Nist AI Rmf

NIST AI Risk Management Framework

Implement the NIST AI RMF 1.0 for comprehensive AI risk management following the Govern, Map, Measure, Manage framework.

When to Use This Skill

Use this skill when you need to:

• Implement AI risk management
• Meet US federal AI requirements
• Establish AI governance framework
• Document AI risks and controls
• Prepare for AI audits
• Deploy AI responsibly

Trigger phrases: "NIST AI RMF", "AI risk management", "federal AI compliance", "AI governance", "AI risk framework", "responsible AI"

What This Skill Does

NIST AI RMF Functions

Instructions

Step 1: GOVERN Function

class AIGovernanceFramework:
    """NIST AI RMF GOVERN function implementation."""

    def establish_governance_structure(self) -> GovernanceStructure:
        """Define AI governance roles and responsibilities."""
        return GovernanceStructure(
            roles=[
                Role(
                    title="AI Governance Board",
                    responsibilities=[
                        "Approve AI strategy and policies",
                        "Oversee high-risk AI deployments",
                        "Review AI incident reports",
                        "Ensure regulatory compliance"
                    ],
                    members=["CTO", "CISO", "Legal", "Ethics Officer"]
                ),
                Role(
                    title="AI Risk Manager",
                    responsibilities=[
                        "Conduct AI risk assessments",
                        "Maintain AI risk register",
                        "Report to governance board",
                        "Coordinate risk treatment"
                    ]
                ),
                Role(
                    title="AI System Owner",
                    responsibilities=[
                        "Accountable for AI system risks",
                        "Ensure compliance with policies",
                        "Approve AI system changes",
                        "Respond to incidents"
                    ]
                ),
            ],
            reporting_structure={
                "ai_system_owner": "ai_risk_manager",
                "ai_risk_manager": "ai_governance_board",
                "ai_governance_board": "executive_leadership"
            }
        )

    def create_ai_policy(self) -> AIPolicy:
        """Create organizational AI policy."""
        return AIPolicy(
            title="Organizational AI Policy",
            version="1.0",
            sections=[
                PolicySection(
                    name="Purpose",
                    content="Establish principles for responsible AI development and use"
                ),
                PolicySection(
                    name="Scope",
                    content="All AI systems developed, deployed, or acquired"
                ),
                PolicySection(
                    name="Principles",
                    content=[
                        "Transparency: AI decisions should be explainable",
                        "Fairness: AI should not discriminate",
                        "Privacy: AI must protect personal data",
                        "Safety: AI must be reliable and secure",
                        "Accountability: Clear ownership of AI risks"
                    ]
                ),
                PolicySection(
                    name="Requirements",
                    content=[
                        "Risk assessment for all AI systems",
                        "Impact assessment for high-risk AI",
                        "Human oversight for consequential decisions",
                        "Continuous monitoring of AI performance"
                    ]
                ),
            ]
        )

    def define_risk_tolerance(self) -> RiskTolerance:
        """Define organizational AI risk tolerance."""
        return RiskTolerance(
            categories={
                "safety": {
                    "tolerance": "very_low",
                    "description": "No tolerance for AI causing physical harm"
                },
                "fairness": {
                    "tolerance": "low",
                    "description": "Minimal tolerance for discriminatory outcomes"
                },
                "privacy": {
                    "tolerance": "low",
                    "description": "Strong protection of personal data required"
                },
                "security": {
                    "tolerance": "low",
                    "description": "Robust defenses against adversarial attacks"
                },
                "reliability": {
                    "tolerance": "medium",
                    "description": "Acceptable degradation under defined conditions"
                }
            }
        )

Step 2: MAP Function

class AISystemMapping:
    """NIST AI RMF MAP function implementation."""

    def characterize_ai_system(self, system_info: dict) -> AICharacterization:
        """Characterize AI system context and risks."""
        return AICharacterization(
            system_id=system_info["id"],
            basic_info={
                "name": system_info["name"],
                "purpose": system_info["purpose"],
                "type": system_info["type"],  # ML, rule-based, hybrid
                "deployment_environment": system_info["environment"],
            },
            intended_use={
                "primary_users": system_info["users"],
                "use_cases": system_info["use_cases"],
                "intended_benefits": system_info["benefits"],
                "limitations": system_info["limitations"],
            },
            data_characteristics={
                "training_data_sources": system_info["data_sources"],
                "data_types": system_info["data_types"],
                "data_volume": system_info["data_volume"],
                "sensitive_data": system_info["sensitive_data"],
            },
            technical_characteristics={
                "model_type": system_info["model_type"],
                "model_complexity": system_info["complexity"],
                "explainability_level": system_info["explainability"],
                "update_frequency": system_info["update_frequency"],
            }
        )

    def identify_stakeholders(self, system_id: str) -> list:
        """Identify AI system stakeholders and impacts."""
        return [
            Stakeholder(
                type="direct_users",
                description="Users who interact directly with the AI system",
                potential_impacts=["accuracy of decisions", "user experience"],
                engagement_method="user testing, feedback collection"
            ),
            Stakeholder(
                type="affected_individuals",
                description="People affected by AI system decisions",
                potential_impacts=["fairness", "access to services", "privacy"],
                engagement_method="impact assessments, public consultation"
            ),
            Stakeholder(
                type="operators",
                description="Staff who operate and maintain the AI system",
                potential_impacts=["workload", "skill requirements"],
                engagement_method="training, operational feedback"
            ),
            Stakeholder(
                type="oversight_bodies",
                description="Regulators and auditors",
                potential_impacts=["compliance requirements"],
                engagement_method="reporting, audits"
            ),
        ]

    def map_ai_risks(self, characterization: AICharacterization) -> list:
        """Map potential risks based on AI system characteristics."""
        risk_categories = [
            "Validity and reliability",
            "Safety",
            "Security and resilience",
            "Accountability and transparency",
            "Explainability and interpretability",
            "Privacy",
            "Fairness (harmful bias management)"
        ]

        risks = []
        for category in risk_categories:
            category_risks = self._assess_category_risks(characterization, category)
            risks.extend(category_risks)

        return risks

Step 3: MEASURE Function

class AIRiskMeasurement:
    """NIST AI RMF MEASURE function implementation."""

    def assess_ai_risks(self, system_id: str, mapped_risks: list) -> RiskAssessmentReport:
        """Quantify and analyze identified AI risks."""
        assessed_risks = []

        for risk in mapped_risks:
            assessment = RiskAssessment(
                risk_id=risk.id,
                category=risk.category,
                description=risk.description,
                likelihood=self._assess_likelihood(risk),
                impact=self._assess_impact(risk),
                existing_controls=self._identify_controls(risk),
                residual_risk=self._calculate_residual(risk),
                confidence_level=self._assess_confidence(risk)
            )
            assessed_risks.append(assessment)

        return RiskAssessmentReport(
            system_id=system_id,
            assessment_date=datetime.utcnow(),
            risks=assessed_risks,
            overall_risk_level=self._calculate_overall_risk(assessed_risks),
            recommendations=self._generate_recommendations(assessed_risks)
        )

    def test_ai_system(self, system_id: str) -> TestingReport:
        """Comprehensive AI system testing."""
        tests = {
            "accuracy_testing": self._test_accuracy(system_id),
            "fairness_testing": self._test_fairness(system_id),
            "robustness_testing": self._test_robustness(system_id),
            "security_testing": self._test_security(system_id),
            "explainability_testing": self._test_explainability(system_id),
        }

        return TestingReport(
            system_id=system_id,
            test_date=datetime.utcnow(),
            test_results=tests,
            overall_status=self._determine_status(tests),
            issues_found=self._collect_issues(tests)
        )

    def _test_fairness(self, system_id: str) -> FairnessTestResult:
        """Test AI system for fairness across protected groups."""
        protected_attributes = ["gender", "race", "age", "disability"]

        results = {}
        for attr in protected_attributes:
            results[attr] = {
                "demographic_parity_ratio": self._calc_dp_ratio(system_id, attr),
                "equalized_odds_difference": self._calc_eo_diff(system_id, attr),
                "disparate_impact_ratio": self._calc_di_ratio(system_id, attr),
                "threshold_passed": True  # Based on defined thresholds
            }

        return FairnessTestResult(
            system_id=system_id,
            metrics=results,
            overall_fair=all(r["threshold_passed"] for r in results.values())
        )

    def _test_robustness(self, system_id: str) -> RobustnessTestResult:
        """Test AI system robustness against adversarial inputs."""
        return RobustnessTestResult(
            system_id=system_id,
            tests=[
                {"test": "noise_perturbation", "passed": True, "accuracy_drop": 0.02},
                {"test": "adversarial_examples", "passed": True, "success_rate": 0.05},
                {"test": "out_of_distribution", "passed": True, "detection_rate": 0.95},
            ]
        )

Step 4: MANAGE Function

class AIRiskManagement:
    """NIST AI RMF MANAGE function implementation."""

    def prioritize_risks(self, assessment: RiskAssessmentReport) -> PrioritizedRiskList:
        """Prioritize risks for treatment."""
        prioritized = sorted(
            assessment.risks,
            key=lambda r: (r.impact.value * r.likelihood.value, r.impact.value),
            reverse=True
        )

        return PrioritizedRiskList(
            system_id=assessment.system_id,
            risks=[
                PrioritizedRisk(
                    risk=risk,
                    priority=idx + 1,
                    treatment_urgency=self._determine_urgency(risk)
                )
                for idx, risk in enumerate(prioritized)
            ]
        )

    def develop_treatment_plan(
        self,
        risk: RiskAssessment,
        strategy: str
    ) -> RiskTreatmentPlan:
        """Develop risk treatment plan."""
        strategies = {
            "mitigate": self._develop_mitigation_plan,
            "transfer": self._develop_transfer_plan,
            "avoid": self._develop_avoidance_plan,
            "accept": self._develop_acceptance_plan,
        }

        plan_func = strategies.get(strategy)
        if not plan_func:
            raise ValueError(f"Unknown strategy: {strategy}")

        plan = plan_func(risk)

        audit_log.info(
            "risk_treatment_plan_created",
            risk_id=risk.risk_id,
            strategy=strategy
        )

        return plan

    def _develop_mitigation_plan(self, risk: RiskAssessment) -> RiskTreatmentPlan:
        """Develop risk mitigation controls."""
        return RiskTreatmentPlan(
            risk_id=risk.risk_id,
            strategy="mitigate",
            controls=[
                Control(
                    name="Bias detection and correction",
                    type="preventive",
                    implementation="Automated fairness testing in CI/CD",
                    effectiveness="high",
                    cost="medium"
                ),
                Control(
                    name="Human oversight",
                    type="detective",
                    implementation="Review queue for high-impact decisions",
                    effectiveness="high",
                    cost="high"
                ),
            ],
            target_residual_risk="low",
            implementation_timeline="Q1 2025",
            responsible_party="AI System Owner"
        )

    def monitor_risks(self, system_id: str) -> MonitoringReport:
        """Continuous risk monitoring."""
        return MonitoringReport(
            system_id=system_id,
            timestamp=datetime.utcnow(),
            metrics={
                "model_performance": self._get_performance_metrics(system_id),
                "fairness_metrics": self._get_fairness_metrics(system_id),
                "incident_count": self._get_incident_count(system_id),
                "control_effectiveness": self._assess_control_effectiveness(system_id),
            },
            alerts=self._check_thresholds(system_id),
            recommendations=self._generate_monitoring_recommendations(system_id)
        )

    def respond_to_incidents(self, incident: AIIncident) -> IncidentResponse:
        """Respond to AI-related incidents."""
        response = IncidentResponse(
            incident_id=incident.id,
            severity=incident.severity,
            response_actions=[],
            timeline=[]
        )

        # Immediate containment
        if incident.severity in ["critical", "high"]:
            response.response_actions.append(
                Action(
                    type="containment",
                    description="Disable AI system or affected functionality",
                    status="completed"
                )
            )

        # Root cause analysis
        response.response_actions.append(
            Action(
                type="investigation",
                description="Conduct root cause analysis",
                status="in_progress"
            )
        )

        audit_log.info(
            "ai_incident_response_initiated",
            incident_id=incident.id,
            severity=incident.severity
        )

        return response

Documentation Requirements

Required Documents

• AI Governance Policy
• AI Risk Management Procedure
• AI System Inventory
• Risk Assessment Methodology
• Testing and Validation Procedures

Required Records

• AI system characterizations
• Risk assessments
• Test results
• Treatment plans and status
• Monitoring reports
• Incident records

Quality Checklist

• GOVERN: Governance structure established
• GOVERN: AI policy approved
• MAP: All AI systems characterized
• MAP: Stakeholders identified
• MEASURE: Risk assessments completed
• MEASURE: Testing performed
• MANAGE: Risks prioritized
• MANAGE: Treatment plans developed
• MANAGE: Monitoring active

Related Skills

• iso42001-ai-governance - ISO 42001 AI management
• ai-agent-governance - AI agent governance
• security-review - Security vulnerability review

Version: 1.0.0 Last Updated: December 2025 Based on: AI Templates compliance_governance/compliance_frameworks/

Iterative Refinement Strategy

This skill is optimized for an iterative approach:

1. Execute: Perform the core steps defined above.
2. Review: Critically analyze the output (coverage, quality, completeness).
3. Refine: If targets aren't met, repeat the specific implementation steps with improved context.
4. Loop: Continue until the definition of done is satisfied.