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Comprehensive documentation for implementing AI compliance across major regulatory frameworks
Overview
Our compliance framework documentation provides practical guidance for implementing AI governance across major regulatory frameworks. Each guide includes concrete steps, technical measures, and documentation templates to help you meet compliance requirements.
Regulatory Disclaimer
This documentation is provided for informational purposes only and does not constitute legal advice. We recommend consulting with legal counsel for specific regulatory requirements.
EU AI Act
Comprehensive implementation guidance for the European Union's AI Act regulations
GDPR for AI
Application of General Data Protection Regulation principles to AI systems and data processing
NIST AI RMF
Implementation of the NIST AI Risk Management Framework for AI systems
ISO/IEC 42001
Implementation of the ISO/IEC 42001 standard for AI management systems
EU AI Act
The EU AI Act is a comprehensive regulatory framework proposed by the European Commission to ensure the safety, transparency, and ethical use of AI systems in the European Union.
Risk Classification
The EU AI Act classifies AI systems into four risk categories: unacceptable risk, high risk, limited risk, and minimal risk. Each category has specific requirements and obligations.
1// Example of risk classification API usage2import { ComplianceFramework } from '@akioudai/safety-sdk';34// Initialize EU AI Act compliance framework5const euAiActFramework = new ComplianceFramework({6 apiKey: process.env.AKIOUDAI_API_KEY,7 framework: 'eu_ai_act'8});910// Function to classify AI system risk level11async function classifyAISystemRisk(systemDescription) {12 const classification = await euAiActFramework.classifyRisk({13 systemName: 'Customer Support Assistant',14 description: systemDescription,15 domain: 'customer service',16 capabilities: [17 'chat interaction',18 'information retrieval',19 'personalized responses'20 ],21 dataTypes: [22 'customer queries',23 'customer history',24 'product information'25 ]26 });27 28 console.log(`Risk Category: ${classification.riskCategory}`);29 console.log(`Confidence: ${classification.confidence}`);30 console.log('Reasons:');31 32 for (const reason of classification.reasons) {33 console.log(`- ${reason}`);34 }35 36 // Get compliance requirements based on risk category37 const requirements = await euAiActFramework.getRequirements(38 classification.riskCategory39 );40 41 return {42 classification,43 requirements44 };45}
Risk Categories Overview
Unacceptable Risk
AI systems that pose clear threats to safety, livelihoods, or rights. These are prohibited.
High Risk
AI systems used in critical infrastructure, education, employment, essential services, law enforcement, etc. Subject to strict requirements.
Limited Risk
AI systems with specific transparency obligations (e.g., chatbots, emotion recognition, deepfakes).
Minimal Risk
All other AI systems. Minimal obligations but voluntary adoption of codes of conduct encouraged.
Key Requirements
For high-risk AI systems, the EU AI Act mandates several key requirements, including:
- Risk Assessment and Mitigation: Systematic analysis of risks and implementation of mitigation measures
- High-Quality Data: Training data must be relevant, representative, and free from biases
- Technical Documentation: Comprehensive documentation of the system's design, capabilities, and limitations
- Record-Keeping: Detailed logs of the system's operation for accountability
- Transparency: Clear information for users about the system's capabilities and limitations
- Human Oversight: Effective oversight by humans to prevent or minimize risks
- Robustness and Accuracy: Systems must be technically robust and accurate
For a detailed implementation guide for the EU AI Act, see our comprehensive EU AI Act documentation.
GDPR for AI
The General Data Protection Regulation (GDPR) has significant implications for AI systems that process personal data. Our GDPR for AI framework helps you implement data protection principles specific to AI applications.
Data Minimization
GDPR requires that personal data processing is limited to what is necessary for the specified purpose. For AI systems, this means careful consideration of training data and inference inputs.
1// Example of GDPR DPIA generation2import { ComplianceFramework } from '@akioudai/safety-sdk';34// Initialize GDPR compliance framework5const gdprFramework = new ComplianceFramework({6 apiKey: process.env.AKIOUDAI_API_KEY,7 framework: 'gdpr'8});910// Generate a Data Protection Impact Assessment11async function generateDPIA(aiSystem) {12 const dataInventory = await gdprFramework.createDataInventory({13 dataTypes: aiSystem.dataTypes,14 dataSubjects: aiSystem.dataSubjects,15 processingPurposes: aiSystem.processingPurposes,16 retentionPeriods: aiSystem.retentionPeriods,17 dataRecipients: aiSystem.dataRecipients18 });19 20 // Analyze the necessity and proportionality21 const necessityAnalysis = await gdprFramework.analyzeNecessity({22 processingPurposes: aiSystem.processingPurposes,23 dataMinimization: aiSystem.dataMinimizationMeasures,24 alternatives: aiSystem.alternativeApproaches25 });26 27 // Identify and assess risks28 const riskAssessment = await gdprFramework.assessRisks({29 dataInventory,30 processingOperations: aiSystem.processingOperations,31 securityMeasures: aiSystem.securityMeasures32 });33 34 // Generate DPIA document35 const dpia = await gdprFramework.generateDPIA({36 systemName: aiSystem.name,37 systemDescription: aiSystem.description,38 dataController: aiSystem.dataController,39 dataInventory,40 necessityAnalysis,41 riskAssessment,42 mitigationMeasures: aiSystem.mitigationMeasures43 });44 45 return {46 dpia,47 riskScore: riskAssessment.overallScore,48 highRisks: riskAssessment.risks.filter(r => r.severity === 'high')49 };50}
Implementation Notes
- Conduct Data Protection Impact Assessments (DPIAs) for high-risk AI processing
- Implement technical measures to minimize data usage in training and inference
- Document your data minimization strategy and justification for data used
Transparency
GDPR requires transparency about how personal data is processed. For AI systems, this includes providing information about automated decision-making and the logic involved.
Transparency Requirements Checklist
For complete GDPR compliance guidance specific to AI systems, including practical implementation steps and documentation templates, see our GDPR for AI guide.
NIST AI RMF
The NIST AI Risk Management Framework is a voluntary framework developed by the National Institute of Standards and Technology (NIST) to help organizations address the risks of AI systems throughout their lifecycle.
Governance
The NIST AI RMF emphasizes the importance of AI governance as a foundation for managing AI risks. This includes organizational policies, processes, and practices for managing AI systems.
Governance Implementation Steps
Establish AI Governance Structure
Define roles, responsibilities, and decision-making authorities for AI systems
Develop AI Policies
Create comprehensive policies for AI development, deployment, and monitoring
Implement Risk Management Processes
Establish processes for identifying, assessing, and mitigating AI risks
Define Documentation Requirements
Establish documentation standards for AI systems throughout their lifecycle
Map Function
The Map function in the NIST AI RMF involves identifying and documenting the context, capabilities, and characteristics of AI systems. Our SDK provides tools to map your systems to the framework:
1// Example of NIST AI RMF mapping2import { ComplianceFramework } from '@akioudai/safety-sdk';34// Initialize NIST AI RMF compliance framework5const nistFramework = new ComplianceFramework({6 apiKey: process.env.AKIOUDAI_API_KEY,7 framework: 'nist_ai_rmf'8});910// Map AI system to NIST AI RMF11async function mapToNistFramework(aiSystem) {12 // Map function - identify context and AI model characteristics13 const mapResult = await nistFramework.mapSystem({14 systemName: aiSystem.name,15 description: aiSystem.description,16 useCase: aiSystem.useCase,17 modelType: aiSystem.modelType,18 deploymentEnvironment: aiSystem.deploymentEnvironment,19 stakeholders: aiSystem.stakeholders20 });21 22 // Measure function - identify risks and impacts23 const measureResult = await nistFramework.measureRisks({24 mapResult,25 dataSources: aiSystem.dataSources,26 modelCapabilities: aiSystem.capabilities,27 potentialImpacts: aiSystem.impacts28 });29 30 // Manage function - implement controls and governance31 const manageResult = await nistFramework.manageRisks({32 measureResult,33 proposedControls: aiSystem.controls,34 governance: aiSystem.governanceStructure,35 documentationLevel: aiSystem.documentationLevel36 });37 38 // Generate comprehensive report39 const report = await nistFramework.generateReport({40 mapResult,41 measureResult,42 manageResult43 });44 45 return {46 report,47 riskProfile: measureResult.riskProfile,48 controlGaps: manageResult.controlGaps,49 recommendations: manageResult.recommendations50 };51}
For comprehensive guidance on implementing the NIST AI RMF, including detailed documentation for each function (Map, Measure, Manage, Govern), see our NIST AI RMF implementation guide.
ISO/IEC 42001
ISO/IEC 42001 is an emerging international standard for AI management systems. It provides a framework for organizations to establish, implement, maintain, and continually improve their AI management system.
AI Management System
An AI management system under ISO/IEC 42001 includes policies, processes, and practices for governing AI systems in an organization.
1// Example of ISO/IEC 42001 implementation2import { ComplianceFramework } from '@akioudai/safety-sdk';34// Initialize ISO/IEC 42001 compliance framework5const isoFramework = new ComplianceFramework({6 apiKey: process.env.AKIOUDAI_API_KEY,7 framework: 'iso_42001'8});910// Implement ISO/IEC 42001 AI management system11async function implementIsoManagementSystem(organization) {12 // Context analysis13 const contextAnalysis = await isoFramework.analyzeContext({14 organization: organization.name,15 industry: organization.industry,16 aiActivities: organization.aiActivities,17 stakeholders: organization.stakeholders,18 externalFactors: organization.externalFactors19 });20 21 // Leadership and commitment22 const leadershipPlan = await isoFramework.developLeadershipPlan({23 roles: organization.roles,24 responsibilities: organization.responsibilities,25 resources: organization.resources,26 policies: organization.aiPolicies27 });28 29 // Planning30 const managementPlan = await isoFramework.createManagementPlan({31 contextAnalysis,32 leadershipPlan,33 objectives: organization.aiObjectives,34 risks: organization.identifiedRisks,35 opportunities: organization.opportunities36 });37 38 // Generate documentation39 const documentation = await isoFramework.generateDocumentation({40 contextAnalysis,41 leadershipPlan,42 managementPlan,43 processDiagrams: organization.processDiagrams,44 controlDescriptions: organization.controlDescriptions45 });46 47 // Pre-certification assessment48 const assessmentResult = await isoFramework.performAssessment({49 documentation,50 implementationEvidence: organization.implementationEvidence,51 performanceData: organization.performanceData52 });53 54 return {55 documentation,56 assessmentResult,57 readinessScore: assessmentResult.overallScore,58 gapAreas: assessmentResult.gaps,59 remediation: assessmentResult.remediationPlan60 };61}
Certification Process
Organizations seeking ISO/IEC 42001 certification must undergo an audit by an accredited certification body. Our framework helps you prepare for certification:
Certification Preparation Steps
Gap Analysis
Assess your current practices against ISO/IEC 42001 requirements
Documentation Development
Create required documentation for the AI management system
Implementation
Implement processes and controls according to the standard
Internal Audit
Perform internal audits to ensure readiness for certification
Certification Audit
Undergo external audit by an accredited certification body
For detailed guidance on implementing ISO/IEC 42001 and preparing for certification, see our ISO/IEC 42001 implementation guide.
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