NIST AI RMF Playbook

The NIST AI Risk Management Framework (AI RMF) Playbook provides a comprehensive guide for organizations to assess and manage risks associated with Artificial Intelligence (AI) systems. It is structured around four key functions: Govern, Map, Measure, and Manage. Each function encompasses specific categories and subcategories, offering detailed suggestions to achieve desired outcomes.

1. Govern

This function focuses on establishing policies, processes, and procedures to oversee AI risk management effectively. Key aspects include:

• Legal and Regulatory Compliance: Understanding and documenting applicable legal requirements related to AI.

• Integration of Trustworthy AI Characteristics: Incorporating principles such as fairness, transparency, and accountability into organizational practices.

• Risk Management Processes: Implementing transparent policies and procedures based on organizational risk priorities.

• Accountability Structures: Defining clear roles and responsibilities for AI risk management across the organization.

• Workforce Diversity and Inclusion: Prioritizing diversity, equity, inclusion, and accessibility in AI risk management throughout the AI lifecycle.

2. Map

This function involves understanding the context, capabilities, and potential impacts of AI systems. Key aspects include:

• Context Establishment: Defining the business value and application context of AI systems.

• System Categorization: Classifying AI systems based on their capabilities and intended use.

• Risk and Benefit Mapping: Identifying potential risks and benefits associated with AI components, including third-party software and data.

• Impact Characterization: Assessing potential impacts on individuals, groups, communities, organizations, and society.

3. Measure

This function emphasizes the evaluation of AI systems for trustworthiness characteristics. Key aspects include:

• Metric Identification: Selecting appropriate methods and metrics to measure AI risks.

• System Evaluation: Assessing AI systems for attributes such as safety, security, robustness, fairness, and privacy.

• Risk Tracking: Implementing mechanisms to monitor identified AI risks over time.

• Feedback Assessment: Gathering and evaluating feedback on the effectiveness of measurement approaches.

4. Manage

This function focuses on prioritizing and responding to AI risks based on assessments. Key aspects include:

• Risk Prioritization: Determining the significance of AI risks and allocating resources accordingly.

• Response Planning: Developing strategies to address high-priority AI risks, including mitigation, transfer, avoidance, or acceptance.

• Resource Allocation: Ensuring necessary resources are available to manage AI risks effectively.

• Third-Party Risk Management: Monitoring and controlling risks associated with third-party AI components.

• Communication Plans: Establishing plans for response, recovery, and communication regarding identified AI risks.

This resource offers comprehensive guidance to tailor your AI risk assessment and management practices to your organization's specific needs and objectives.

For a detailed exploration of each function, category, and subcategory, along with suggested actions, you can refer to the NIST AI RMF Playbook available on NIST's Knowledge base home page. I've also linked the corresponding artifacts below.

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References Links:

• https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf

• https://airc.nist.gov/docs/AI_RMF_Playbook.pdf

• https://airc.nist.gov/AI_RMF_Knowledge_Base/Playbook