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Management of AI Risk
Rating: 4.3 out of 5(21 ratings)
1,336 students

Management of AI Risk

Implementing the NIST AI RMF, AI Risk Management
Created byRichea Perry
Last updated 9/2025
English
English [Auto],

What you'll learn

  • How to practically and theoritically appy the NIST AI RMF.
  • How to use The NIST AI RMF Playbook for managing risks associated with AI Systems.
  • How to Frame Risk in association with AI systems.
  • A detail understanding of all the elements of the NIST AI RMF as presented by the NIST Publication.
  • Using Google SAIF to Conduct a Practical AI Risk Self Assessment

Course content

4 sections • 36 lectures • 4h 59m total length

  • The Importance of AI Risk Management3:46

    Learn why AI risk management is essential to ensure secure, ethical, and compliant AI deployments, addressing bias, explainability, data integrity, regulatory readiness, and responsible innovation.

  • 1-Course Introduction2:45

    Introduce AI risk management fundamentals through the NIST AI risk management framework, outlining how to build an organization-wide IT, IoT, and AI program to protect confidentiality, integrity, and availability.

  • 2-Intro the Framework outline9:16

    Explore the AI risk management framework (AI RMF) and its voluntary, open, transparent, multidisciplinary approach that embeds trustworthiness across design, development, use, and evaluation, drawing on 240+ organizations.

  • 3-AI RMF Executive Summary pt19:16

    Explore the ai rmf foundations, core functions, and audience, outlining governance, map, measure, and manage to enhance trustworthiness and manage ai risk across the life cycle.

  • 4-AI RMF Executive Summary pt211:24

    Explore the ai risk management framework by nest, with two parts and core functions governance, map, measure, and manage, designed to foster trustworthy ai across the life cycle.

  • 5-Part 1-Framing Risk pt114:42

    Explore the ai risk management framework, framing risk through scope, perspective, and communication, and learn to assess harms, mitigate negative impacts, and build trustworthy ai.

  • 6-Part 1-Framing Risk pt214:49

    Frame risk by examining measurement challenges across third party data, software, and hardware, emergent risk, and life cycle stages to support trustworthy AI.

  • 7-Part 1-Framing Risk pt39:16

    Frame and prioritize AI risk using the AI risk management framework and playbook, including residual risk, organizational integration, accountability, data privacy, and ISO guide 73 standards.

  • 8-Part 1-Audience17:22

    Identify and manage AI risk through the Nisq AI risk management framework's audience element, coordinating diverse AI actors across the life cycle with TEV processes and OECD classifications.

  • 9-Part 1-AI Risks and Trustworthiness pt117:22

    Explore the Nisq AI risk management framework, focusing on audience and life cycle dimensions, OECD classification, and TEV testing for trustworthy, responsible AI.

  • 10-Part 1-AI Risks and Trustworthiness pt27:02

    Explore what makes ai trustworthy by examining valid, reliable, accurate, robust, and bias-managed, generalizable performance, with validation, testing, and monitoring to minimize harm and ensure oversight.

  • 11-Part 1-AI Risks and Trustworthiness pt35:33

    Outline the safe element of the ai risk management framework, emphasizing responsible design and decision making, risk documentation, and early life-cycle safety practices.

  • 12-Part 1-AI Risks and Trustworthiness pt45:47

    Explain secure and resilient AI systems per ISO/IEC TS 5723 2022, emphasizing the CIA triad and integrating CSF and RMF into the AI system development life cycle.

  • 13-Part 1-AI Risks and Trustworthiness pt56:22

    Promote accountable and transparent AI by clarifying design decisions, training data provenance, and deployment practices, while aligning risk management with human–AI interaction and those interacting with the system.

  • 14-Part 1-AI Risks and Trustworthiness pt66:22

    Explore how accountability and transparency underpin trustworthy AI, covering life-cycle information from design and training data to deployment, human interaction, risk management, and governance.

  • 15-Part 1-AI Risks and Trustworthiness pt65:29

    Explore how privacy and the privacy framework guide enterprise artificial intelligence risk management and trustworthiness, shaping design, development, and deployment to protect anonymity, confidentiality, and user control.

  • 16-Part 1-AI Risks and Trustworthiness pt77:17

    Explore how fairness and harmful bias affect AI risk and trustworthiness, identify bias categories (systemic, computational, statistical, human cognitive), and leverage the AI risk management framework and NICE 1270 guidance.

  • 17-Part 1-Effectiveness of the AI RMF6:34

    Assess the effectiveness of the AI risk management framework by measuring trustworthiness improvements, defining metrics, and evaluating policies, processes, and outcomes to guide AI risk governance.

Requirements

  • A basic understanding of information security.
  • A willingness and committment to learn.

Description

Management of AI Risk-Implementing the NIST AI RMF

In an era where artificial intelligence (AI) is transforming industries and enhancing decision-making processes, the need for effective risk management has never been more critical. This course, "Management of AI Risk," provides a comprehensive exploration of the NIST AI-100-1 Risk Management Framework (RMF), equipping participants with the knowledge and skills necessary to identify, assess, and mitigate risks associated with AI systems.

By doing this course, you will receive an in-depth and thorough, word for word walk-through of all the information presented in the NIST AI RMF documentation.

You will also get an opportunity to perform a practical AI Risk assessment using Google SAIF.

The course covers the following areas as presented by the NIST AI-100-1 publication:


  1. Part 1: Foundational Information

    • Framing Risk

      • 1.1 Understanding and Addressing Risks, Impacts, and Harms

      • 1.2 Challenges for AI Risk Management

      • 1.2.2 Risk Tolerance

      • 1.2.3 Risk Prioritization

      • 1.2.4 Organizational Integration and Management of Risk


    • Audience

    • AI Risks and Trustworthiness

      • 1.2.4 Organizational Integration and Management of Risk

      • 3.2 Safe

      • 3.3 Secure and Resilient

      • 3.4 Accountable and Transparent

      • 3.5 Explainable and Interpretable

      • 3.6 Privacy-Enhanced

      • 3.7 Fair – with Harmful Bias Managed

    • Effectiveness of the AI RMF

  2. Part 2: Core and Profiles

    • AI RMF Core

      • 5.1 Govern

      • 5.2 Map

      • 5.3 Measure

      • 5.4 Manage

    • AI RMF Profiles

  3. Walk through of Appendix Documents

  4. Walk through of list of tables used in publication

  5. Conducting a Practical AI Risk Assessment

  6. Application and Implementation of the NIST AI RMF

    • Roadmap for Implementing NIST AI RMF

    • Playbook for Implementing NIST AI RMF

    • Case Scenarios for Implementing NIST AI RMF

    • Crosswalk Documents for the development of the NIST AI RMF

Who this course is for:

  • GRC Professionals
  • IS Risk Managers
  • Cybersecurity Analyst
  • AI Application developers
  • Those interesting in learning about cybersecurity frameworks
  • CISO

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