Compliance Framework

ISO/IEC 42001 Implementation

Comprehensive implementation toolkit for establishing an ISO/IEC 42001-compliant AI management system with certification-ready documentation

Overview

ISO/IEC 42001 is the international standard for AI management systems (AIMS), providing a structured framework for organizations to manage AI-related risks and opportunities. Our implementation toolkit provides a comprehensive set of tools, templates, and guidance to establish, document, implement, and maintain an AI management system compliant with ISO/IEC 42001 requirements.

Standard Overview

ISO/IEC 42001 follows the high-level structure common to ISO management system standards, making it compatible with other standards like ISO 9001 (quality), ISO 27001 (information security), and ISO 31000 (risk management). The standard is designed to help organizations demonstrate responsible AI governance and establish trust with stakeholders.

ISO/IEC 42001 Structure

Context of the Organization

Leadership

Planning

Support

Operation

Performance Evaluation

Improvement

ISO/IEC 42001 follows the Plan-Do-Check-Act (PDCA) cycle common to ISO management system standards, providing a systematic approach to establishing, implementing, maintaining, and continually improving an AI management system.

Benefits of ISO/IEC 42001 Implementation

Risk Management

Systematic approach to identifying and managing AI-related risks

Stakeholder Trust

Demonstrate responsible AI governance to customers, regulators, and partners

Operational Efficiency

Streamlined processes and improved efficiency in AI development and deployment

Core ISO/IEC 42001 Components

Context of the Organization

Understanding organizational context and stakeholder requirements for AI management

Implementation Approaches:

Organizational context analysis

Stakeholder analysis and requirements

AI scope definition

Process mapping for AI activities

Management system boundaries

Implementation details

Leadership & Commitment

Establishing leadership responsibility and organizational roles for AI management

Implementation Approaches:

Leadership commitment documentation

AI policy development

Organizational roles and responsibilities

Authority and accountability definition

Communication strategy

Implementation details

Planning

Risk-based planning approach for AI management system objectives

Implementation Approaches:

AI risk assessment framework

Opportunity identification process

AI management objectives

Change management planning

Resource allocation

Implementation details

Support

Ensuring necessary resources, competence, and documentation

Implementation Approaches:

Resource requirements assessment

Competence development framework

AI awareness program

Documentation control system

Communication procedures

Implementation details

Operation

Operational planning, control, and lifecycle management for AI systems

Implementation Approaches:

Operational planning methodology

AI lifecycle management

Supply chain management

Change control process

Emergency preparedness

Implementation details

Performance Evaluation

Monitoring, measuring, and evaluating AI management effectiveness

Implementation Approaches:

Monitoring and measurement framework

AI performance metrics

Internal audit program

Management review process

Compliance evaluation

Implementation details

Improvement

Continuous improvement of the AI management system

Implementation Approaches:

Nonconformity handling process

Corrective action procedures

Continual improvement methodology

Feedback incorporation system

Innovation management

Implementation details

Context of the Organization

Understanding the organization's context, stakeholder requirements, and defining the scope of the AI management system.

Implementation Example

// ISO/IEC 42001 Context Analysis Implementation
import { ISOCompliance } from '@akioudai/safety-sdk';

// Initialize Context Analysis module
const contextAnalyzer = new ISOCompliance.ContextAnalyzer({
  apiKey: process.env.AKIOUDAI_API_KEY,
  organization: {
    name: 'Example Corp',
    industry: 'financial services',
    size: 'large enterprise',
    location: 'global operations'
  }
});

// Analyze internal and external context
const organizationalContext = await contextAnalyzer.analyzeContext({
  external: {
    regulatory: [
      'Financial regulations in operating jurisdictions',
      'AI-specific regulations (EU AI Act, etc.)',
      'Data protection regulations (GDPR, CCPA, etc.)'
    ],
    market: [
      'Competitive landscape',
      'Customer expectations for AI transparency',
      'Industry best practices for AI governance'
    ],
    technological: [
      'Evolving AI technologies and standards',
      'Cybersecurity threats',
      'Integration with legacy systems'
    ],
    social: [
      'Public perception of AI in financial services',
      'Ethical considerations for automated decision-making',
      'Demographic trends in customer base'
    ]
  },
  internal: {
    governance: [
      'Corporate governance structure',
      'Risk management framework',
      'Decision-making processes'
    ],
    capabilities: [
      'AI expertise and resources',
      'Technology infrastructure',
      'Data management maturity'
    ],
    culture: [
      'Organizational culture toward technology adoption',
      'Innovation mindset',
      'Risk appetite for AI implementation'
    ],
    strategy: [
      'Corporate strategy alignment with AI initiatives',
      'Digital transformation roadmap',
      'AI investment priorities'
    ]
  }
});

// Identify interested parties and their requirements
const stakeholderAnalysis = await contextAnalyzer.analyzeStakeholders({
  stakeholders: [
    {
      group: 'Customers',
      requirements: [
        'Fair and unbiased AI decisions',
        'Transparency in AI processes',
        'Protection of personal data'
      ],
      priority: 'high'
    },
    {
      group: 'Regulators',
      requirements: [
        'Compliance with applicable regulations',
        'Appropriate risk management',
        'Transparent reporting on AI systems'
      ],
      priority: 'high'
    },
    {
      group: 'Employees',
      requirements: [
        'Clear guidelines for AI development',
        'Training on AI governance',
        'Ethical use of AI technologies'
      ],
      priority: 'medium'
    },
    {
      group: 'Shareholders',
      requirements: [
        'Responsible AI innovation',
        'Risk mitigation for AI initiatives',
        'Value creation through AI technologies'
      ],
      priority: 'medium'
    }
  ]
});

// Define the scope of AI management system
const aimsScope = await contextAnalyzer.defineScope({
  included: [
    'Customer-facing AI systems',
    'Internal decision-making AI systems',
    'Risk assessment AI systems',
    'All stages of AI system lifecycle',
    'All locations where AI systems are developed or operated'
  ],
  excluded: [
    'Non-AI automated systems',
    'Third-party systems where no control exists',
    'Research-only AI prototypes not in production'
  ],
  boundaries: {
    organizational: 'All departments developing or operating AI systems',
    geographical: 'All global locations',
    technological: 'All production AI systems regardless of technology stack'
  }
});

// Generate context documentation
const contextDocumentation = await contextAnalyzer.generateDocumentation({
  context: organizationalContext,
  stakeholders: stakeholderAnalysis,
  scope: aimsScope,
  format: 'pdf'
});

console.log('Organizational context analyzed:', organizationalContext);
console.log('Stakeholder analysis completed:', stakeholderAnalysis);
console.log('AIMS scope defined:', aimsScope);
console.log('Context documentation generated:', contextDocumentation);

Key Implementation Activities

Analyze internal and external issues affecting the AIMS
Identify stakeholders and their requirements
Define the scope of the AI management system
Document organizational context
Establish processes needed for the AIMS

Documentation Requirements

Context analysis report
Stakeholder analysis and requirements register
AIMS scope statement
Process maps for AI-related activities
AIMS manual or equivalent documentation

Our Implementation Toolkit Provides

Context Analysis Templates

Structured templates for analyzing internal and external context

Stakeholder Analysis Tools

Methodologies and tools for comprehensive stakeholder analysis

Scope Definition Guidance

Guidance and templates for defining appropriate AIMS scope

Leadership and Commitment

Establishing top management commitment, defining AI policy, and assigning roles and responsibilities for the AI management system.

Implementation Example

// ISO/IEC 42001 Leadership Implementation
import { ISOCompliance } from '@akioudai/safety-sdk';

// Initialize Leadership module
const leadershipModule = new ISOCompliance.LeadershipModule({
  apiKey: process.env.AKIOUDAI_API_KEY,
  organization: {
    name: 'Example Corp',
    industry: 'financial services'
  }
});

// Define AI policy
const aiPolicy = await leadershipModule.createAIPolicy({
  purpose: 'To establish a framework for responsible and effective management of AI systems',
  commitments: [
    'Compliance with applicable laws and regulations',
    'Ethical and responsible development and use of AI',
    'Protection of individual rights and privacy',
    'Transparency and explainability of AI systems',
    'Continuous improvement of AI management practices'
  ],
  principles: [
    {
      name: 'Accountability',
      description: 'Clear ownership and responsibility for AI systems'
    },
    {
      name: 'Fairness',
      description: 'AI systems that avoid unfair bias and discrimination'
    },
    {
      name: 'Transparency',
      description: 'Explainable AI systems with appropriate disclosure'
    },
    {
      name: 'Privacy',
      description: 'Protection of personal data in AI systems'
    },
    {
      name: 'Security',
      description: 'Secure AI systems resistant to attacks and manipulation'
    }
  ],
  scope: 'All AI systems developed, implemented, or operated by Example Corp',
  approval: {
    approver: 'Board of Directors',
    date: '2023-11-15',
    version: '1.0'
  }
});

// Define roles and responsibilities
const rolesResponsibilities = await leadershipModule.defineRoles({
  executiveSponsorship: {
    role: 'Chief AI Officer',
    responsibilities: [
      'Overall accountability for the AI management system',
      'Ensuring adequate resources for the AIMS',
      'Reporting to the board on AI governance',
      'Approving major AI initiatives and policies'
    ]
  },
  aiManagementCommittee: {
    chair: 'Chief AI Officer',
    members: [
      'Chief Technology Officer',
      'Chief Risk Officer',
      'Chief Privacy Officer',
      'Head of AI Development',
      'Head of Data Science'
    ],
    responsibilities: [
      'Oversight of AIMS implementation',
      'Review of AI risks and opportunities',
      'Approval of AI management procedures',
      'Resource allocation for AI governance'
    ],
    meetingFrequency: 'Monthly'
  },
  aimsManager: {
    role: 'AI Governance Director',
    responsibilities: [
      'Day-to-day management of the AIMS',
      'Coordination of AIMS activities',
      'Monitoring AIMS performance',
      'Reporting to AI Management Committee'
    ],
    reportingLine: 'Chief AI Officer'
  },
  departmentalResponsibilities: [
    {
      department: 'AI Development',
      responsibilities: [
        'Implementing AIMS requirements in AI development',
        'Conducting risk assessments for new AI systems',
        'Documenting AI system design and testing'
      ]
    },
    {
      department: 'Legal and Compliance',
      responsibilities: [
        'Ensuring AI systems meet regulatory requirements',
        'Reviewing AI-related contracts and agreements',
        'Monitoring changes in AI regulations'
      ]
    },
    {
      department: 'Risk Management',
      responsibilities: [
        'Integrating AI risks into enterprise risk framework',
        'Conducting regular AI risk assessments',
        'Developing risk mitigation strategies'
      ]
    }
  ]
});

// Generate leadership documentation
const leadershipDocumentation = await leadershipModule.generateDocumentation({
  policy: aiPolicy,
  roles: rolesResponsibilities,
  format: 'pdf'
});

console.log('AI policy developed:', aiPolicy);
console.log('Roles and responsibilities defined:', rolesResponsibilities);
console.log('Leadership documentation generated:', leadershipDocumentation);

Key Leadership Responsibilities

Demonstrating commitment to the AIMS
Establishing AI policy and objectives
Ensuring integration of AIMS requirements into business processes
Providing resources for the AIMS
Promoting continual improvement

AI Policy Requirements

Appropriate to the organization's purpose
Commitment to satisfy applicable requirements
Commitment to continual improvement
Framework for setting AI objectives
Documented, communicated, and available to stakeholders

AI Governance Structure

Role/Group	Composition	Responsibilities	Accountability
Board of Directors	Board members, potentially with AI committee	Ultimate oversight, approval of AI policy, risk appetite	Shareholders, regulators
AI Executive Sponsor	CTO, CDO, or dedicated CAIO	Executive accountability, resource allocation, reporting to board	Board of Directors
AI Governance Committee	Cross-functional leaders (IT, Legal, Risk, Data, Business)	Oversight of AI management system, policy review, risk oversight	AI Executive Sponsor
AIMS Manager	Dedicated role with AI governance expertise	Day-to-day management of AIMS, coordination, monitoring, reporting	AI Governance Committee

Operation

Planning and controlling the operational processes needed for the AI management system, including AI lifecycle management.

Implementation Example

// ISO/IEC 42001 Operational Planning Implementation
import { ISOCompliance } from '@akioudai/safety-sdk';

// Initialize Operation module
const operationModule = new ISOCompliance.OperationModule({
  apiKey: process.env.AKIOUDAI_API_KEY,
  organization: {
    name: 'Example Corp',
    industry: 'financial services'
  }
});

// Define operational planning for AI systems
const operationalPlan = await operationModule.createOperationalPlan({
  aiLifecycleStages: [
    {
      stage: 'Planning and Requirements',
      processes: [
        {
          name: 'Requirements Analysis',
          description: 'Gathering and documenting requirements for AI systems',
          controlObjectives: [
            'Ensure regulatory compliance is considered in requirements',
            'Identify potential risks and ethical concerns early',
            'Document data requirements and quality criteria'
          ],
          procedures: ['AI_REQ_001', 'AI_REQ_002', 'AI_REQ_003'],
          responsible: 'Product Manager & AI Architect'
        },
        {
          name: 'Risk Assessment',
          description: 'Assessing risks associated with the planned AI system',
          controlObjectives: [
            'Identify and classify risks by type and severity',
            'Determine risk tolerance and mitigation requirements',
            'Document risk assessment findings'
          ],
          procedures: ['AI_RISK_001', 'AI_RISK_002'],
          responsible: 'Risk Management Team'
        }
      ]
    },
    {
      stage: 'Design and Development',
      processes: [
        {
          name: 'AI System Design',
          description: 'Designing AI system architecture and components',
          controlObjectives: [
            'Incorporate privacy-by-design principles',
            'Ensure explainability mechanisms are designed in',
            'Design for robustness and security'
          ],
          procedures: ['AI_DES_001', 'AI_DES_002', 'AI_DES_003'],
          responsible: 'AI Development Team'
        },
        {
          name: 'Data Management',
          description: 'Managing data for AI training and operation',
          controlObjectives: [
            'Ensure data quality and representativeness',
            'Implement data protection measures',
            'Maintain data provenance records'
          ],
          procedures: ['DATA_001', 'DATA_002', 'DATA_003'],
          responsible: 'Data Management Team'
        },
        {
          name: 'Development and Testing',
          description: 'Developing and testing AI system components',
          controlObjectives: [
            'Follow secure coding practices',
            'Test for performance, robustness, and bias',
            'Validate against requirements'
          ],
          procedures: ['AI_DEV_001', 'AI_TEST_001', 'AI_TEST_002'],
          responsible: 'AI Development Team & QA Team'
        }
      ]
    },
    {
      stage: 'Deployment and Operation',
      processes: [
        {
          name: 'Deployment Process',
          description: 'Deploying AI systems to production',
          controlObjectives: [
            'Verify pre-deployment checklist completion',
            'Ensure rollback procedures are in place',
            'Manage deployment risks'
          ],
          procedures: ['AI_DEPL_001', 'AI_DEPL_002'],
          responsible: 'DevOps Team'
        },
        {
          name: 'Monitoring and Maintenance',
          description: 'Ongoing monitoring and maintenance of AI systems',
          controlObjectives: [
            'Detect performance degradation or bias',
            'Monitor for security issues',
            'Ensure continued regulatory compliance'
          ],
          procedures: ['AI_MON_001', 'AI_MON_002', 'AI_MON_003'],
          responsible: 'AI Operations Team'
        }
      ]
    },
    {
      stage: 'Retirement',
      processes: [
        {
          name: 'System Retirement',
          description: 'Retiring AI systems when they reach end of life',
          controlObjectives: [
            'Ensure proper data handling during decommissioning',
            'Maintain records for compliance purposes',
            'Manage transitional risks to replacement systems'
          ],
          procedures: ['AI_RET_001', 'AI_RET_002'],
          responsible: 'AI Operations Team & Data Management Team'
        }
      ]
    }
  ],
  supplierManagement: {
    supplierCategories: [
      {
        category: 'AI Component Suppliers',
        controlRequirements: [
          'Compliance with our AI standards and policies',
          'Documentation of model characteristics and limitations',
          'Security and privacy controls',
          'Performance guarantees and SLAs'
        ],
        evaluationCriteria: [
          'Technical capability',
          'Security practices',
          'Regulatory compliance',
          'Support services'
        ]
      },
      {
        category: 'Data Suppliers',
        controlRequirements: [
          'Data quality standards compliance',
          'Data provenance documentation',
          'Legal right to use data',
          'Data protection measures'
        ],
        evaluationCriteria: [
          'Data quality metrics',
          'Privacy compliance',
          'Historical reliability',
          'Diversity and representativeness of data'
        ]
      }
    ],
    evaluationProcess: 'PROC_SUP_EVAL_001',
    onboardingProcess: 'PROC_SUP_ONB_001',
    monitoringProcess: 'PROC_SUP_MON_001'
  },
  changeManagement: {
    changeCategories: [
      {
        category: 'Model Updates',
        approvalRequirements: 'AI Management Committee for major updates, AI Governance Team for minor',
        testingRequirements: 'Full regression testing, bias assessment, and performance validation',
        documentationRequirements: 'Update all model cards, version control documentation, and change logs'
      },
      {
        category: 'Data Updates',
        approvalRequirements: 'Data Governance Team',
        testingRequirements: 'Data quality validation, distribution shift analysis',
        documentationRequirements: 'Data provenance, quality metrics, and change impact analysis'
      }
    ],
    emergencyChanges: {
      process: 'PROC_EMER_CHANGE_001',
      postImplementationReview: 'Required within 48 hours of emergency change'
    }
  }
});

// Generate operational documentation
const operationalDocumentation = await operationModule.generateDocumentation({
  operationalPlan: operationalPlan,
  procedureTemplates: true,
  workInstructions: true,
  format: 'pdf'
});

console.log('Operational plan developed:', operationalPlan);
console.log('Operational documentation generated:', operationalDocumentation);

Operational Planning Key Areas

AI lifecycle management
Operational criteria for processes
Control of operational processes
Management of change
Outsourced processes and supplier controls

AI Lifecycle Management

Planning and requirements
Design and development
Verification and validation
Deployment and operation
Monitoring and maintenance
Retirement and decommissioning

Our Implementation Toolkit Provides

Process Documentation Templates

Templates for documenting AI operational processes

Change Management Procedures

Procedures for managing changes to AI systems

Supplier Management Framework

Framework for managing AI suppliers and outsourced processes

Performance Evaluation

Monitoring, measuring, analyzing, and evaluating the performance of the AI management system.

Implementation Example

// ISO/IEC 42001 Performance Evaluation Implementation
import { ISOCompliance } from '@akioudai/safety-sdk';

// Initialize Performance Evaluation module
const evaluationModule = new ISOCompliance.EvaluationModule({
  apiKey: process.env.AKIOUDAI_API_KEY,
  organization: {
    name: 'Example Corp',
    industry: 'financial services'
  }
});

// Define monitoring and measurement framework
const monitoringFramework = await evaluationModule.createMonitoringFramework({
  monitoringCategories: [
    {
      category: 'AI System Performance',
      metrics: [
        {
          name: 'Accuracy Rate',
          description: 'Percentage of correct predictions or decisions',
          method: 'Automated performance testing',
          frequency: 'Weekly',
          target: '>95%',
          responseThreshold: '<90% triggers investigation'
        },
        {
          name: 'Bias Metrics',
          description: 'Statistical measures of fairness across protected attributes',
          method: 'Fairness testing framework',
          frequency: 'Monthly',
          target: 'Disparate impact ratio >0.8',
          responseThreshold: '<0.7 triggers remediation'
        },
        {
          name: 'Robustness Score',
          description: 'System performance under data perturbations or adversarial inputs',
          method: 'Robustness testing suite',
          frequency: 'Quarterly',
          target: '>90% maintenance of accuracy',
          responseThreshold: '<80% triggers review'
        }
      ]
    },
    {
      category: 'AIMS Process Performance',
      metrics: [
        {
          name: 'Risk Assessment Completion Rate',
          description: 'Percentage of AI systems with completed risk assessments',
          method: 'Documentation review',
          frequency: 'Quarterly',
          target: '100%',
          responseThreshold: '<95% triggers process review'
        },
        {
          name: 'Control Implementation Rate',
          description: 'Percentage of required controls implemented',
          method: 'Control testing',
          frequency: 'Quarterly',
          target: '100%',
          responseThreshold: '<90% triggers remediation'
        },
        {
          name: 'Incident Response Time',
          description: 'Average time to respond to AI incidents',
          method: 'Incident tracking system',
          frequency: 'Per incident and quarterly review',
          target: '<4 hours',
          responseThreshold: '>8 hours triggers process review'
        }
      ]
    },
    {
      category: 'Compliance Evaluation',
      metrics: [
        {
          name: 'Regulatory Compliance Rate',
          description: 'Percentage of applicable regulatory requirements met',
          method: 'Compliance audits',
          frequency: 'Semi-annually',
          target: '100%',
          responseThreshold: '<100% triggers immediate action'
        },
        {
          name: 'Policy Compliance Rate',
          description: 'Percentage of internal AI policy requirements met',
          method: 'Internal audits',
          frequency: 'Quarterly',
          target: '100%',
          responseThreshold: '<95% triggers remediation'
        }
      ]
    }
  ],
  monitoringTools: [
    {
      name: 'AI Performance Dashboard',
      description: 'Real-time visualization of key AI performance metrics',
      metrics: ['Accuracy Rate', 'Bias Metrics', 'Robustness Score'],
      users: ['AI Operations Team', 'AI Development Team', 'Management']
    },
    {
      name: 'AIMS Compliance Tracker',
      description: 'Tracking system for AIMS compliance requirements',
      metrics: ['Risk Assessment Completion Rate', 'Control Implementation Rate', 'Regulatory Compliance Rate', 'Policy Compliance Rate'],
      users: ['AI Governance Team', 'Compliance Team', 'Management']
    },
    {
      name: 'Incident Management System',
      description: 'System for tracking and managing AI incidents',
      metrics: ['Incident Response Time'],
      users: ['AI Operations Team', 'Incident Response Team', 'Management']
    }
  ],
  dataAnalysis: {
    methods: [
      'Statistical trend analysis',
      'Root cause analysis for incidents',
      'Correlation analysis between metrics',
      'Comparative benchmarking'
    ],
    responsibilities: {
      collection: 'AI Operations Team & AI Governance Team',
      analysis: 'AI Governance Team & Data Analysis Team',
      reporting: 'AI Governance Director'
    },
    tools: [
      'Statistical analysis software',
      'BI reporting platform',
      'Custom AI monitoring tools'
    ]
  }
});

// Define internal audit program
const auditProgram = await evaluationModule.createAuditProgram({
  auditTypes: [
    {
      type: 'AIMS Process Audits',
      scope: 'Evaluate conformity of AIMS processes to ISO/IEC 42001 requirements',
      methodology: 'Process review, document sampling, interviews',
      frequency: 'Annual for each process area',
      auditors: 'Internal audit team with AI governance training'
    },
    {
      type: 'AI System Audits',
      scope: 'Evaluate AI systems for compliance with AIMS requirements',
      methodology: 'System testing, code review, documentation review',
      frequency: 'Annual for high-risk systems, biennial for others',
      auditors: 'AI audit specialists'
    },
    {
      type: 'Compliance Audits',
      scope: 'Verify compliance with regulatory and legal requirements',
      methodology: 'Compliance checklist verification, documentation review',
      frequency: 'Semi-annual',
      auditors: 'Compliance team with legal support'
    }
  ],
  auditSchedule: {
    planningCycle: 'Annual with quarterly updates',
    prioritizationCriteria: [
      'Risk level of AI systems',
      'Results of previous audits',
      'Changes to systems or processes',
      'Regulatory requirements'
    ]
  },
  auditProcedures: {
    planning: 'PROC_AUDIT_PLAN_001',
    execution: 'PROC_AUDIT_EXEC_001',
    reporting: 'PROC_AUDIT_REP_001',
    followUp: 'PROC_AUDIT_FUP_001'
  },
  auditorRequirements: {
    qualifications: [
      'Understanding of ISO/IEC 42001 requirements',
      'Knowledge of AI technologies and risks',
      'Audit methodology training',
      'Independence from area being audited'
    ],
    training: 'Annual auditor training program on AI governance',
    evaluation: 'Annual competency assessment'
  }
});

// Define management review process
const managementReviewProcess = await evaluationModule.createManagementReviewProcess({
  frequency: 'Quarterly with annual comprehensive review',
  participants: [
    'Chief AI Officer (chair)',
    'AI Governance Director',
    'Chief Risk Officer',
    'Chief Technology Officer',
    'Business Unit Leaders',
    'AI Development Lead',
    'AI Operations Lead'
  ],
  inputs: [
    'Status of actions from previous reviews',
    'Changes in external and internal issues',
    'AIMS performance metrics',
    'Nonconformities and corrective actions',
    'Audit results',
    'Risk assessment results',
    'Opportunities for improvement'
  ],
  outputs: [
    'Improvement opportunities',
    'Changes needed to the AIMS',
    'Resource requirements',
    'Action items with owners and deadlines'
  ],
  documentation: {
    preparation: 'PROC_MGMT_REV_PREP_001',
    recordKeeping: 'PROC_MGMT_REV_REC_001',
    followUp: 'PROC_MGMT_REV_FUP_001'
  }
});

// Generate evaluation documentation
const evaluationDocumentation = await evaluationModule.generateDocumentation({
  monitoringFramework,
  auditProgram,
  managementReview: managementReviewProcess,
  format: 'pdf'
});

console.log('Monitoring framework developed:', monitoringFramework);
console.log('Audit program established:', auditProgram);
console.log('Management review process defined:', managementReviewProcess);
console.log('Evaluation documentation generated:', evaluationDocumentation);

Key Performance Evaluation Activities

Monitoring and measurement
Analysis and evaluation of results
Internal audits
Management review
Compliance evaluation

Performance Metrics Examples

AI system performance metrics
Process effectiveness indicators
Compliance metrics
Risk mitigation effectiveness
Stakeholder satisfaction metrics

Our Implementation Toolkit Provides

Monitoring Framework Templates

Templates for establishing monitoring frameworks

Audit Program Templates

Templates and procedures for internal audit programs

Management Review Guides

Guidelines and templates for effective management reviews

ISO/IEC 42001 Certification Path

Our implementation toolkit includes a comprehensive certification roadmap to guide you from initial assessment to successful certification.

Gap Assessment

• Current state assessment
• Gap analysis report
• Implementation roadmap

Implementation

• Process development
• Documentation creation
• Control implementation

Internal Audit

• Full system audit
• Nonconformity resolution
• Management review

Stage 1 Audit

• Documentation review
• Readiness assessment
• Stage 2 planning

Stage 2 Audit

• Implementation audit
• Certification decision
• Ongoing surveillance

Our certification toolkit includes all templates, checklists, and guidance needed for successful certification

Request Certification Toolkit

Additional Resources

ISO/IEC 42001 Template Library

Download comprehensive templates for ISO/IEC 42001 implementation, including policy templates, process documentation, and audit checklists.

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