Compliance Frameworks

AI Compliance Frameworks

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

Latest Draft (2023)

Comprehensive implementation guidance for the European Union's AI Act regulations

Key Elements:
Risk ClassificationTechnical RequirementsConformity AssessmentImplementation Toolkit
Relevant for:Companies offering AI systems in the EU market
Key approach:Risk-based approach to AI regulation

GDPR for AI

Established (2018)

Application of General Data Protection Regulation principles to AI systems and data processing

Key Elements:
Data MinimizationPurpose LimitationTransparency RequirementsData Subject Rights
Relevant for:Any AI system processing personal data of EU residents
Key approach:Data protection principles applied to AI

NIST AI RMF

Published (January 2023)

Implementation of the NIST AI Risk Management Framework for AI systems

Key Elements:
GovernanceMap FunctionMeasure FunctionManage Function
Relevant for:US organizations, global enterprises seeking structured AI governance
Key approach:Voluntary framework for AI risk management

ISO/IEC 42001

Under Development

Implementation of the ISO/IEC 42001 standard for AI management systems

Key Elements:
AI Management SystemDocumentation RequirementsOperational ControlsCertification Process
Relevant for:Organizations seeking certification and structured AI governance
Key approach:Management system standard for AI

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.

EU AI Act Risk Classification
1// Example of risk classification API usage
2import { ComplianceFramework } from '@akioudai/safety-sdk';
3
4// Initialize EU AI Act compliance framework
5const euAiActFramework = new ComplianceFramework({
6 apiKey: process.env.AKIOUDAI_API_KEY,
7 framework: 'eu_ai_act'
8});
9
10// Function to classify AI system risk level
11async 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 category
37 const requirements = await euAiActFramework.getRequirements(
38 classification.riskCategory
39 );
40
41 return {
42 classification,
43 requirements
44 };
45}
javascript

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.

GDPR Data Protection Impact Assessment
1// Example of GDPR DPIA generation
2import { ComplianceFramework } from '@akioudai/safety-sdk';
3
4// Initialize GDPR compliance framework
5const gdprFramework = new ComplianceFramework({
6 apiKey: process.env.AKIOUDAI_API_KEY,
7 framework: 'gdpr'
8});
9
10// Generate a Data Protection Impact Assessment
11async 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.dataRecipients
18 });
19
20 // Analyze the necessity and proportionality
21 const necessityAnalysis = await gdprFramework.analyzeNecessity({
22 processingPurposes: aiSystem.processingPurposes,
23 dataMinimization: aiSystem.dataMinimizationMeasures,
24 alternatives: aiSystem.alternativeApproaches
25 });
26
27 // Identify and assess risks
28 const riskAssessment = await gdprFramework.assessRisks({
29 dataInventory,
30 processingOperations: aiSystem.processingOperations,
31 securityMeasures: aiSystem.securityMeasures
32 });
33
34 // Generate DPIA document
35 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.mitigationMeasures
43 });
44
45 return {
46 dpia,
47 riskScore: riskAssessment.overallScore,
48 highRisks: riskAssessment.risks.filter(r => r.severity === 'high')
49 };
50}
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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

Clear information about AI processing in privacy notices
Explanation of automated decision-making logic in understandable terms
Information about the significance and consequences of AI processing
Mechanism for exercising rights related to automated decisions
Documentation of AI model capabilities and limitations

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

1
Establish AI Governance Structure

Define roles, responsibilities, and decision-making authorities for AI systems

2
Develop AI Policies

Create comprehensive policies for AI development, deployment, and monitoring

3
Implement Risk Management Processes

Establish processes for identifying, assessing, and mitigating AI risks

4
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:

NIST AI RMF Mapping
1// Example of NIST AI RMF mapping
2import { ComplianceFramework } from '@akioudai/safety-sdk';
3
4// Initialize NIST AI RMF compliance framework
5const nistFramework = new ComplianceFramework({
6 apiKey: process.env.AKIOUDAI_API_KEY,
7 framework: 'nist_ai_rmf'
8});
9
10// Map AI system to NIST AI RMF
11async function mapToNistFramework(aiSystem) {
12 // Map function - identify context and AI model characteristics
13 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.stakeholders
20 });
21
22 // Measure function - identify risks and impacts
23 const measureResult = await nistFramework.measureRisks({
24 mapResult,
25 dataSources: aiSystem.dataSources,
26 modelCapabilities: aiSystem.capabilities,
27 potentialImpacts: aiSystem.impacts
28 });
29
30 // Manage function - implement controls and governance
31 const manageResult = await nistFramework.manageRisks({
32 measureResult,
33 proposedControls: aiSystem.controls,
34 governance: aiSystem.governanceStructure,
35 documentationLevel: aiSystem.documentationLevel
36 });
37
38 // Generate comprehensive report
39 const report = await nistFramework.generateReport({
40 mapResult,
41 measureResult,
42 manageResult
43 });
44
45 return {
46 report,
47 riskProfile: measureResult.riskProfile,
48 controlGaps: manageResult.controlGaps,
49 recommendations: manageResult.recommendations
50 };
51}
javascript

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.

ISO/IEC 42001 Implementation
1// Example of ISO/IEC 42001 implementation
2import { ComplianceFramework } from '@akioudai/safety-sdk';
3
4// Initialize ISO/IEC 42001 compliance framework
5const isoFramework = new ComplianceFramework({
6 apiKey: process.env.AKIOUDAI_API_KEY,
7 framework: 'iso_42001'
8});
9
10// Implement ISO/IEC 42001 AI management system
11async function implementIsoManagementSystem(organization) {
12 // Context analysis
13 const contextAnalysis = await isoFramework.analyzeContext({
14 organization: organization.name,
15 industry: organization.industry,
16 aiActivities: organization.aiActivities,
17 stakeholders: organization.stakeholders,
18 externalFactors: organization.externalFactors
19 });
20
21 // Leadership and commitment
22 const leadershipPlan = await isoFramework.developLeadershipPlan({
23 roles: organization.roles,
24 responsibilities: organization.responsibilities,
25 resources: organization.resources,
26 policies: organization.aiPolicies
27 });
28
29 // Planning
30 const managementPlan = await isoFramework.createManagementPlan({
31 contextAnalysis,
32 leadershipPlan,
33 objectives: organization.aiObjectives,
34 risks: organization.identifiedRisks,
35 opportunities: organization.opportunities
36 });
37
38 // Generate documentation
39 const documentation = await isoFramework.generateDocumentation({
40 contextAnalysis,
41 leadershipPlan,
42 managementPlan,
43 processDiagrams: organization.processDiagrams,
44 controlDescriptions: organization.controlDescriptions
45 });
46
47 // Pre-certification assessment
48 const assessmentResult = await isoFramework.performAssessment({
49 documentation,
50 implementationEvidence: organization.implementationEvidence,
51 performanceData: organization.performanceData
52 });
53
54 return {
55 documentation,
56 assessmentResult,
57 readinessScore: assessmentResult.overallScore,
58 gapAreas: assessmentResult.gaps,
59 remediation: assessmentResult.remediationPlan
60 };
61}
javascript

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

1
Gap Analysis

Assess your current practices against ISO/IEC 42001 requirements

2
Documentation Development

Create required documentation for the AI management system

3
Implementation

Implement processes and controls according to the standard

4
Internal Audit

Perform internal audits to ensure readiness for certification

5
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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