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]
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:

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:

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.

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

### MAP 1: Context Established
**Rating**: [Not Implemented / Partial / Substantial / Full]

**Findings:**
- [Findings with evidence]

**Gaps:**
- [ ] [Gap description]

**Recommendations:**
- [Recommendation with priority]

[Continue for all MAP categories...]

---

## MEASURE Function Assessment

### MEASURE 1: Methods and Metrics
**Rating**: [Not Implemented / Partial / Substantial / Full]

**Findings:**
- [Findings with evidence]

**Gaps:**
- [ ] [Gap description]

**Recommendations:**
- [Recommendation with priority]

[Continue for all MEASURE categories...]

---

## MANAGE Function Assessment

### MANAGE 1: Risk Prioritization
**Rating**: [Not Implemented / Partial / Substantial / Full]

**Findings:**
- [Findings with evidence]

**Gaps:**
- [ ] [Gap description]

**Recommendations:**
- [Recommendation with priority]

[Continue for all MANAGE categories...]

---

## Risk Register

| 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] |

---

## Remediation Roadmap

### Phase 1: Critical (0-30 days)
1. [Action item with owner and deadline]
2. [Action item with owner and deadline]

### Phase 2: High Priority (30-90 days)
1. [Action item with owner and deadline]

### Phase 3: Medium Priority (90-180 days)
1. [Action item with owner and deadline]

### Phase 4: Continuous Improvement
1. [Ongoing practices]

---

## Compliance Alignment

### Regulatory Mapping
| 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] |

---

## Next Steps

### Immediate Actions
1. [ ] Address critical findings
2. [ ] Assign risk owners
3. [ ] Establish monitoring cadence

### Short-term (1-3 months)
1. [ ] Implement Phase 1 remediation
2. [ ] Establish governance structure
3. [ ] Train personnel on AI RMF

### Long-term (3-12 months)
1. [ ] Complete all remediation phases
2. [ ] Conduct follow-up assessment
3. [ ] Integrate into organizational risk management

---

## Resources

- [NIST AI RMF 1.0](https://www.nist.gov/itl/ai-risk-management-framework)
- [NIST AI RMF Playbook](https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook)
- [NIST AI RMF Generative AI Profile](https://airc.nist.gov/Docs/1)
- [NIST Trustworthy AI Resource Center](https://airc.nist.gov/)

---

**Assessment Version**: 1.0
**Date**: [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

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.