m
  1. Who Is Responsible for AI Risk? Free Preview On Udemy 07:29
  2. AI Actors Across the Lifecycle 06:27
  3. Understanding the AI Lifecycle 06:01
  4. Human-AI Interaction 05:15
  5. Human Oversight and Decision Authority 05:13
  6. What Makes an AI System Trustworthy? 05:24
  7. Valid and Reliable 04:54
  8. Safe 04:37
  9. Secure and Resilient 05:32
  10. Accountable and Transparent 04:40
  11. Explainable and Interpretable 05:22
  12. Privacy-Enhanced 05:11
  13. Fair with Harmful Bias Managed 05:28
  14. Trustworthiness Tradeoffs 04:44
  15. Synthesis: LARA, Through All Seven 14:06
GOVERN: Creating the Conditions for AI Risk Management 18 lessons · 1h 28m
  1. Understanding the GOVERN Function Free Preview On Udemy 08:24
  2. AI Policies, Processes, Procedures, and Practices 05:28
  3. Legal and Regulatory Requirements 04:19
  4. Connecting Trustworthiness to Organizational Policy 04:09
  5. Risk Tolerance and Governance Decisions 04:00
  6. AI Risk Documentation and Transparency 03:50
  7. Roles, Responsibilities, and Accountability 03:35
  8. Independent Assessment and Separation of Duties 03:42
  9. Leadership Responsibility for AI Risk 03:41
  10. Building Diverse and Interdisciplinary Teams 03:40
  11. Defining Human-AI Roles 03:53
  12. Creating an AI Risk-Aware Culture 03:50
  13. Training and Workforce Competence 03:43
  14. Internal and External Risk Communication 03:51
  15. Stakeholder Feedback and Redress 03:45
  16. Third-Party AI and Supply-Chain Governance 03:42
  17. Contingency Planning for Third-Party AI 03:42
  18. GOVERN Applied Exercise 17:13
MAP: Understanding AI Risk in Context 20 lessons · 2h 5m
  1. Understanding the MAP Function Free Preview On Udemy 08:16
  2. Establishing the AI System Context 07:12
  3. Intended Purpose and Intended Use 05:39
  4. Mission and Organizational Alignment 05:17
  5. Legal, Regulatory, and Societal Context 06:32
  6. Identifying Stakeholders and Affected Communities 05:52
  7. Defining AI Tasks and Methods 05:51
  8. AI Knowledge Limits and System Limitations 04:54
  9. Establishing System Requirements 05:05
  10. Benefits, Costs, and Potential Harms 04:56
  11. Defining the Appropriate Scope of AI Use 05:13
  12. Operator Knowledge and Proficiency 04:59
  13. Human Oversight Requirements 05:06
  14. Data, Models, Software, and System Dependencies 04:47
  15. Third-Party AI Components 05:00
  16. Intellectual Property and Data Rights Risk 05:01
  17. Determining Likelihood and Magnitude of Impacts 05:22
  18. Making the Initial Go/No-Go Case 05:04
  19. Building an AI Risk Map 05:17
  20. MAP Applied Exercise 19:53
MEASURE: Assessing AI Risk 23 lessons · 2h 30m
  1. Understanding the MEASURE Function Free Preview On Udemy 08:15
  2. What Can---and Cannot---Be Measured 06:50
  3. Selecting AI Risk Metrics 06:24
  4. Quantitative, Qualitative, and Mixed Methods 06:25
  5. Testing, Evaluation, Verification, and Validation 06:32
  6. Testing Under Deployment-Like Conditions 06:47
  7. Independent Evaluation 06:12
  8. Validity and Reliability Measurement 06:11
  9. Safety Assessment 05:59
  10. Security and Resilience Assessment 05:49
  11. Transparency and Accountability Assessment 05:23
  12. Explainability and Interpretability Assessment 05:35
  13. Privacy Assessment 05:01
  14. Bias and Fairness Assessment 05:06
  15. Environmental and Broader Impact Measures 05:58
  16. Human-AI Performance Measurement 05:50
  17. Monitoring AI in Production 05:35
  18. Identifying Existing, Unanticipated, and Emergent Risks 05:53
  19. Feedback, Problems, and Appeals as Measurement Evidence 05:34
  20. Understanding Measurement Uncertainty and Limitations 05:57
  21. Evaluating Whether Measurement Works 05:43
  22. Documenting AI Risk Evidence 05:38
  23. MEASURE Applied Exercise 17:45
MANAGE: Making AI Risk Decisions 19 lessons · 2h 6m
  1. Understanding the MANAGE Function Free Preview On Udemy 08:34
  2. Does the AI System Still Meet Its Intended Purpose? 06:53
  3. Prioritizing AI Risks 06:27
  4. Developing Risk Treatment Plans 05:59
  5. Avoid, Mitigate, Transfer, or Accept 06:23
  6. Documenting Residual Risk 06:18
  7. Allocating Resources Based on Risk 06:24
  8. Considering Non-AI Alternatives 06:27
  9. Sustaining the Value of Deployed AI 05:40
  10. Managing Unknown and Emerging Risks 06:18
  11. Disengagement and Stop Conditions 06:02
  12. Third-Party Risk Management 05:58
  13. Post-Deployment Monitoring 06:01
  14. AI Incident Response and Recovery 05:32
  15. Communicating AI Incidents and Errors 04:56
  16. Change Management 05:39
  17. Decommissioning AI Systems 05:57
  18. Continuous Improvement 05:51
  19. MANAGE Applied Exercise 14:57
AI RMF Profiles and Practical Implementation 16 lessons · 2h 13m
  1. What Is an AI RMF Profile? Free Preview On Udemy 09:17
  2. Use-Case Profiles 07:41
  3. Current and Target Profiles 07:51
  4. Comparing Current and Target Profiles 07:18
  5. Building an AI RMF Profile 07:36
  6. Building an AI System Inventory 07:22
  7. Creating an AI Risk Register 07:36
  8. Risk Classification and Triage 07:08
  9. AI Approval and Review Gates 07:48
  10. AI Impact Assessments 07:56
  11. Documenting AI Risk Decisions 07:43
  12. Monitoring After Deployment 08:09
  13. Managing Changes to Models, Data, and Use Cases 08:53
  14. Audit Evidence and Assurance 08:30
  15. Building an Organizational AI RMF Workflow 08:10
  16. Implementation Activity: Build a Profile and Risk Register 13:32
The AI RMF Playbook and Framework Integration 10 lessons · 1h 28m
  1. What Is the NIST AI RMF Playbook? Free Preview On Udemy 09:27
  2. Using the Playbook Without Turning It Into a Checklist 07:58
  3. Selecting Playbook Actions Based on Risk 08:57
  4. Integrating AI RMF with Enterprise Risk Management 08:43
  5. AI RMF and Cybersecurity Risk Management 09:08
  6. AI RMF and Privacy Risk Management 08:16
  7. AI RMF and ISO/IEC 42001 09:06
  8. AI RMF and the EU AI Act 09:27
  9. Understanding Framework Crosswalks 08:27
  10. Avoiding Duplicate Governance Programs 08:16
Generative and Emerging AI Risk 16 lessons · 2h 27m
  1. Why Generative AI Changes the Risk Landscape Free Preview On Udemy 10:17
  2. Understanding the NIST Generative AI Profile 09:37
  3. Confabulation and Information Integrity 08:10
  4. Data Privacy and Sensitive Information 08:27
  5. Information Security Risks 09:06
  6. Prompt Injection and Manipulation 09:39
  7. Harmful and Biased Content 08:18
  8. Intellectual Property and Content Provenance 08:35
  9. Human Overreliance and Automation Bias 08:20
  10. Third-Party Foundation Models 09:01
  11. Synthetic Content and Authenticity 08:52
  12. From Generative AI to Agentic AI 08:45
  13. What Makes Agentic AI Different? 08:30
  14. Autonomy, Tools, Permissions, and Actions 08:38
  15. Human Intervention and Stop Mechanisms 09:05
  16. Applying GOVERN, MAP, MEASURE, and MANAGE to Emerging AI 13:35
Course Review and Practical Next Steps 5 lessons · 30m
  1. AI RMF as a Continuous Risk Management System 06:35
  2. Common AI Risk Management Mistakes 06:05
  3. Your First Steps Implementing AI RMF 06:27
  4. Final Assessment 07:18
  5. Course Conclusion 03:28

About this course

This course covers the utilization of artificial intelligence.

Go beyond a mere understanding of AI Risk Management through the NIST AI Risk Management Framework and discover how to implement AI risk management effectively.

This all-encompassing course offers a thorough analysis of the NIST AI Risk Management Framework (NIST AI RMF 1.0) together with its tangible applications to AI governance, AI risk management, AI risk assessment, trustworthy AI, and responsible AI.

It is crafted for professionals who seek a detailed exploration rather than a basic overview of the framework. You will learn how organizations can leverage the NIST AI RMF to recognize, assess, quantify, prioritize, manage, and supervise AI risks across the AI lifecycle, and how these actions integrate into a robust organizational AI governance structure.

Comprehend the NIST AI RMF from the ground up to its practical application.

Initially, you will investigate how NIST conceptualizes AI risk, its implications, and potential harms, along with the specific difficulties in gauging and managing AI risks. You will delve into AI participants and duties, the AI lifecycle, interactions between humans and AI, risk tolerance, prioritization of risks, and the traits of trustworthy AI.

Subsequently, you will engage deeply with the four Core Functions of the NIST AI RMF:

  • GOVERN — Develop AI governance frameworks, policies, accountabilities, risk tolerance levels, organizational culture, engage stakeholders, manage third-party oversight, and institute effective AI risk-management protocols.
  • MAP — Define the operational context of an AI system, identify stakeholders and communities impacted, comprehend intended objectives and effects, chart dependencies and risks, and ascertain what should be measured and managed.
  • MEASURE — Create significant methods for AI risk measurement and evaluation, encompassing metrics, testing, evaluation, verification and validation (TEVV), characteristics of trustworthiness, human-AI performance, uncertainty, production oversight, and decision-making evidence.
  • MANAGE — Rank AI risks, formulate risk treatment strategies, assess residual risk, make knowledgeable risk choices, set up monitoring and escalation systems, handle AI incidents and modifications, and decide when an AI system necessitates alteration, suspension, replacement, or decommissioning.

Rather than viewing GOVERN, MAP, MEASURE, and MANAGE as mere steps on a checklist or a one-time series of actions, you will learn how these Functions interact dynamically as AI systems, organizational goals, stakeholders, technologies, and risks evolve.

Transform the framework into actionable AI risk management.

Understanding the framework is just the beginning.

Throughout the course, module-specific toolkits reinforce your learning through practical worksheets, scenario workbooks, applicable exercises, field guides, and resources for implementation.

You will confront realistic AI risk-management scenarios involving:

  • AI system context and inventories
  • AI actors, stakeholders, and communities affected
  • AI governance responsibilities and roles
  • Risk tolerance and prioritization in AI
  • Risk maps and registers for AI
  • Assessment of AI impacts
  • Planning for AI measurement and TEVV
  • Evaluation of trustworthy AI
  • Vendor and third-party AI risks
  • Monitoring after deployment
  • Management of AI incidents
  • Triggers for change and reassessment
  • Decisions on risk treatment and residual risk
  • Profiles of Current and Target AI RMF
  • Analysis of gaps and planning for implementation

These resources are aimed at helping you transition from understanding AI risk-management principles to applying them in authentic organizational contexts.

Surpass the four Core Functions.

This course also investigates AI RMF Profiles and how Current and Target Profiles assist organizations in identifying gaps, setting priorities, and planning improvements.

You will learn to leverage the NIST AI RMF Playbook as a resource for implementation while maintaining a clear distinction between the framework's outcomes and its supporting implementation guidance.

Additionally, you will explore how NIST AI risk management concepts relate to generative AI and new AI technologies, addressing risks that might necessitate further governance, measurement, monitoring, and management considerations.

Enhance your practical capabilities in AI governance and risk management.

Upon concluding the course, you will have cultivated an interconnected understanding of how AI governance, risk assessment, measurement, evidence, risk treatment, monitoring, and ongoing improvement function together.

The aim is not merely to memorize NIST terminology, but to acquire the knowledge and practical judgment essential for actively engaging in an organization's AI risk management program and applying the NIST AI RMF to real-world AI systems and use cases.

There is no need to be an AI developer or data scientist. The course treats AI risk management as an organizational and socio-technical challenge, making it applicable to both technical and non-technical professionals tasked with governing, evaluating, acquiring, deploying, overseeing, or managing AI systems.

NIST AI RMF 1.0 is a voluntary framework for risk management and not a certification standard. This is independent educational training and is not associated with or endorsed by NIST.

Who this course is for

  • Professionals in AI governance, trustworthy AI, and responsible AI seeking a hands-on understanding of NIST AI RMF 1.0.
  • Risk management, GRC, compliance, and auditing professionals tasked with evaluating or managing organizational AI risk.
  • Cybersecurity, information security, privacy, and data governance specialists expanding their roles into AI governance.
  • Leaders in technology, IT management, AI program management, product management, and business leadership responsible for AI systems and applications.
  • Professionals in third-party risk, vendor management, procurement, and assurance roles responsible for assessing externally provided AI solutions.
  • Individuals new to AI governance or AI risk management who aim to gain both NIST AI RMF expertise and practical implementation capabilities.

AI Risk Management & In-Depth Implementation (NIST AI RMF)

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