Nist Ai Rmf

NIST AI Risk Management Framework (AI RMF 1.0)

This skill enables AI agents to perform a comprehensive AI risk assessment using the NIST AI Risk Management Framework (AI RMF 1.0), published January 2023 by the National Institute of Standards and Technology.

The AI RMF is a voluntary, technology- and sector-agnostic framework designed to help organizations manage risks associated with AI systems throughout their lifecycle. It promotes trustworthy AI development by addressing risks that affect individuals, organizations, and society.

Use this skill to identify, assess, and manage AI risks; establish governance structures; ensure trustworthy AI characteristics; and align with international AI risk management best practices.

Combine with "ISO 42001 AI Governance" for comprehensive compliance coverage or "OWASP LLM Top 10" for security-focused assessment.

When to Use This Skill

Invoke this skill when:

• Assessing risks of AI systems before deployment
• Establishing AI governance and accountability structures
• Evaluating trustworthiness of AI products and services
• Preparing for regulatory compliance (EU AI Act, state AI laws)
• Conducting periodic AI risk reviews
• Evaluating third-party AI tools and vendors
• Building organizational AI risk management programs
• Documenting AI system risks for stakeholders

Inputs Required

When executing this assessment, gather:

• aisystemdescription: Description of the AI system (purpose, capabilities, deployment context, users, data sources) [REQUIRED]
• systemlifecyclestage: Current stage (design, development, deployment, monitoring, decommissioning) [OPTIONAL, defaults to deployment]
• organization_context: Organization size, industry, risk tolerance, regulatory environment [OPTIONAL]
• deployment_domain: Sector and setting, including whether the system supports critical infrastructure [OPTIONAL]
• existing_controls: Current risk management processes or controls in place [OPTIONAL]
• specific_concerns: Known risks, incidents, or areas of focus [OPTIONAL]
• stakeholders: Key stakeholders and affected communities [OPTIONAL]

Trustworthy AI Characteristics

The AI RMF identifies seven characteristics of trustworthy AI that serve as evaluation criteria across all functions:

1. Valid and Reliable: System performs as intended with consistent results
2. Safe: System does not endanger human life, health, property, or the environment
3. Secure and Resilient: System withstands adverse events and recovers gracefully
4. Accountable and Transparent: Information about the system is available to stakeholders
5. Explainable and Interpretable: Mechanisms and outputs can be understood
6. Privacy-Enhanced: Human autonomy and data rights are protected
7. Fair with Harmful Bias Managed: System does not produce discriminatory outcomes

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The 4 Core Functions

The AI RMF Core is composed of four functions, each broken into categories and subcategories:

GOVERN Function

Establishes organizational policies, processes, and accountability for AI risk management. GOVERN is cross-cutting and applies across all other functions.

GOVERN 1: Policies and Processes

Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively.

• GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented
• GOVERN 1.2: Trustworthy AI characteristics integrated into organizational policies, processes, and practices
• GOVERN 1.3: Processes to determine needed risk management activity levels based on organizational risk tolerance
• GOVERN 1.4: Risk management process and outcomes established through transparent policies, procedures, and controls
• GOVERN 1.5: Ongoing monitoring and periodic review of risk management process with clear roles and responsibilities
• GOVERN 1.6: Mechanisms to inventory AI systems resourced by organizational risk priorities
• GOVERN 1.7: Processes for decommissioning and phasing out AI systems safely

GOVERN 2: Accountability Structures

Accountability structures ensure appropriate teams and individuals are empowered, responsible, and trained for AI risk management.

• GOVERN 2.1: Roles, responsibilities, and communication lines documented and clear
• GOVERN 2.2: Personnel receive AI risk management training
• GOVERN 2.3: Executive leadership takes responsibility for AI decisions

GOVERN 3: Workforce Diversity and Inclusion

Workforce diversity, equity, inclusion, and accessibility processes are prioritized in AI risk management.

• GOVERN 3.1: Decision-making informed by diverse team (demographics, disciplines, expertise)
• GOVERN 3.2: Policies define roles for human-AI configurations and oversight

GOVERN 4: Risk Culture

Organizational teams are committed to a culture that considers and communicates AI risk.

• GOVERN 4.1: Policies foster critical thinking and safety-first mindset
• GOVERN 4.2: Teams document and communicate risks and impacts broadly
• GOVERN 4.3: Practices enable AI testing, incident identification, and information sharing

GOVERN 5: Stakeholder Engagement

Processes are in place for robust engagement with relevant AI actors.

• GOVERN 5.1: Policies collect, consider, and integrate external feedback on impacts
• GOVERN 5.2: Mechanisms regularly incorporate adjudicated feedback into system design

GOVERN 6: Third-Party Risk

Policies and procedures address AI risks from third-party software, data, and supply chain.

• GOVERN 6.1: Policies address risks from third-party entities including IP infringement
• GOVERN 6.2: Contingency processes handle failures in high-risk third-party systems

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MAP Function

Identifies and contextualizes AI system risks within the operational environment.

MAP 1: Context Established

Context is established and understood.

• MAP 1.1: Intended purposes, beneficial uses, laws, norms, and deployment settings documented
• MAP 1.2: Interdisciplinary AI actors with demographic diversity participate and documented
• MAP 1.3: Organization's mission and goals for AI technology understood and documented
• MAP 1.4: Business value or context clearly defined or re-evaluated
• MAP 1.5: Organizational risk tolerances determined and documented
• MAP 1.6: System requirements elicited with socio-technical considerations

MAP 2: System Categorization

Categorization of the AI system is performed.

• MAP 2.1: Specific tasks and methods defined (classifiers, generative models, recommenders)
• MAP 2.2: System knowledge limits and human oversight documented
• MAP 2.3: Scientific integrity and TEVV considerations identified

MAP 3: Capabilities and Costs

AI capabilities, targeted usage, goals, expected benefits, and costs are understood.

• MAP 3.1: Potential benefits of intended functionality examined and documented
• MAP 3.2: Potential costs (monetary and non-monetary) from AI errors documented
• MAP 3.3: Targeted application scope specified based on capability
• MAP 3.4: Operator and practitioner proficiency assessed
• MAP 3.5: Human oversight processes defined and documented

MAP 4: Component Risks

Risks and benefits are mapped for all components including third-party.

• MAP 4.1: Approaches for mapping technology and legal risks documented
• MAP 4.2: Internal risk controls for components identified and documented

MAP 5: Impact Characterization

Impacts to individuals, groups, communities, organizations, and society are characterized.

• MAP 5.1: Likelihood and magnitude of impacts (beneficial and harmful) documented
• MAP 5.2: Practices for regular engagement with relevant AI actors documented

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MEASURE Function

Employs tools, techniques, and methodologies to assess, benchmark, and monitor AI risk.

MEASURE 1: Methods and Metrics

Appropriate methods and metrics are identified and applied.

• MEASURE 1.1: Approaches and metrics selected starting with most significant risks
• MEASURE 1.2: Appropriateness of metrics regularly assessed and updated
• MEASURE 1.3: Internal experts or independent assessors involved in assessments

MEASURE 2: Trustworthiness Evaluation

AI systems are evaluated for trustworthy characteristics.

• MEASURE 2.1: Test sets, metrics, and tool details documented during TEVV
• MEASURE 2.2: Evaluations with human subjects meet requirements and represent relevant populations
• MEASURE 2.3: Performance or assurance criteria measured and demonstrated
• MEASURE 2.4: Functionality and behavior monitored in production
• MEASURE 2.5: System demonstrated valid and reliable with generalizability limitations documented
• MEASURE 2.6: System regularly evaluated for safety risks with residual risk within tolerance
• MEASURE 2.7: Security and resilience evaluated and documented
• MEASURE 2.8: Transparency and accountability risks examined
• MEASURE 2.9: AI model explained, validated, and output interpreted within context
• MEASURE 2.10: Privacy risk examined and documented
• MEASURE 2.11: Fairness and bias evaluated with results documented
• MEASURE 2.12: Environmental impact and sustainability assessed
• MEASURE 2.13: Effectiveness of TEVV metrics evaluated

MEASURE 3: Risk Tracking

Mechanisms for tracking identified AI risks over time are in place.

• MEASURE 3.1: Approaches track existing, unanticipated, and emergent risks
• MEASURE 3.2: Risk tracking considered for settings where assessment is difficult
• MEASURE 3.3: Feedback processes for end users to report problems and appeal outcomes

MEASURE 4: Measurement Efficacy

Feedback about efficacy of measurement is gathered and assessed.

• MEASURE 4.1: Measurement approaches informed by domain experts and end users
• MEASURE 4.2: Results validated for consistency with intended performance
• MEASURE 4.3: Measurable performance improvements or declines identified

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MANAGE Function

Allocates resources to mapped and measured risks on a regular basis.

MANAGE 1: Risk Prioritization

AI risks based on assessments are prioritized, responded to, and managed.

• MANAGE 1.1: Determination made whether AI system achieves intended purposes
• MANAGE 1.2: Treatment of risks prioritized based on impact, likelihood, and resources
• MANAGE 1.3: Responses to high-priority risks developed (mitigate, transfer, avoid, accept)
• MANAGE 1.4: Negative residual risks documented for downstream users

MANAGE 2: Benefit Maximization

Strategies to maximize AI benefits and minimize negative impacts are planned and documented.

• MANAGE 2.1: Resources to manage risks considered alongside non-AI alternatives
• MANAGE 2.2: Mechanisms to sustain value of deployed systems
• MANAGE 2.3: Procedures to respond to and recover from unknown risks
• MANAGE 2.4: Mechanisms to supersede, disengage, or deactivate inconsistent systems

MANAGE 3: Third-Party Risk Management

AI risks and benefits from third-party entities are managed.

• MANAGE 3.1: Third-party risks regularly monitored with controls applied
• MANAGE 3.2: Pre-trained models monitored as part of regular maintenance

MANAGE 4: Communication and Monitoring

Risk treatments and communication plans are documented and monitored.

• MANAGE 4.1: Post-deployment monitoring plans with user input, appeal, and decommissioning mechanisms
• MANAGE 4.2: Continual improvement activities integrated into updates
• MANAGE 4.3: Incidents communicated to relevant actors; tracking and recovery documented

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Audit Procedure

Follow these steps systematically:

Step 1: System Understanding (15 minutes)

1. Review AI system:

• Analyze ai_system_description and system_lifecycle_stage
• Identify system type (classifier, generative, recommender, autonomous, etc.)
• Document data sources, models, and deployment environment
• Note stakeholders and affected communities

1. Understand context:

• Review organization_context and regulatory environment
• Identify applicable laws and standards
• Note risk tolerance and existing controls

1. Define scope:

• Determine which functions and categories to assess
• Prioritize based on lifecycle stage and concerns

Step 2: GOVERN Assessment (20 minutes)

Evaluate organizational governance:

• [ ] G1: Are AI risk policies in place and transparent?
• [ ] G1.1: Legal/regulatory requirements understood and documented?
• [ ] G1.2: Trustworthy AI characteristics in organizational policies?
• [ ] G1.5: Monitoring and review processes planned?
• [ ] G1.6: AI system inventory maintained?
• [ ] G2: Accountability structures defined?
• [ ] G2.1: Roles and responsibilities clear?
• [ ] G2.3: Executive leadership accountable?
• [ ] G3.1: Diverse team informing decisions?
• [ ] G4: Risk culture fostered?
• [ ] G5: Stakeholder engagement processes in place?
• [ ] G6: Third-party risks addressed?

Step 3: MAP Assessment (20 minutes)

Evaluate risk identification and context:

• [ ] M1.1: Intended purposes and deployment context documented?
• [ ] M1.5: Risk tolerances determined?
• [ ] M2.1: AI tasks and methods defined?
• [ ] M2.2: Knowledge limits and human oversight documented?
• [ ] M3.1: Benefits examined and documented?
• [ ] M3.2: Costs from AI errors documented?
• [ ] M3.5: Human oversight processes defined?
• [ ] M4.1: Component risks mapped?
• [ ] M5.1: Impact likelihood and magnitude documented?

Step 4: MEASURE Assessment (25 minutes)

Evaluate risk measurement and monitoring:

• [ ] ME1.1: Risk metrics selected and applied?
• [ ] ME2.3: Performance criteria measured?
• [ ] ME2.4: Production behavior monitored?
• [ ] ME2.5: Validity and reliability demonstrated?
• [ ] ME2.6: Safety risks evaluated?
• [ ] ME2.7: Security and resilience evaluated?
• [ ] ME2.9: Model explainability documented?
• [ ] ME2.10: Privacy risk examined?
• [ ] ME2.11: Fairness and bias evaluated?
• [ ] ME3.1: Risk tracking in place?
• [ ] ME3.3: User feedback mechanisms established?

Step 5: MANAGE Assessment (20 minutes)

Evaluate risk response and treatment:

• [ ] MA1.1: System achieves intended purposes?
• [ ] MA1.2: Risk treatment prioritized?
• [ ] MA1.3: Response plans for high-priority risks?
• [ ] MA2.1: Non-AI alternatives considered?
• [ ] MA2.3: Unknown risk response procedures?
• [ ] MA2.4: Deactivation mechanisms in place?
• [ ] MA3.1: Third-party risks monitored?
• [ ] MA4.1: Post-deployment monitoring implemented?
• [ ] MA4.3: Incident communication and recovery documented?

Step 6: Report Generation (20 minutes)

Compile assessment findings with ratings and recommendations.

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Output Format

Generate a comprehensive NIST AI RMF assessment report:

# NIST AI RMF Assessment Report

**AI System**: [Name/Description]
**Organization**: [Name]
**Date**: [Date]
**Lifecycle Stage**: [Design/Development/Deployment/Monitoring]
**Evaluator**: [AI Agent or Human]
**AI RMF Version**: 1.0 (January 2023)

---

## Executive Summary

### Overall Risk Profile: [Low / Medium / High / Critical]

**System Type**: [Classifier / Generative / Recommender / Autonomous / Other]
**Deployment Context**: [Internal / Customer-facing / Public / Critical infrastructure]
**Regulatory Applicability**: [EU AI Act risk level, state laws, sector regulations]

### Key Findings
- **Total Issues**: [X]
  - Critical: [X] (immediate action required)
  - High: [X] (action required within 3

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [mastepanoski](https://github.com/mastepanoski)
- **Source:** [mastepanoski/claude-skills](https://github.com/mastepanoski/claude-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.