| name | nist-ai-rmf |
| description | AI risk assessment using NIST AI RMF 1.0 framework. Evaluate AI systems across 4 core functions (Govern, Map, Measure, Manage) for trustworthy and responsible AI deployment. |
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:
- ai_system_description: Description of the AI system (purpose, capabilities, deployment context, users, data sources) [REQUIRED]
- system_lifecycle_stage: 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:
- Valid and Reliable: System performs as intended with consistent results
- Safe: System does not endanger human life, health, property, or the environment
- Secure and Resilient: System withstands adverse events and recovers gracefully
- Accountable and Transparent: Information about the system is available to stakeholders
- Explainable and Interpretable: Mechanisms and outputs can be understood
- Privacy-Enhanced: Human autonomy and data rights are protected
- Fair with Harmful Bias Managed: System does not produce discriminatory outcomes
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
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
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
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
Audit Procedure
Follow these steps systematically:
Step 1: System Understanding (15 minutes)
-
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
-
Understand context:
- Review
organization_context and regulatory environment
- Identify applicable laws and standards
- Note risk tolerance and existing controls
-
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:
Step 3: MAP Assessment (20 minutes)
Evaluate risk identification and context:
Step 4: MEASURE Assessment (25 minutes)
Evaluate risk measurement and monitoring:
Step 5: MANAGE Assessment (20 minutes)
Evaluate risk response and treatment:
Step 6: Report Generation (20 minutes)
Compile assessment findings with ratings and recommendations.
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 30 days)
- Medium: [X] (action required within 90 days)
- Low: [X] (improvements recommended)
### Trustworthiness Summary
| Characteristic | Status | Rating |
|---|---|---|
| Valid & Reliable | [Status] | [1-5] |
| Safe | [Status] | [1-5] |
| Secure & Resilient | [Status] | [1-5] |
| Accountable & Transparent | [Status] | [1-5] |
| Explainable & Interpretable | [Status] | [1-5] |
| Privacy-Enhanced | [Status] | [1-5] |
| Fair (Bias Managed) | [Status] | [1-5] |
---
## GOVERN Function Assessment
### GOVERN 1: Policies and Processes
**Rating**: [Not Implemented / Partial / Substantial / Full]
**Findings:**
- [Finding 1 with evidence]
- [Finding 2 with evidence]
**Gaps:**
- [ ] [Gap description]
**Recommendations:**
- [Recommendation with priority]
### GOVERN 2: Accountability Structures
**Rating**: [Not Implemented / Partial / Substantial / Full]
[Continue for all GOVERN categories...]
---
## MAP Function Assessment
: [Not Implemented / Partial / Substantial / Full]
[Findings with evidence]
[ ] [Gap description]
[Recommendation with priority]
[Continue for all MAP categories...]
---
: [Not Implemented / Partial / Substantial / Full]
[Findings with evidence]
[ ] [Gap description]
[Recommendation with priority]
[Continue for all MEASURE categories...]
---
: [Not Implemented / Partial / Substantial / Full]
[Findings with evidence]
[ ] [Gap description]
[Recommendation with priority]
[Continue for all MANAGE categories...]
---
| ID | Risk Description | Function | Likelihood | Impact | Priority | Mitigation |
|---|---|---|---|---|---|---|
| R1 | [Description] | [G/M/ME/MA] | [L/M/H] | [L/M/H] | [P0-P3] | [Strategy] |
| R2 | [Description] | [G/M/ME/MA] | [L/M/H] | [L/M/H] | [P0-P3] | [Strategy] |
---
[Action item with owner and deadline]
[Action item with owner and deadline]
[Action item with owner and deadline]
[Action item with owner and deadline]
[Ongoing practices]
---
| Regulation | Relevant AI RMF Functions | Status |
|---|---|---|
| EU AI Act | GOVERN, MAP, MEASURE | [Status] |
| NIST CSF 2.0 | GOVERN, MANAGE | [Status] |
| State AI Laws | GOVERN, MAP | [Status] |
| Sector Regulations | [Relevant functions] | [Status] |
---
[ ] Address critical findings
[ ] Assign risk owners
[ ] Establish monitoring cadence
[ ] Implement Phase 1 remediation
[ ] Establish governance structure
[ ] Train personnel on AI RMF
[ ] Complete all remediation phases
[ ] Conduct follow-up assessment
[ ] Integrate into organizational risk management
---
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: 1.0
: [Date]
Scoring Guide
Use this scale for subcategory ratings:
| Rating | Description |
|---|
| Not Implemented | No evidence of activity or documentation |
| Partial | Some activity but inconsistent or incomplete |
| Substantial | Mostly implemented with minor gaps |
| Full | Fully implemented and regularly maintained |
Use this scale for trustworthiness characteristics:
| Score | Description |
|---|
| 1 | Not addressed |
| 2 | Minimally addressed |
| 3 | Partially addressed |
| 4 | Substantially addressed |
| 5 | Fully addressed and monitored |
Generative AI Considerations
For generative AI systems, additionally evaluate (per NIST AI 600-1 GenAI Profile, July 2024):
- Content provenance: Mechanisms to track AI-generated content origin
- Confabulation risk: Controls for hallucinated or fabricated outputs
- Data privacy: Training data protections and consent
- Environmental impact: Computational resource consumption
- Information security: Prompt injection and adversarial robustness
- Harmful content: Filters and safeguards for toxic or dangerous outputs
- Third-party risks: Foundation model and API dependencies
- Human-AI interaction: User awareness that they are interacting with AI
Critical Infrastructure Note
If the AI system supports critical infrastructure operations, also check current NIST AI RMF profile material for trustworthy AI in critical infrastructure. NIST released a concept note for this profile on April 7, 2026; treat it as additional context rather than a replacement for AI RMF 1.0.
Best Practices
- Start with GOVERN: Establish governance before mapping risks
- Iterate continuously: Risk management is ongoing, not one-time
- Engage stakeholders: Include diverse perspectives in assessments
- Document everything: Maintain evidence for accountability
- Align with existing frameworks: Integrate with NIST CSF, ISO 42001, SOC 2
- Tailor to context: Adapt depth of assessment to system risk level
- Test in production: Monitor deployed systems, not just pre-deployment
- Plan for failure: Have incident response and decommissioning procedures
- Consider societal impact: Look beyond organizational risks
- Stay current: Monitor evolving AI regulations and standards
Version
1.0 - Initial release (NIST AI RMF 1.0 compliant)
Remember: The NIST AI RMF is voluntary and risk-based. Not all subcategories apply to every system. Tailor the assessment depth to the system's risk profile and organizational context.
