Nist AI Rmf Governance

NIST AI RMF Governance

Overview

The NIST AI Risk Management Framework (AI RMF 1.0) (NIST AI 100-1, January 2023) provides voluntary guidance for trustworthy AI across four core functions:

Trustworthy AI characteristics (NIST): valid & reliable, safe, secure & resilient, accountable & transparent, explainable & interpretable, privacy-enhanced, fair with harmful bias managed.

Companion resources: NIST AI RMF Playbook, Generative AI profile (NIST AI 600-1), cross-walk to NIST CSF 2.0 (nist-csf-2-assessment).

This skill is the primary AI-specific governance path for compliance-agent-skills deployments.

When to Use

Use this skill when:

• Deploying or auditing LLM agents, MCP servers, or GenAI in regulated workflows
• Building AI governance policies (acceptable use, human oversight, model selection)
• Mapping AI risks for compliance agents processing PHI, PCI, or PII
• TEVV (test, evaluation, verification, validation) for agent outputs and tool calls
• Executive/board AI risk reporting using NIST taxonomy
• Harmonizing with SOC 2 (CC8 change, CC7 monitoring) and ISO 27001 for AI systems
• Customer AI due diligence questionnaires (NIST AI RMF, SOC 2 + AI addendum)

Do not use this skill when:

• Pure infrastructure security with no AI/ML (use nist-csf-2-assessment)
• PHI redaction tuning only (use hipaa-phi-redaction-pipeline)
• Children's data FTC rules (use coppa-children-privacy)
• Student education records (use ferpa-education-records)

Core Process

Execute steps in order.

Step 1: GOVERN — AI accountability structure

1. Document AI system inventory: agents, models, MCP tools, embeddings, fine-tunes.
2. Assign roles: executive sponsor, AI risk owner, legal/privacy, security, domain experts.
3. Policies required:
   • Acceptable use for LLM prompts (no raw PHI/PAN without redaction)
   • Human-in-the-loop for high-impact decisions
   • Model/vendor change management (CC8.1 alignment)
4. Artifact: AI governance charter + RACI matrix.

Step 2: MAP — Context and risk framing

1. For each agent use case document:
   • Intended purpose and context of use
   • Actors: users, subjects, operators, third parties (LLM vendor)
   • Data types: ePHI, PI, CHD, CUI, public
   • Benefits and potential harms (individual, group, societal)
2. Categorize risk tier (low / moderate / high) based on autonomy, data sensitivity, reversibility.
3. Map dependencies: agent.py redaction gate, mcp-compliance-integration, vendor BAAs/DPAs.
4. Populate templates/nist-ai-rmf-profile.yaml MAP section.

Step 3: MAP — Impacts and likelihood

1. Identify failure modes:
   • Hallucinated regulatory citations (using-compliance-agent-skills anti-patterns)
   • Prompt injection via MCP tools
   • Deanonymization without authorization (agent.py opt-in)
   • Tool over-permission (Postgres MCP write access)
2. Document trustworthiness gaps per NIST characteristics.
3. Cross-reference loaded skills — agent must not invent controls.

Step 4: MEASURE — Metrics and TEVV

1. Define measurable metrics:

   Metric	Example target
   Redaction false negative rate	0% on SSN in test corpus
   Skill routing accuracy	100% framework match on test prompts
   Unauthorized tool call rate	0 in audit mode
   Human override rate	Tracked for high-tier use cases

2. TEVV plan:
   • Pre-deployment: compliance_tests/ corpus, adversarial prompts
   • Production: CCM alerts on deanonymize flag, MCP anomaly detection
3. Document evaluation datasets — synthetic only in CI; no production PHI.
4. Populate MEASURE section in AI RMF profile template.

Step 5: MEASURE — Documentation and transparency

1. Model cards / system cards for agent stack: base model, skills loaded, redaction profile, MCP tools.
2. User-facing disclosure when interacting with AI agent (FTC/state AI transparency trends).
3. Log retention for audit — align audit-logging-integrity.

Step 6: MANAGE — Risk treatment

1. Prioritize risks by tier and exploitability.
2. Controls (map to repo capabilities):
   • Technical: Presidio balanced profile, MCP OAuth, audit mode hooks
   • Process: /scope before /audit, one primary skill per thread
   • Human: Privacy officer sign-off on breach-tier agent outputs
3. Residual risk acceptance with executive sign-off for high-tier deployments.

Step 7: MANAGE — Continuous monitoring and incidents

1. Integrate with soc2-ccm-continuous-monitoring for drift detection.
2. AI-specific incidents: model behavior change, vendor breach, jailbreak — route to breach-incident-response.
3. Change management when adding skills, MCP servers, or model versions — re-run MAP/MEASURE delta.

Step 8: Profile completion and roadmap

1. Complete Current vs Target profile in templates/nist-ai-rmf-profile.yaml.
2. 12-month improvement plan with owners.
3. Cross-walk high-priority controls to NIST CSF 2.0 subcategories for unified reporting.

Common Rationalizations

Red Flags

• Production agent without AI use-case inventory or risk tier
• MCP tools with write access and no human oversight on high-tier flows
• No TEVV before adding new framework skills to production
• Deanonymization enabled globally in EU or HIPAA paths
• Model version changes without change-management record
• Customer-facing agent with no disclosure of automated assistance

Verification

• GOVERN: AI inventory, RACI, and acceptable-use policy documented
• MAP: Use cases with harms, data types, and risk tiers completed
• MEASURE: Metrics and TEVV plan with synthetic test corpus referenced
• MEASURE: System/model card for agent stack published internally
• MANAGE: Risk treatment and residual risk sign-off for high-tier cases
• MANAGE: CCM and incident hooks integrated for AI-specific events
• AI RMF profile template completed with Current/Target states
• Cross-walk to NIST CSF 2.0 documented where required