Compliance Framework

NIST AI Risk Management Framework

Comprehensive implementation guide for the NIST AI Risk Management Framework with practical tools, processes, and documentation

Overview

The NIST AI Risk Management Framework (AI RMF) is a voluntary framework designed to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. Our implementation toolkit provides concrete tools and methodologies to implement each function of the framework.

Framework Structure

The NIST AI RMF is organized around four core functions: Govern, Map, Measure, and Manage. Each function contains categories and subcategories of activities to help organizations address AI risks. Our implementation toolkit provides tools for each function.

Framework Structure

Govern

Map

Measure

Manage

These four functions provide a flexible, risk-based approach that can be customized to various AI technologies and organizational contexts. The framework is intended to be implemented in the way that best serves each organization's needs.

Core NIST AI RMF Functions

Govern

Cultivate awareness and implement an organizational governance structure for AI risk management

Implementation Approaches:

AI governance structure definition

Executive-level risk oversight

Cross-functional risk management teams

Documentation of risk policies

Training and awareness programs

Implementation details

Map

Identify, analyze, and document the AI system context and potential AI risks

Implementation Approaches:

AI system inventory and classification

Context documentation

Risk identification workshops

Stakeholder impact analysis

Risk mapping and documentation

Implementation details

Measure

Analyze, assess, and track identified AI risks throughout the AI lifecycle

Implementation Approaches:

Quantitative risk assessment methodologies

Risk metrics and KPIs definition

Regular risk measurement cadence

Risk prioritization frameworks

Risk trend analysis and reporting

Implementation details

Manage

Allocate resources to and implement risk responses based on assessment and prioritization

Implementation Approaches:

Risk mitigation strategy development

Technical control implementation

Organizational control implementation

Third-party risk management

Continuous validation of controls

Implementation details

Govern Function Implementation

The Govern function establishes a comprehensive organizational governance structure to oversee AI risk management activities and create a risk-aware culture.

Implementation Example

// NIST AI RMF Governance Implementation
import { NISTCompliance } from '@akioudai/safety-sdk';

// Initialize governance module
const governance = new NISTCompliance.Governance({
  apiKey: process.env.AKIOUDAI_API_KEY,
  organization: {
    name: 'Example Corp',
    industry: 'healthcare',
    size: 'enterprise'
  }
});

// Define AI governance structure
const governanceStructure = await governance.defineStructure({
  board: {
    oversightCommittee: 'Technology and Risk Committee',
    meetingFrequency: 'quarterly',
    responsibilities: [
      'Approve AI risk management policy',
      'Review risk reports',
      'Ensure adequate resources',
      'Set risk appetite'
    ]
  },
  executive: {
    chiefAIRiskOfficer: 'Jane Smith',
    chiefDataOfficer: 'John Davis',
    reportingLine: 'CEO',
    responsibilities: [
      'Implement AI risk policy',
      'Oversee risk management activities',
      'Report to board'
    ]
  },
  committees: [
    {
      name: 'AI Risk Working Group',
      members: ['IT', 'Legal', 'Compliance', 'Product', 'Data Science'],
      meetingFrequency: 'monthly',
      responsibilities: [
        'Identify emerging risks',
        'Coordinate risk mitigation',
        'Review incidents'
      ]
    }
  ],
  policies: [
    {
      name: 'AI Risk Management Policy',
      version: '1.2',
      lastReviewed: '2023-10-15',
      owner: 'Chief AI Risk Officer',
      approver: 'Board of Directors'
    },
    {
      name: 'AI Development Standards',
      version: '2.1',
      lastReviewed: '2023-09-22',
      owner: 'CTO',
      approver: 'Technology Leadership Team'
    }
  ]
});

// Generate governance documentation
const governanceDocumentation = await governance.generateDocumentation({
  format: 'pdf',
  sections: ['roles', 'policies', 'procedures', 'reporting']
});

// Implement training program
const trainingProgram = await governance.implementTraining({
  audiences: [
    {
      group: 'Board members',
      content: 'AI risk oversight fundamentals',
      frequency: 'annual'
    },
    {
      group: 'AI developers',
      content: 'Risk-aware development practices',
      frequency: 'quarterly'
    },
    {
      group: 'Business users',
      content: 'AI risk identification basics',
      frequency: 'annual'
    }
  ]
});

console.log('Governance structure defined:', governanceStructure);
console.log('Documentation generated:', governanceDocumentation);
console.log('Training program implemented:', trainingProgram);

Key Governance Activities

Establishing AI risk management leadership and oversight
Defining roles and responsibilities across the organization
Creating AI risk policies and standards
Implementing AI risk training and awareness programs
Defining risk escalation and reporting procedures

Implementation Challenges

Securing executive buy-in and resource commitment
Balancing innovation with risk management
Integrating AI risk with enterprise risk frameworks
Building necessary expertise across teams
Establishing meaningful metrics for governance effectiveness

Governance Documentation Templates

AI Risk Policy

Comprehensive policy template defining AI risk management approach

RACI Matrix

Responsibility assignment matrix for AI risk activities

Governance Charter

AI risk committee charter and operating procedures

Map Function Implementation

The Map function involves identifying, documenting, and analyzing context, capabilities, and potential risks of AI systems throughout their lifecycle.

Implementation Example

// NIST AI RMF Map Function Implementation
import { NISTCompliance } from '@akioudai/safety-sdk';

// Initialize Map function
const mapper = new NISTCompliance.MapFunction({
  apiKey: process.env.AKIOUDAI_API_KEY
});

// Document AI system context
const systemContext = await mapper.documentContext({
  system: {
    name: 'Clinical Decision Support System',
    version: '2.1.3',
    description: 'Machine learning system to assist physicians with diagnosis',
    modelTypes: ['random forest', 'neural network ensemble'],
    developmentTeam: 'Healthcare AI Division'
  },
  data: {
    trainingDataSources: ['anonymized patient records', 'medical literature'],
    dataCharacteristics: {
      personalData: true,
      sensitiveData: true,
      demographicData: true
    },
    dataGovernance: 'Healthcare Data Governance Framework v3'
  },
  deployment: {
    environment: 'Hospital clinical systems',
    integrationPoints: ['EHR system', 'physician portal'],
    userTypes: ['attending physicians', 'specialists', 'residents']
  },
  businessContext: {
    businessObjectives: ['improve diagnosis accuracy', 'reduce time to treatment'],
    stakeholders: ['patients', 'physicians', 'hospital administration', 'regulators'],
    regulations: ['HIPAA', 'FDA Software as Medical Device']
  }
});

// Identify and map AI risks
const riskMap = await mapper.identifyRisks({
  systemContext,
  riskCategories: ['technical', 'societal', 'operational', 'security'],
  workshops: [
    {
      participants: ['AI team', 'clinical staff', 'legal', 'privacy'],
      date: '2023-10-10',
      duration: '4 hours'
    }
  ],
  methodologies: ['expert elicitation', 'scenario analysis', 'failure mode analysis']
});

// Generate NIST RMF Map documentation
const mapDocumentation = await mapper.generateDocumentation({
  systemContext,
  riskMap,
  format: 'pdf',
  sections: ['context', 'risks', 'stakeholders', 'data', 'deployment']
});

console.log('System context documented:', systemContext);
console.log('Risks identified:', riskMap.risks.length);
console.log('Risk categories:', riskMap.riskCategories);
console.log('Map documentation generated:', mapDocumentation);

Key Mapping Activities

Documenting AI system context and capabilities
Identifying system interdependencies and integration points
Cataloging data sources, types, and flows
Conducting stakeholder impact analysis
Identifying and documenting potential AI risks

Risk Categories

Technical: Accuracy, reliability, security, robustness
Societal: Fairness, privacy, autonomy, transparency
Operational: Integration, maintenance, scalability
Strategic: Reputation, compliance, market position

Map Function Toolkit Components

AI System Inventory Tool

Centralized repository to document and track all AI systems across the organization

Risk Identification Workshop Toolkit

Structured templates and facilitation guides for conducting risk identification workshops

Measure Function Implementation

The Measure function involves analyzing, assessing and tracking identified AI risks to inform decision-making and response priorities.

Implementation Example

// NIST AI RMF Measure Function Implementation
import { NISTCompliance } from '@akioudai/safety-sdk';

// Initialize Measure function
const measurer = new NISTCompliance.MeasureFunction({
  apiKey: process.env.AKIOUDAI_API_KEY
});

// Define assessment methodology
const assessmentMethodology = await measurer.defineMethodology({
  quantitative: {
    riskFormula: 'likelihood * impact',
    likelihoodScale: [
      { value: 1, label: 'Rare', criteria: 'Less than once per year' },
      { value: 2, label: 'Unlikely', criteria: 'Once per year' },
      { value: 3, label: 'Possible', criteria: 'Once per quarter' },
      { value: 4, label: 'Likely', criteria: 'Once per month' },
      { value: 5, label: 'Almost Certain', criteria: 'Once per week or more' }
    ],
    impactScale: [
      { value: 1, label: 'Negligible', criteria: 'Minimal impact to stakeholders' },
      { value: 2, label: 'Minor', criteria: 'Limited, temporary impact' },
      { value: 3, label: 'Moderate', criteria: 'Significant but containable impact' },
      { value: 4, label: 'Major', criteria: 'Substantial impact, regulatory concern' },
      { value: 5, label: 'Severe', criteria: 'Critical impact, regulatory violations' }
    ]
  },
  qualitative: {
    techniques: ['expert interviews', 'peer review', 'risk workshops'],
    participants: ['domain experts', 'AI engineers', 'users', 'compliance team']
  }
});

// Assess risks based on methodology
const riskAssessment = await measurer.assessRisks({
  methodology: assessmentMethodology,
  risks: [
    {
      id: 'RISK-001',
      description: 'Biased predictions due to unrepresentative training data',
      category: 'technical',
      assessment: {
        likelihood: 4,
        impact: 5,
        score: 20,
        level: 'High',
        confidence: 'Medium',
        evidenceBase: ['data analysis', 'literature review', 'similar incidents']
      }
    },
    {
      id: 'RISK-002',
      description: 'Unauthorized access to sensitive patient data',
      category: 'security',
      assessment: {
        likelihood: 2,
        impact: 5,
        score: 10,
        level: 'Medium',
        confidence: 'High',
        evidenceBase: ['security audit', 'threat modeling']
      }
    },
    // Additional risks would be included here
  ]
});

// Define and implement risk metrics
const riskMetrics = await measurer.defineMetrics({
  keyRiskIndicators: [
    {
      name: 'Bias Detection Rate',
      description: 'Percentage of biased predictions detected',
      formula: 'biased_predictions / total_predictions',
      target: '< 0.1%',
      threshold: '0.5%',
      frequency: 'weekly'
    },
    {
      name: 'False Recommendation Rate',
      description: 'Percentage of incorrect clinical recommendations',
      formula: 'incorrect_recommendations / total_recommendations',
      target: '< 0.01%',
      threshold: '0.05%',
      frequency: 'daily'
    }
  ],
  monitoringPlan: {
    tools: ['AI monitoring dashboard', 'automated alerts', 'weekly reports'],
    responsibilities: {
      collection: 'AI Operations Team',
      analysis: 'AI Risk Analyst',
      reporting: 'AI Risk Manager'
    },
    escalation: {
      thresholdExceeded: 'AI Risk Committee within 24 hours',
      criticalRisk: 'Executive Team and Board within 4 hours'
    }
  }
});

console.log('Assessment methodology defined:', assessmentMethodology);
console.log('Risks assessed:', riskAssessment.risks.length);
console.log('Key Risk Indicators defined:', riskMetrics.keyRiskIndicators.length);

Key Measurement Activities

Defining risk assessment methodologies and criteria
Establishing quantitative and qualitative metrics
Assessing likelihood and impact of identified risks
Prioritizing risks based on assessment results
Tracking risk metrics over time to identify trends

Measurement Tools

Risk Matrices: Visual frameworks for likelihood/impact assessment
Key Risk Indicators: Quantifiable metrics to track risk exposure
Risk Dashboards: Visual representations of risk metrics over time
Testing Tools: Automated testing for bias, robustness, and security

Risk Assessment Approach

Risk Type	Assessment Method	Key Metrics	Tools
Bias & Fairness	Statistical analysis across demographic groups	Disparate impact ratio, statistical parity difference	Fairness testing tools, demographic analysis
Robustness	Adversarial testing, input variation	Error rates under perturbation, sensitivity analysis	Adversarial attack tools, robustness metrics
Privacy	Privacy impact assessment, data flow analysis	Data minimization score, re-identification risk	Privacy assessment toolkit, anonymization tests
Security	Threat modeling, vulnerability assessment	Vulnerability counts, security test coverage	Security testing tools, penetration testing

Manage Function Implementation

The Manage function involves implementing controls and approaches to address AI risks based on priorities and organizational risk tolerance.

Implementation Example

// NIST AI RMF Manage Function Implementation
import { NISTCompliance } from '@akioudai/safety-sdk';

// Initialize Manage function
const manager = new NISTCompliance.ManageFunction({
  apiKey: process.env.AKIOUDAI_API_KEY
});

// Define risk response strategy
const riskResponseStrategy = await manager.defineStrategy({
  approachByRiskLevel: {
    high: 'mitigate', // Active risk reduction required
    medium: 'mitigate/accept', // Case-by-case with justification
    low: 'accept/monitor' // Typically accepted with monitoring
  },
  prioritizationFramework: {
    factors: ['risk score', 'implementation cost', 'effectiveness', 'time to implement'],
    weightings: {
      'risk score': 0.5,
      'implementation cost': 0.2,
      'effectiveness': 0.2,
      'time to implement': 0.1
    }
  },
  approvalProcess: {
    high: 'AI Risk Committee',
    medium: 'Department Head',
    low: 'AI Risk Manager'
  }
});

// Implement controls for identified risks
const controlImplementation = await manager.implementControls({
  controls: [
    {
      id: 'CTRL-001',
      name: 'Training Data Diversity Verification',
      description: 'Process to verify training data includes diverse demographics',
      risk: 'RISK-001', // Biased predictions risk
      type: 'preventive',
      implementation: {
        responsible: 'Data Science Team',
        verification: 'AI Ethics Committee',
        status: 'implemented',
        evidence: 'data_diversity_report_2023Q4.pdf'
      }
    },
    {
      id: 'CTRL-002',
      name: 'Encrypted Data Storage',
      description: 'End-to-end encryption for all patient data',
      risk: 'RISK-002', // Unauthorized access risk
      type: 'preventive',
      implementation: {
        responsible: 'IT Security Team',
        verification: 'Security Auditor',
        status: 'implemented',
        evidence: 'encryption_audit_2023.pdf'
      }
    },
    {
      id: 'CTRL-003',
      name: 'Human Review of High-Risk Recommendations',
      description: 'Process for physician review of high-risk AI recommendations',
      risk: 'RISK-001', // Biased predictions risk
      type: 'detective',
      implementation: {
        responsible: 'Clinical Operations',
        verification: 'Medical Director',
        status: 'in progress',
        evidence: 'review_process_draft_v3.pdf'
      }
    }
  ]
});

// Develop management plans
const managementPlan = await manager.developManagementPlan({
  monitoringPlan: {
    schedule: 'continuous with weekly review',
    metrics: ['false recommendation rate', 'model drift percentage', 'data quality score'],
    tools: ['AI monitoring dashboard', 'model performance tracker'],
    responsibilities: 'AI Operations Team'
  },
  incidentResponsePlan: {
    activationCriteria: ['patient safety concern', 'metrics exceeding thresholds', 'external complaints'],
    responseTeam: ['Medical Director', 'AI Risk Manager', 'Legal Counsel', 'Communications'],
    procedures: ['investigation_procedure.pdf', 'containment_procedure.pdf', 'notification_procedure.pdf'],
    testing: 'quarterly tabletop exercises'
  },
  reportingPlan: {
    internal: {
      executive: 'monthly',
      board: 'quarterly',
      staff: 'as needed'
    },
    external: {
      regulators: 'as required',
      patients: 'as required by law',
      public: 'annual transparency report'
    }
  }
});

console.log('Risk response strategy defined:', riskResponseStrategy);
console.log('Controls implemented:', controlImplementation.controls.length);
console.log('Management plan developed:', managementPlan);

Key Management Activities

Developing risk response strategies
Implementing technical and organizational controls
Documenting control effectiveness
Continuous monitoring and validation
Incident response and remediation procedures

Risk Response Options

Accept: Acknowledge the risk and its potential impacts
Mitigate: Implement controls to reduce likelihood or impact
Transfer: Share the risk with a third party (e.g., insurance)
Avoid: Eliminate the risk by changing approach or scope

Control Implementation Example

Risk Area	Control Type	Implementation Example	Verification Method
Bias	Preventive	Diverse training data collection process	Statistical testing across demographic groups
Model Drift	Detective	Continuous performance monitoring system	Statistical process control charts
Explainability	Corrective	Post-hoc explanation generation	User comprehension testing
Security	Preventive/Detective	Model poisoning detection system	Penetration testing, security reviews