Control Implementation Checklist

This comprehensive checklist maps all ISO 42001 Annex A controls to practical implementation steps. Use this as your roadmap for implementing a complete AI management system.

How to Use This Checklist

1. Assessment: Check your current state for each control
2. Prioritization: Focus on high-risk areas and mandatory controls first
3. Planning: Create implementation plan with timelines and owners
4. Execution: Implement controls systematically
5. Validation: Verify each control is working effectively
6. Documentation: Document implementation and evidence
7. Monitoring: Continuously monitor control effectiveness
8. Improvement: Refine and enhance based on experience

Control Status Legend

• ☐ Not Started: Control not yet implemented
• 🔄 In Progress: Implementation underway
• ✅ Implemented: Control in place
• ✓ Verified: Control tested and validated
• 🔍 Audit Ready: Documentation complete

ISO 42001 ANNEX A CONTROLS

A.1 AI SYSTEM INVENTORY

Objective: Maintain comprehensive inventory of all AI systems

A.1.1 AI System Identification and Documentation

Requirements: ☐ Inventory of all AI systems established ☐ Each AI system has unique identifier ☐ System owner identified for each AI system ☐ AI system classification (risk level) ☐ Inventory updated regularly

Implementation Steps:

1. Create Inventory Framework ☐ Define what constitutes an "AI system" in your organization ☐ Create inventory template with required fields ☐ Establish unique naming/numbering convention ☐ Set up inventory management system (spreadsheet, database, or tool)

2. Identify All AI Systems ☐ Survey all departments for AI systems ☐ Include deployed, in development, and pilot systems ☐ Include third-party AI services ☐ Document shadow AI (unauthorized AI use)

3. Document Each System ☐ System name and identifier ☐ Description and purpose ☐ Business owner and technical owner ☐ Risk classification (low/medium/high) ☐ Deployment status (dev/staging/production/retired) ☐ User base (internal/external, volume) ☐ Data sources and types ☐ Technology stack ☐ Regulatory applicability (EU AI Act, etc.) ☐ Integration points ☐ Deployment date and version

4. Establish Update Process ☐ Define update frequency (monthly recommended) ☐ Assign responsibility for updates ☐ Create change notification process ☐ Implement version control ☐ Archive retired systems

Inventory Template:

AI System ID: [AI-SYS-001]
System Name: [Customer Churn Prediction Model]
Business Owner: [Name, Department]
Technical Owner: [Name, Team]
Risk Level: [High/Medium/Low]
Status: [Production]
Description: [Brief description]
Purpose: [Business purpose]
Users: [Internal - Sales team, ~200 users]
Data Sources: [CRM, Transaction DB]
Data Types: [Customer data, PII]
Technology: [Python, XGBoost, AWS SageMaker]
Regulations: [GDPR, EU AI Act - High Risk]
Deployed: [2024-06-15]
Version: [2.1.0]
Last Review: [2025-12-01]
Next Review: [2026-03-01]

Evidence: ☐ AI System Inventory document ☐ Regular update logs ☐ Owner acknowledgments

ISO 42001 Reference: Clause 4.4, A.1

A.2 DATA GOVERNANCE

Objective: Ensure high-quality, compliant data management for AI

A.2.1 Data Management Framework

Requirements: ☐ Data governance framework established ☐ Data quality requirements defined ☐ Data roles and responsibilities assigned ☐ Data policies and procedures documented

Implementation Steps:

1. Establish Data Governance Organization ☐ Appoint Chief Data Officer or equivalent ☐ Create Data Governance Board ☐ Assign Data Stewards for key domains ☐ Define Data Engineer roles ☐ Establish Data Quality team

2. Define Data Quality Standards ☐ Accuracy requirements (target: >95%) ☐ Completeness requirements (target: <5% missing) ☐ Consistency requirements (100%) ☐ Timeliness requirements (define by use case) ☐ Validity requirements (100% schema compliance)

3. Create Data Policies ☐ Data quality policy ☐ Data access policy ☐ Data retention and deletion policy ☐ Data classification policy ☐ Data sharing policy

4. Implement Data Quality Processes ☐ Data profiling procedures ☐ Data validation rules ☐ Data quality monitoring ☐ Data quality issue resolution ☐ Data quality reporting

Evidence: ☐ Data Governance Framework document ☐ Data quality standards ☐ Data policies ☐ Organizational chart with data roles

A.2.2 Data Provenance and Lineage

Requirements: ☐ Data sources documented ☐ Data lineage tracked ☐ Data transformations recorded ☐ Data versioning implemented

Implementation Steps:

1. Document Data Sources ☐ Identify all data sources ☐ Document collection methods ☐ Record legal basis for collection ☐ Track source system owners

2. Implement Lineage Tracking ☐ Select lineage tool (Apache Atlas, DataHub, etc.) ☐ Configure automated lineage capture ☐ Document manual processes ☐ Track data transformations ☐ Record data consumers

3. Version Control ☐ Implement data versioning system (DVC, etc.) ☐ Tag datasets with versions ☐ Document version changes ☐ Maintain version history

Evidence: ☐ Data lineage diagrams ☐ Data source documentation ☐ Versioned datasets

A.2.3 Data Cataloging

Requirements: ☐ Data catalog established ☐ Metadata managed ☐ Data discoverable ☐ Data usage tracked

Implementation Steps:

1. Deploy Data Catalog ☐ Select catalog tool (Alation, Collibra, DataHub, etc.) ☐ Configure catalog infrastructure ☐ Define metadata standards ☐ Train users on catalog

2. Catalog All Datasets ☐ Add datasets to catalog ☐ Document schema and structure ☐ Add business descriptions ☐ Tag with classifications ☐ Link to lineage ☐ Record quality metrics

3. Maintain Catalog ☐ Regular updates process ☐ User feedback mechanism ☐ Usage analytics ☐ Quality improvements

Evidence: ☐ Data catalog with entries for all AI datasets ☐ Catalog usage reports ☐ User training records

A.2.4 Data Access Controls

Requirements: ☐ Access controls implemented ☐ Least privilege principle applied ☐ Access regularly reviewed ☐ Access audit logs maintained

Implementation Steps:

1. Classify Data ☐ Define classification levels (Public, Internal, Confidential, Restricted) ☐ Classify all datasets ☐ Document classification rationale

2. Implement Access Controls ☐ Role-Based Access Control (RBAC) ☐ Authentication (MFA required) ☐ Authorization rules ☐ Encryption (in transit and at rest) ☐ Data masking for non-production

3. Access Request Process ☐ Define request procedure ☐ Approval workflow ☐ Time-limited access ☐ Access recertification (quarterly)

4. Monitor and Audit ☐ Access logging enabled ☐ Anomaly detection ☐ Regular access reviews ☐ Compliance reporting

Evidence: ☐ Data classification matrix ☐ Access control policy ☐ Access logs ☐ Access review reports

ISO 42001 Reference: A.2

A.3 TRAINING DATA MANAGEMENT

Objective: Ensure training data is representative, unbiased, and high-quality

A.3.1 Training Data Selection and Quality

Requirements: ☐ Training data selection criteria defined ☐ Data quality validated ☐ Representativeness assessed ☐ Sample size justified

Implementation Steps:

1. Define Selection Criteria ☐ Relevance to problem ☐ Recency requirements ☐ Quality thresholds ☐ Representativeness requirements ☐ Sufficient volume

2. Validate Data Quality ☐ Accuracy checks ☐ Completeness verification ☐ Consistency validation ☐ Outlier detection ☐ Noise assessment

3. Assess Representativeness ☐ Demographic distribution analysis ☐ Comparison to target population ☐ Edge case coverage ☐ Class balance evaluation

4. Document Training Data ☐ Create Datasheet for Dataset ☐ Document collection methodology ☐ Record known limitations ☐ Specify recommended uses

Evidence: ☐ Training data selection documentation ☐ Data quality reports ☐ Representativeness analysis ☐ Datasheets for datasets

A.3.2 Bias Identification and Mitigation

Requirements: ☐ Bias assessment performed ☐ Historical bias identified ☐ Sampling bias evaluated ☐ Mitigation strategies implemented

Implementation Steps:

1. Identify Potential Biases ☐ Historical bias (data reflects past discrimination) ☐ Representation bias (underrepresented groups) ☐ Measurement bias (measurement method biased) ☐ Aggregation bias (inappropriate aggregation) ☐ Label bias (biased labeling)

2. Quantify Bias ☐ Demographic analysis of training data ☐ Statistical disparity assessment ☐ Proxy variable analysis ☐ Correlation with protected attributes

3. Implement Mitigation ☐ Re-sampling (over/under-sampling) ☐ Re-weighting samples ☐ Synthetic data generation ☐ Improved data collection ☐ Feature engineering

4. Validate Mitigation ☐ Re-assess bias after mitigation ☐ Verify fairness improvements ☐ Check for performance trade-offs ☐ Document mitigation effectiveness

Evidence: ☐ Bias assessment reports ☐ Mitigation strategy documentation ☐ Post-mitigation validation results

A.3.3 Training Data Versioning and Traceability

Requirements: ☐ Training datasets versioned ☐ Lineage tracked ☐ Changes documented ☐ Reproducibility ensured

Implementation Steps:

1. Implement Versioning ☐ Use data versioning tool (DVC, etc.) ☐ Semantic versioning for datasets ☐ Immutable dataset storage ☐ Version tags with metadata

2. Track Lineage ☐ Document data sources ☐ Record transformations ☐ Link to model versions ☐ Maintain audit trail

3. Ensure Reproducibility ☐ Snapshot datasets at model training time ☐ Document preprocessing steps ☐ Version transformation code ☐ Record random seeds

Evidence: ☐ Versioned training datasets ☐ Lineage documentation ☐ Reproducibility validation

ISO 42001 Reference: A.3

A.4 MODEL DEVELOPMENT

Objective: Develop AI models with appropriate controls and documentation

A.4.1 Model Design and Selection

Requirements: ☐ Model design documented ☐ Algorithm selection justified ☐ Alternatives considered ☐ Design approved

Implementation Steps:

1. Document Design ☐ Create Model Design Document ☐ Define problem formulation ☐ Specify input/output ☐ Define success metrics ☐ Identify constraints

2. Select Algorithm ☐ Evaluate multiple algorithms ☐ Consider interpretability requirements ☐ Assess performance requirements ☐ Evaluate resource constraints ☐ Document selection rationale

3. Design Review ☐ Peer review of design ☐ Domain expert review ☐ Security review ☐ Privacy review ☐ Approval from technical lead

Evidence: ☐ Model Design Documents ☐ Algorithm comparison analysis ☐ Design review approvals

A.4.2 Model Training and Optimization

Requirements: ☐ Reproducible training process ☐ Experiments tracked ☐ Hyperparameters optimized ☐ Overfitting prevented

Implementation Steps:

1. Set Up Development Environment ☐ Secure development infrastructure ☐ Version control (Git) ☐ Experiment tracking (MLflow, W&B, etc.) ☐ Development guidelines documented

2. Implement Reproducibility ☐ Set random seeds ☐ Lock dependencies ☐ Version code, data, and models ☐ Document environment

3. Track Experiments ☐ Log all experiments ☐ Record hyperparameters ☐ Track metrics ☐ Save artifacts ☐ Enable comparison

4. Optimize Model ☐ Define search space ☐ Use systematic optimization (grid, random, Bayesian) ☐ Validate on separate set ☐ Apply early stopping ☐ Prevent overfitting

Evidence: ☐ Training code in version control ☐ Experiment tracking logs ☐ Reproducibility validation

A.4.3 Model Explainability

Requirements: ☐ Explainability mechanisms implemented ☐ Global interpretability provided ☐ Local explanations available ☐ Explanations validated

Implementation Steps:

1. Implement Global Explainability ☐ Feature importance calculated ☐ SHAP summary plots ☐ Partial dependence plots ☐ Model behavior documentation

2. Implement Local Explainability ☐ SHAP values for individual predictions ☐ LIME or equivalent ☐ Counterfactual explanations ☐ Example-based explanations

3. Create User-Facing Explanations ☐ Plain language templates ☐ Visualizations ☐ Confidence indicators ☐ Uncertainty communication

4. Validate Explanations ☐ Fidelity testing (explanations match model) ☐ Consistency testing ☐ User comprehension testing ☐ Domain expert review

Evidence: ☐ Explainability implementation code ☐ Example explanations ☐ User testing results ☐ Validation reports

A.4.4 Model Documentation

Requirements: ☐ Model cards created ☐ Technical documentation complete ☐ Limitations documented ☐ Usage guidelines provided

Implementation Steps:

1. Create Model Card ☐ Model details section ☐ Intended use section ☐ Training data section ☐ Performance metrics ☐ Fairness analysis ☐ Ethical considerations ☐ Limitations ☐ Recommendations

2. Technical Documentation ☐ Architecture details ☐ Hyperparameters ☐ Training procedure ☐ Code references ☐ Dependencies

3. Usage Guidelines ☐ Intended use cases ☐ Out-of-scope uses ☐ User requirements ☐ Input specifications ☐ Output interpretation

Evidence: ☐ Model cards for all production models ☐ Technical documentation ☐ Usage guidelines

ISO 42001 Reference: A.4

A.5 MODEL EVALUATION AND VALIDATION

Objective: Verify models meet performance, fairness, and robustness requirements

A.5.1 Model Testing

Requirements: ☐ Comprehensive testing performed ☐ Test set evaluation completed ☐ Cross-validation conducted ☐ Test results documented

Implementation Steps:

1. Performance Testing ☐ Hold-out test set evaluation ☐ Primary metric calculated ☐ Secondary metrics calculated ☐ Statistical significance tested ☐ Confidence intervals calculated ☐ Comparison to baseline

2. Cross-Validation ☐ K-fold cross-validation (K=5 or 10) ☐ Performance across folds ☐ Stability assessment ☐ Mean and std dev calculated

3. Temporal Validation ☐ Performance over time periods ☐ Recent vs historical data ☐ Trend analysis

4. Segment Analysis ☐ Performance by customer segment ☐ Performance by product category ☐ Performance by region ☐ No unacceptable degradation

Evidence: ☐ Test results reports ☐ Cross-validation results ☐ Segment performance analysis

A.5.2 Independent Validation

Requirements: ☐ Independent validation for high-risk systems ☐ Validation team separate from development ☐ Validation report created ☐ Issues identified and addressed

Implementation Steps:

1. Establish Validation Team ☐ Independent from development (required) ☐ ML engineering expertise ☐ Domain expertise ☐ Fairness expertise

2. Conduct Validation ☐ Review design and implementation ☐ Re-run tests independently ☐ Validate data quality ☐ Assess fairness ☐ Test robustness ☐ Review documentation

3. Document Findings ☐ Validation report created ☐ Issues identified ☐ Recommendations provided ☐ Acceptance recommendation

4. Address Issues ☐ Remediation plan for issues ☐ Re-validation if needed ☐ Final approval

Evidence: ☐ Validation reports ☐ Independence attestation ☐ Remediation documentation

A.5.3 Performance Assessment

Requirements: ☐ Performance metrics appropriate ☐ Acceptance criteria defined ☐ Performance meets requirements ☐ Performance across groups validated

Implementation Steps:

1. Define Metrics ☐ Task-appropriate metrics selected ☐ Business-relevant metrics included ☐ Baseline performance established

2. Set Acceptance Criteria ☐ Minimum performance thresholds ☐ Comparison to baseline ☐ Statistical significance requirements ☐ Fairness thresholds

3. Evaluate Performance ☐ Calculate all metrics ☐ Compare to acceptance criteria ☐ Assess statistical significance ☐ Evaluate across demographics

4. Document Results ☐ Performance summary ☐ Comparison to requirements ☐ Recommendation (approve/reject)

Evidence: ☐ Performance evaluation reports ☐ Acceptance criteria documentation ☐ Approval decisions

A.5.4 Fairness Evaluation

Requirements: ☐ Fairness metrics defined ☐ Performance across groups assessed ☐ Fairness thresholds met ☐ Bias mitigation implemented if needed

Implementation Steps:

1. Define Fairness Metrics ☐ Demographic parity ☐ Equal opportunity ☐ Equalized odds ☐ Calibration ☐ Predictive parity

2. Assess Fairness ☐ Performance by protected groups ☐ Calculate fairness metrics ☐ Identify disparities ☐ Analyze root causes

3. Implement Mitigation (if needed) ☐ Re-sampling ☐ Re-weighting ☐ Fairness constraints ☐ Post-processing calibration

4. Validate Mitigation ☐ Re-assess fairness ☐ Verify improvements ☐ Check performance impact ☐ Document results

5. Set Thresholds ☐ Maximum disparity: [5%] recommended ☐ Document threshold rationale ☐ Verify compliance

Evidence: ☐ Fairness evaluation reports ☐ Group performance comparisons ☐ Mitigation documentation (if applicable) ☐ Threshold compliance verification

ISO 42001 Reference: A.5

A.6 DEPLOYMENT AND USE

Objective: Deploy AI systems safely with appropriate controls

A.6.1 Deployment Planning and Control

Requirements: ☐ Deployment plan created ☐ Readiness assessment completed ☐ Approval obtained ☐ Phased deployment strategy

Implementation Steps:

1. Create Deployment Plan ☐ Deployment strategy selected (shadow/canary/blue-green) ☐ Timeline and milestones ☐ Resource requirements ☐ Rollback procedures ☐ Success criteria

2. Readiness Assessment ☐ Complete deployment readiness checklist ☐ Technical readiness verified ☐ Governance readiness confirmed ☐ Documentation complete ☐ Training completed

3. Obtain Approvals ☐ Low-risk: Technical Lead ☐ Medium-risk: Department Head ☐ High-risk: AI Governance Board

4. Execute Deployment ☐ Follow deployment plan ☐ Monitor closely during rollout ☐ Validate at each phase ☐ Address issues promptly

Evidence: ☐ Deployment plans ☐ Readiness assessments ☐ Approval documentation ☐ Deployment logs

A.6.2 User Training and Awareness

Requirements: ☐ User training completed ☐ Usage guidelines provided ☐ Limitations communicated ☐ Feedback mechanisms established

Implementation Steps:

1. Develop Training Materials ☐ User guides ☐ Video tutorials ☐ FAQs ☐ Quick reference guides

2. Conduct Training ☐ Training sessions ☐ Hands-on practice ☐ Q&A sessions ☐ Competency assessment

3. Communicate Limitations ☐ Clear documentation of limitations ☐ Out-of-scope uses identified ☐ Edge cases explained ☐ When to escalate

4. Establish Feedback ☐ Feedback channels ☐ Issue reporting process ☐ Feature requests ☐ User satisfaction surveys

Evidence: ☐ Training materials ☐ Training completion records ☐ User guides ☐ Feedback mechanisms

A.6.3 Operational Procedures

Requirements: ☐ Standard Operating Procedures (SOPs) documented ☐ Human oversight mechanisms implemented ☐ Override procedures defined ☐ Escalation processes established

Implementation Steps:

1. Document SOPs ☐ Daily operations procedures ☐ Weekly review procedures ☐ Monthly assessment procedures ☐ Incident response procedures

2. Implement Human Oversight ☐ Define oversight model (HITL/HOTL/HOOTL) ☐ Implement oversight interfaces ☐ Train operators ☐ Monitor oversight effectiveness

3. Define Override Procedures ☐ Override authority matrix ☐ Override process documented ☐ Documentation requirements ☐ Override monitoring

4. Establish Escalation ☐ Escalation levels defined ☐ Escalation triggers identified ☐ Escalation procedures documented ☐ Response time SLAs

Evidence: ☐ Standard Operating Procedures ☐ Human oversight implementation ☐ Override and escalation procedures ☐ Runbooks

A.6.4 Change Management

Requirements: ☐ Change management process defined ☐ Changes approved before implementation ☐ Impact assessment performed ☐ Changes documented

Implementation Steps:

1. Define Change Process ☐ Change request procedure ☐ Impact assessment requirements ☐ Testing requirements ☐ Approval workflow ☐ Communication plan

2. Implement Change Controls ☐ Change request system ☐ Change advisory board (for major changes) ☐ Testing environments ☐ Rollback procedures

3. Document Changes ☐ Change log maintained ☐ Configuration management database (CMDB) ☐ Version control ☐ Release notes

Evidence: ☐ Change management procedure ☐ Change requests and approvals ☐ Change log ☐ Release documentation

ISO 42001 Reference: A.6

A.7 MONITORING AND CONTINUAL IMPROVEMENT

Objective: Ensure ongoing performance and continuous improvement

A.7.1 Performance Monitoring

Requirements: ☐ Continuous performance monitoring ☐ Metrics tracked and trended ☐ Dashboards created ☐ Alerts configured

Implementation Steps:

1. Define Monitoring Metrics ☐ Performance metrics (accuracy, precision, recall, etc.) ☐ System metrics (latency, throughput, error rate) ☐ Business metrics (user satisfaction, business impact) ☐ Fairness metrics

2. Implement Monitoring Infrastructure ☐ Monitoring tools deployed ☐ Data collection configured ☐ Dashboards created ☐ Alerting rules defined

3. Set Alert Thresholds ☐ Critical: Performance <[90%], Error rate >[1%] ☐ High: Performance <[92%], Latency >[500ms] ☐ Medium: Performance <[94%] ☐ Review and adjust based on experience

4. Monitor Continuously ☐ Real-time monitoring ☐ Daily reviews ☐ Weekly trend analysis ☐ Monthly comprehensive reviews

Evidence: ☐ Monitoring dashboards ☐ Alert configurations ☐ Monitoring reports ☐ Alert response logs

A.7.2 Data Quality Monitoring

Requirements: ☐ Input data quality monitored ☐ Data drift detected ☐ Quality issues addressed ☐ Data pipeline health tracked

Implementation Steps:

1. Implement Data Quality Checks ☐ Schema validation ☐ Null rate monitoring ☐ Range validation ☐ Type validation ☐ Outlier detection

2. Monitor Data Drift ☐ Distribution monitoring ☐ PSI calculation (Population Stability Index) ☐ Drift alerts (threshold: PSI > 0.2) ☐ Root cause analysis for drift

3. Track Data Pipeline Health ☐ Pipeline execution monitoring ☐ Data freshness checks ☐ Volume monitoring ☐ Error rate tracking

Evidence: ☐ Data quality dashboards ☐ Drift detection reports ☐ Data pipeline monitoring ☐ Issue resolution logs

A.7.3 Bias and Fairness Monitoring

Requirements: ☐ Fairness metrics monitored continuously ☐ Bias drift detected ☐ Fairness issues addressed promptly ☐ Regular fairness audits

Implementation Steps:

1. Implement Fairness Monitoring ☐ Continuous fairness metric calculation ☐ Demographic group performance tracking ☐ Disparate impact monitoring ☐ Fairness dashboards

2. Set Fairness Thresholds ☐ Maximum disparity: [5%] across groups ☐ Alert if threshold exceeded ☐ Investigation required for violations

3. Conduct Regular Audits ☐ High-risk systems: Quarterly fairness audit ☐ Medium-risk systems: Semi-annual audit ☐ Low-risk systems: Annual audit ☐ Document findings and actions

4. Address Fairness Issues ☐ Investigation procedure ☐ Remediation plan ☐ Re-validation ☐ Communication to stakeholders

Evidence: ☐ Fairness monitoring dashboards ☐ Audit reports ☐ Remediation documentation

A.7.4 Incident Management

Requirements: ☐ Incident detection and reporting ☐ Investigation and remediation ☐ Post-incident reviews ☐ Lessons learned captured

Implementation Steps:

1. Establish Incident Process ☐ Incident classification (P0-P3) ☐ Reporting procedures ☐ Investigation procedures ☐ Response procedures ☐ Communication procedures

2. Implement Incident Detection ☐ Automated alerting ☐ User reporting channels ☐ Regular reviews ☐ External notifications

3. Investigation and Response ☐ Immediate triage ☐ Containment actions ☐ Root cause analysis ☐ Remediation implementation ☐ Verification

4. Post-Incident Activities ☐ Post-incident review meeting ☐ Timeline reconstruction ☐ Lessons learned documentation ☐ Preventive measures identified ☐ Policy/procedure updates

Evidence: ☐ Incident response plan ☐ Incident reports ☐ Post-incident reviews ☐ Lessons learned documentation

A.7.5 Continual Improvement

Requirements: ☐ Improvement opportunities identified ☐ Improvements implemented ☐ Effectiveness measured ☐ Best practices captured and shared

Implementation Steps:

1. Identify Improvements ☐ From incidents and issues ☐ From monitoring and analysis ☐ From user feedback ☐ From audits and reviews ☐ From industry best practices

2. Prioritize Improvements ☐ Impact assessment ☐ Effort estimation ☐ Risk reduction ☐ Priority ranking

3. Implement Improvements ☐ Improvement project plan ☐ Resource allocation ☐ Implementation ☐ Testing and validation

4. Measure Effectiveness ☐ Before/after metrics ☐ Improvement verification ☐ Stakeholder feedback ☐ Lessons learned

5. Share Best Practices ☐ Internal knowledge sharing ☐ Documentation updates ☐ Training updates ☐ Industry contribution

Evidence: ☐ Improvement tracking log ☐ Improvement implementation documentation ☐ Effectiveness measurement reports ☐ Best practices documentation

ISO 42001 Reference: A.7

IMPLEMENTATION ROADMAP

Phase 1: Foundation (Months 1-3)

Priority: Critical foundational controls

☐ A.1: AI System Inventory

• Complete inventory of all AI systems
• Classify risk levels
• Assign owners

☐ A.2: Data Governance Framework

• Establish governance organization
• Define data quality standards
• Implement access controls

☐ AI Policy

• Draft and approve AI policy
• Communicate to organization
• Initial training

☐ Governance Structure

• Establish AI Governance Board
• Establish AI Ethics Committee
• Define roles and responsibilities

Deliverables:

• AI System Inventory
• Data Governance Framework
• AI Policy (approved)
• Governance charter

Phase 2: Development Controls (Months 4-6)

Priority: Controls for AI development lifecycle

☐ A.3: Training Data Management

• Implement data quality processes
• Establish bias assessment procedures
• Implement data versioning

☐ A.4: Model Development Standards

• Create model design templates
• Implement experiment tracking
• Establish documentation standards
• Implement explainability

☐ A.5: Validation and Testing

• Create validation framework
• Establish fairness testing procedures
• Implement independent validation (high-risk)

Deliverables:

• Training data procedures
• Development standards and templates
• Validation framework
• Model card template

Phase 3: Deployment and Operations (Months 7-9)

Priority: Controls for deployment and operations

☐ A.6: Deployment Controls

• Define deployment strategies
• Create readiness checklists
• Implement human oversight
• Establish change management

☐ A.7.1-7.2: Monitoring Infrastructure

• Deploy monitoring tools
• Create dashboards
• Configure alerts
• Implement data quality monitoring

Deliverables:

• Deployment procedures
• Human oversight implementation
• Monitoring dashboards
• Standard Operating Procedures

Phase 4: Monitoring and Improvement (Months 10-12)

Priority: Continuous monitoring and improvement

☐ A.7.3-7.4: Advanced Monitoring

• Implement fairness monitoring
• Establish incident management
• Conduct regular audits

☐ A.7.5: Improvement Process

• Establish improvement tracking
• Implement feedback loops
• Conduct retrospectives

☐ Compliance Validation

• Internal audit
• Gap remediation
• External certification (optional)

Deliverables:

• Complete monitoring implementation
• Incident response procedures
• Audit reports
• Certification (if applicable)

QUICK START CHECKLIST

Week 1-2: Assessment ☐ Inventory existing AI systems ☐ Assess current state vs ISO 42001 ☐ Identify critical gaps ☐ Prioritize implementation

Month 1: Foundation ☐ Establish governance structure ☐ Draft AI policy ☐ Create AI system inventory ☐ Assign roles and responsibilities

Month 2-3: Core Controls ☐ Implement data governance framework ☐ Establish development standards ☐ Create validation framework ☐ Approve and communicate policy

Month 4-6: Operationalization ☐ Implement all development controls ☐ Deploy monitoring infrastructure ☐ Establish operational procedures ☐ Train teams

Month 7-12: Optimization ☐ Monitor and refine controls ☐ Conduct internal audits ☐ Continuous improvement ☐ Prepare for certification

SUCCESS CRITERIA

Implementation Success: ☐ All Annex A controls implemented ☐ Evidence documented for each control ☐ Controls operating effectively ☐ Compliance verified through internal audit ☐ Team trained and competent ☐ Stakeholders satisfied

Operational Success: ☐ AI systems meet performance requirements ☐ Fairness thresholds maintained ☐ Incidents managed effectively ☐ Continuous improvement demonstrated ☐ Regulatory compliance maintained ☐ Stakeholder trust established

CONCLUSION

This comprehensive checklist provides a practical roadmap for implementing ISO 42001 controls. Adapt it to your organization's specific needs, risk profile, and maturity level.

Key Success Factors:

1. Executive Commitment: Leadership support essential
2. Adequate Resources: People, budget, tools
3. Phased Approach: Start with critical controls
4. Practical Implementation: Fit controls to context
5. Continuous Improvement: Iterate and enhance
6. Cultural Change: Embed responsible AI in culture

Remember: ISO 42001 is a journey, not a destination. Focus on continuous improvement and building a sustainable AI management system.

Module 3 Complete: You now have comprehensive guidance on implementing AI controls, from policy frameworks to operational procedures. Continue to Module 4 for advanced topics and certification preparation.