Lesson 5.6: AIMS Documentation Pack
Introduction
This lesson provides a comprehensive collection of templates, examples, and practical tools to support your ISO 42001 implementation. These ready-to-use resources can be adapted to your organization's specific needs, saving time and ensuring completeness. The documentation pack includes policy templates, procedure frameworks, form templates, and implementation guides.
How to Use This Documentation Pack
Customization Guidelines
Step 1: Review and Understand
- Read each template thoroughly
- Understand the purpose and requirements
- Identify sections relevant to your organization
Step 2: Customize
- Replace placeholder text with your information
- Add/remove sections based on your scope
- Adjust detail level to your organization size
- Incorporate your branding and style
Step 3: Review and Approve
- Technical review by subject matter experts
- Legal review where appropriate
- Management approval
- Document control registration
Step 4: Implement
- Train personnel on new documents
- Make documents accessible
- Monitor usage and effectiveness
- Gather feedback for refinement
Section 1: Policy Documents
1.1 AI Management System Policy
[ORGANIZATION NAME]
AI MANAGEMENT SYSTEM POLICY
Document ID: AIMS-POL-001
Version: 1.0
Effective Date: [DATE]
Review Date: [ANNUAL REVIEW DATE]
Owner: Chief Executive Officer
Approval: [CEO SIGNATURE]
---
POLICY STATEMENT
[Organization Name] is committed to developing, deploying, and operating
artificial intelligence systems in a manner that is responsible, ethical,
transparent, and compliant with all applicable requirements.
PURPOSE
This policy establishes the framework for our AI Management System (AIMS) in
accordance with ISO/IEC 42001:2023 and demonstrates our commitment to:
1. RESPONSIBLE AI DEVELOPMENT
We design and develop AI systems with careful consideration of their
impacts on people, society, and the environment. We prioritize safety,
reliability, and robustness throughout the AI lifecycle.
2. ETHICAL AI PRACTICES
We uphold the highest ethical standards in all AI activities. We are
committed to fairness, non-discrimination, transparency, and accountability
in our AI systems.
3. REGULATORY COMPLIANCE
We comply with all applicable legal, regulatory, and contractual
requirements related to AI systems, including but not limited to data
protection, privacy, consumer protection, and sector-specific regulations.
4. RISK MANAGEMENT
We systematically identify, assess, and manage risks associated with our
AI systems throughout their lifecycle. We implement appropriate controls
and continuously monitor their effectiveness.
5. TRANSPARENCY AND ACCOUNTABILITY
We maintain clear documentation of our AI systems, their capabilities and
limitations, and their impacts. We establish clear accountability for AI
decisions and actions.
6. STAKEHOLDER ENGAGEMENT
We engage with customers, employees, regulators, and other stakeholders
to understand their needs and concerns regarding our AI systems.
7. CONTINUOUS IMPROVEMENT
We continuously improve our AI Management System through monitoring,
measurement, internal audits, management reviews, and corrective actions.
8. COMPETENCE AND AWARENESS
We ensure our personnel have the necessary competence to perform their
AI-related responsibilities and are aware of the importance of conformity
to this policy and the AIMS.
SCOPE
This policy applies to all:
- AI systems developed, deployed, or operated by [Organization Name]
- Personnel involved in AI-related activities
- Third parties providing AI-related products or services on our behalf
- Locations: [List locations/sites covered]
OBJECTIVES
This policy provides the framework for establishing and reviewing AI management
objectives. Specific, measurable objectives are defined annually and reviewed
quarterly by management.
RESPONSIBILITIES
- **Executive Management**: Overall accountability for AIMS, resource provision
- **AI Director**: Day-to-day AIMS management and coordination
- **Process Owners**: Implementation within their areas of responsibility
- **All Personnel**: Conformity to AIMS requirements and continuous improvement
REVIEW
This policy is reviewed annually by executive management to ensure continued
suitability, adequacy, and effectiveness.
COMMUNICATION
This policy is:
- Communicated to all personnel and relevant interested parties
- Available to interested parties upon request
- Made available through [internal portal/website/handbook]
---
Approved by:
[CEO Name], Chief Executive Officer
[Signature]
Date: [Date]
Next Review: [Annual review date]
1.2 AI Ethics Policy
[ORGANIZATION NAME]
AI ETHICS POLICY
Document ID: AIMS-POL-002
Version: 1.0
Effective Date: [DATE]
Owner: AI Ethics Officer
---
PURPOSE
This policy establishes ethical principles and guidelines for all AI activities
at [Organization Name], ensuring our AI systems benefit people and society
while minimizing potential harms.
ETHICAL PRINCIPLES
1. HUMAN DIGNITY AND RIGHTS
We respect human dignity, autonomy, and rights in all AI applications. Our
AI systems support and enhance human capabilities rather than replace human
decision-making in matters affecting fundamental rights.
2. FAIRNESS AND NON-DISCRIMINATION
We design AI systems that treat all people fairly and do not discriminate
based on protected characteristics including race, gender, age, disability,
religion, or other protected attributes.
3. TRANSPARENCY AND EXPLAINABILITY
We strive to make our AI systems understandable to stakeholders. We provide
clear information about how AI systems work, their limitations, and how they
make decisions.
4. ACCOUNTABILITY
We maintain clear accountability for AI system outcomes. Human oversight is
maintained for significant decisions, and mechanisms exist to challenge or
appeal AI-assisted decisions.
5. SAFETY AND SECURITY
We prioritize the safety and security of people affected by our AI systems.
We implement robust controls to prevent harm and protect against malicious
use.
6. PRIVACY AND DATA PROTECTION
We respect privacy and protect personal data throughout the AI lifecycle.
We collect and use data fairly, lawfully, and transparently.
7. RELIABILITY AND ROBUSTNESS
We develop AI systems that perform reliably and predictably. We test
thoroughly and monitor continuously to detect and address issues.
8. SOCIAL AND ENVIRONMENTAL BENEFIT
We consider the broader social and environmental impacts of our AI systems.
We strive to create positive impacts while minimizing negative consequences.
ETHICAL REVIEW PROCESS
1. All high-risk AI systems undergo ethical review before deployment
2. AI Ethics Committee reviews and approves high-risk system designs
3. Ongoing monitoring ensures continued ethical operation
4. Stakeholder feedback informs ethical assessments
RAISING CONCERNS
All personnel are encouraged and empowered to raise ethical concerns about AI
systems. Concerns can be raised through:
- Direct manager or AI Ethics Officer
- Anonymous ethics hotline: [phone/email]
- Formal incident reporting process
No retaliation will occur for raising concerns in good faith.
---
Approved by: [AI Ethics Officer]
Date: [Date]
Section 2: Procedures
2.1 Risk Assessment Procedure
[ORGANIZATION NAME]
AI RISK ASSESSMENT PROCEDURE
Document ID: AIMS-PROC-001
Version: 1.0
Effective Date: [DATE]
---
1. PURPOSE
This procedure defines the process for identifying, assessing, and treating
risks associated with AI systems.
2. SCOPE
Applies to all AI systems throughout their lifecycle, from conception to
decommissioning.
3. RESPONSIBILITIES
- **AI Director**: Overall accountability for risk management process
- **Risk Manager**: Facilitates risk assessments, maintains risk register
- **Process Owners**: Identify and assess risks in their areas
- **Subject Matter Experts**: Provide technical input to risk assessments
4. RISK ASSESSMENT PROCESS
4.1 RISK IDENTIFICATION
When: At project initiation, major changes, annually for existing systems
Activities:
a) Assemble risk assessment team (diverse perspectives)
b) Review AI system description and context
c) Identify potential risks using structured approach:
- Ethical risks (fairness, transparency, accountability)
- Technical risks (accuracy, robustness, security)
- Privacy risks (data protection, consent)
- Legal/compliance risks (regulatory, contractual)
- Operational risks (reliability, availability)
- Reputational risks (brand, trust)
d) Document each risk in Risk Register
4.2 RISK ANALYSIS
For each identified risk:
a) Assess Likelihood (probability risk will occur):
- RARE (1): < 10% probability
- UNLIKELY (2): 10-30% probability
- POSSIBLE (3): 30-50% probability
- LIKELY (4): 50-75% probability
- ALMOST CERTAIN (5): > 75% probability
b) Assess Impact (consequences if risk occurs):
- INSIGNIFICANT (1): Minimal impact, easily managed
- MINOR (2): Limited impact, some effort to manage
- MODERATE (3): Significant impact, considerable effort
- MAJOR (4): Severe impact, significant resources required
- CATASTROPHIC (5): Extreme impact, fundamental threat
c) Calculate Risk Level: Likelihood × Impact
Risk Matrix:
Impact → | 1 | 2 | 3 | 4 | 5 |
Likelihood ↓ |---|---|----|----|----|
5 Almost Certain| 5 |10 | 15 | 20 | 25 |
4 Likely | 4 | 8 | 12 | 16 | 20 |
3 Possible | 3 | 6 | 9 | 12 | 15 |
2 Unlikely | 2 | 4 | 6 | 8 | 10 |
1 Rare | 1 | 2 | 3 | 4 | 5 |
Risk Levels:
- LOW (1-3): Monitor
- MEDIUM (4-8): Manage with standard controls
- HIGH (9-15): Requires specific treatment plan
- CRITICAL (16-25): Requires immediate senior management attention
4.3 RISK EVALUATION
a) Compare calculated risk level to risk appetite:
- ACCEPTABLE: No further treatment required (monitor)
- REQUIRES TREATMENT: Develop risk treatment plan
b) Prioritize risks requiring treatment:
- Critical risks: Immediate action
- High risks: Within 30 days
- Medium risks: Within 90 days
4.4 RISK TREATMENT
For each risk requiring treatment, develop Risk Treatment Plan:
a) Select treatment strategy:
- AVOID: Eliminate the activity causing risk
- REDUCE: Implement controls to reduce likelihood or impact
- TRANSFER: Share risk with third party (insurance, outsourcing)
- ACCEPT: Deliberately accept risk (with management approval)
b) Define specific actions:
- What will be done?
- Who is responsible?
- When will it be completed?
- What resources are required?
- How will effectiveness be measured?
c) Obtain management approval for treatment plan
d) Implement treatment actions
e) Verify effectiveness of treatment
4.5 RESIDUAL RISK ASSESSMENT
After treatment implementation:
a) Reassess likelihood and impact
b) Calculate residual risk level
c) Verify residual risk is acceptable
d) If unacceptable, implement additional treatments
5. RISK MONITORING AND REVIEW
5.1 ONGOING MONITORING
- Risk owners monitor assigned risks continuously
- Risk indicators tracked and reported monthly
- Emerging risks identified and assessed promptly
- Risk status reported in management reviews
5.2 PERIODIC REVIEW
- All risks reviewed quarterly
- Risk assessments updated for changes in context
- Risk treatment effectiveness evaluated
- Risk register updated
6. DOCUMENTATION
All risk assessment activities documented in:
- Risk Register (AIMS-FORM-001)
- Risk Assessment Report (AIMS-FORM-002)
- Risk Treatment Plan (AIMS-FORM-003)
7. REFERENCES
- ISO/IEC 42001:2023 Clause 6.1
- AI Management System Policy (AIMS-POL-001)
- Risk Management Framework (AIMS-GUIDE-001)
---
Document Control:
Prepared by: [Risk Manager]
Reviewed by: [AI Director]
Approved by: [CTO]
Date: [Date]
2.2 AI System Development Procedure
[ORGANIZATION NAME]
AI SYSTEM DEVELOPMENT PROCEDURE
Document ID: AIMS-PROC-002
Version: 1.0
Effective Date: [DATE]
---
1. PURPOSE
This procedure defines the standardized process for developing AI systems from
requirements through deployment.
2. SCOPE
Applies to all AI system development projects, including new systems and major
updates to existing systems.
3. DEVELOPMENT LIFECYCLE
Our AI systems follow this lifecycle:
PLAN → DESIGN → DEVELOP → TEST → DEPLOY → MONITOR → MAINTAIN → DECOMMISSION
4. PHASE 1: PLANNING
4.1 PROJECT INITIATION
Activities:
- Define business objectives and use case
- Conduct initial feasibility assessment
- Assign project team and roles
- Establish project governance
Deliverables:
- Project charter
- Initial business case
4.2 REQUIREMENTS DEFINITION
Activities:
- Gather functional requirements
- Define non-functional requirements (performance, security, fairness)
- Identify ethical considerations and constraints
- Define success criteria and metrics
- Engage stakeholders for input
Deliverables:
- Requirements specification document
- Stakeholder analysis
- Success criteria definition
Gate: Requirements Review and Approval
4.3 RISK ASSESSMENT
Activities:
- Conduct AI impact assessment (AIMS-PROC-005)
- Identify and assess project risks
- Develop risk treatment plan
- Classify system risk level (high/medium/low)
Deliverables:
- AI Impact Assessment Report
- Risk Assessment Report
- Risk Treatment Plan
Gate: Risk Acceptance by Management
5. PHASE 2: DESIGN
5.1 SYSTEM DESIGN
Activities:
- Design system architecture
- Select algorithms and models
- Design data pipeline
- Define monitoring and logging approach
- Design human oversight mechanisms
- Plan for explainability and transparency
Deliverables:
- System architecture document
- Algorithm selection rationale
- Data requirements specification
- Monitoring and logging design
5.2 DESIGN REVIEW
Activities:
- Peer review of design
- Ethics review (for high-risk systems)
- Security review
- Address review findings
Deliverables:
- Design review report
- Ethics review report (if applicable)
Gate: Design Approval
6. PHASE 3: DEVELOPMENT
6.1 DATA PREPARATION
Activities:
- Collect or acquire training data
- Assess data quality
- Clean and preprocess data
- Document data lineage
- Verify data privacy and security controls
Deliverables:
- Training dataset
- Data quality report
- Data lineage documentation
6.2 MODEL DEVELOPMENT
Activities:
- Develop model using approved algorithms
- Follow secure coding standards
- Document development decisions and trade-offs
- Conduct peer code reviews
- Version control all code and models
Deliverables:
- Model code and artifacts
- Development documentation
- Code review records
6.3 MODEL TRAINING
Activities:
- Train model on prepared data
- Tune hyperparameters
- Document training process and results
- Evaluate training performance
Deliverables:
- Trained model
- Training logs and metrics
- Model performance baseline
7. PHASE 4: TESTING
7.1 FUNCTIONAL TESTING
Activities:
- Test model accuracy and performance
- Test against requirements and success criteria
- Test edge cases and error conditions
- Document test results
7.2 FAIRNESS TESTING
Activities:
- Test for bias across protected groups
- Calculate fairness metrics (disparate impact, etc.)
- Identify and address fairness issues
- Document fairness testing results
7.3 ROBUSTNESS TESTING
Activities:
- Test model stability and reliability
- Test adversarial robustness
- Test handling of out-of-distribution data
- Stress testing
7.4 SECURITY TESTING
Activities:
- Vulnerability scanning
- Penetration testing
- Privacy testing (data leakage, model inversion)
- Security controls verification
7.5 INTEGRATION TESTING
Activities:
- Test integration with other systems
- Test data flows
- Test APIs and interfaces
- End-to-end testing
Deliverables:
- Test plans and test cases
- Test results and reports
- Issue tracking and resolution
Gate: Testing Sign-Off
8. PHASE 5: DEPLOYMENT
8.1 DEPLOYMENT PREPARATION
Activities:
- Prepare production environment
- Configure monitoring and alerting
- Prepare rollback plan
- Conduct deployment readiness review
- Train operations team
8.2 DEPLOYMENT EXECUTION
Activities:
- Deploy to production (staged or full)
- Verify deployment success
- Enable monitoring and logging
- Conduct post-deployment validation
8.3 DEPLOYMENT APPROVAL
Activities:
- Review deployment results
- Confirm success criteria met
- Obtain deployment approval from:
* Project Manager
* AI Director
* Risk Manager (high-risk systems)
* Ethics Officer (high-risk systems)
Deliverables:
- Deployment plan
- Deployment approval record
- Production documentation
Gate: Deployment Approval
9. DOCUMENTATION REQUIREMENTS
All AI systems must have:
a) System Documentation
- System overview and purpose
- Architecture and design
- Algorithm descriptions
- Data sources and lineage
- Performance characteristics
- Known limitations
b) Operational Documentation
- Deployment procedures
- Monitoring procedures
- Incident response procedures
- Maintenance procedures
c) User Documentation
- User guides
- Transparency disclosures
- Limitations and appropriate use
d) Governance Documentation
- Risk assessments
- Impact assessments
- Approval records
- Review reports
10. ROLES AND RESPONSIBILITIES
- **Product Manager**: Business requirements, stakeholder engagement
- **AI Engineers**: System design, development, testing
- **Data Scientists**: Algorithm selection, model training, performance optimization
- **Data Engineers**: Data pipeline, data quality
- **Security Engineers**: Security design and testing
- **AI Ethics Officer**: Ethics review, fairness assessment
- **QA Team**: Test planning and execution
- **Operations Team**: Deployment, monitoring
- **Risk Manager**: Risk assessment and treatment
11. TOOLS AND STANDARDS
- Version Control: [Git/GitHub]
- Project Management: [Jira/other]
- Coding Standards: [Link to standards]
- Testing Tools: [List tools]
- Documentation: [Confluence/other]
12. REFERENCES
- ISO/IEC 42001:2023 Clause 8.2
- AI Impact Assessment Procedure (AIMS-PROC-005)
- Data Management Procedure (AIMS-PROC-003)
- Testing Standards (AIMS-STD-002)
---
Document Control:
Prepared by: [AI Director]
Reviewed by: [Engineering Lead]
Approved by: [CTO]
Date: [Date]
Section 3: Form Templates
3.1 Risk Register Template
[ORGANIZATION NAME]
AI RISK REGISTER
Document ID: AIMS-FORM-001
Last Updated: [DATE]
---
Instructions: Record all identified AI-related risks. Update quarterly and
when significant changes occur.
| Risk ID | Date Identified | AI System | Risk Category | Risk Description | Impact Areas | Current Controls | Likelihood | Impact | Risk Level | Treatment Status | Owner | Review Date |
|---------|----------------|-----------|---------------|------------------|--------------|------------------|------------|--------|------------|------------------|-------|-------------|
| R-001 | 2025-01-15 | Customer Chatbot | Fairness | Potential bias in responses to protected groups | Compliance, Reputation | Training data review, manual QA | 3 | 4 | 12 (High) | Treatment planned | AI Lead | 2025-04-15 |
| R-002 | 2025-01-15 | All systems | Privacy | Data breach exposing training data | Legal, Privacy, Reputation | Encryption, access controls | 2 | 5 | 10 (Medium) | Monitoring | Security | 2025-04-15 |
| R-003 | 2025-01-20 | Recommender | Performance | Model drift reducing accuracy over time | Customer satisfaction | Weekly performance monitoring | 4 | 3 | 12 (High) | Treatment active | Ops Lead | 2025-04-20 |
Risk Categories:
- Ethical (fairness, transparency, accountability)
- Technical (accuracy, robustness, security)
- Privacy (data protection, consent)
- Legal/Compliance (regulatory, contractual)
- Operational (reliability, availability)
- Reputational (brand, trust)
Risk Levels:
- Low (1-3): Monitor
- Medium (4-8): Manage with standard controls
- High (9-15): Requires treatment plan
- Critical (16-25): Immediate senior management attention
Treatment Status:
- Identified: Risk identified, assessment pending
- Assessment: Risk analysis in progress
- Treatment Planned: Plan developed, implementation pending
- Treatment Active: Implementation in progress
- Monitoring: Treatment implemented, effectiveness being monitored
- Accepted: Risk accepted by management
- Closed: Risk no longer applicable
---
Risk Register maintained by: [Risk Manager]
Last reviewed by management: [Date]
Next review: [Quarterly review date]
3.2 AI Impact Assessment Template
[ORGANIZATION NAME]
AI IMPACT ASSESSMENT
Document ID: AIMS-FORM-004
AI System: [System Name]
Assessment Date: [DATE]
Assessor: [Name]
---
1. SYSTEM OVERVIEW
1.1 System Description
[Describe the AI system, its purpose, and how it works]
1.2 Intended Use
[Describe intended uses and users]
1.3 Deployment Context
[Describe where and how system will be deployed]
1.4 Risk Classification
☐ High Risk ☐ Medium Risk ☐ Low Risk
2. STAKEHOLDER IMPACT ASSESSMENT
2.1 Affected Stakeholders
[List all groups potentially affected by the system]
2.2 Stakeholder Engagement
[Describe how stakeholders were engaged in the assessment]
2.3 Positive Impacts
[List expected benefits for each stakeholder group]
2.4 Negative Impacts
[List potential harms or disadvantages for each stakeholder group]
3. ETHICAL CONSIDERATIONS
3.1 Fairness and Non-Discrimination
Is there potential for unfair treatment or discrimination?
☐ Yes ☐ No ☐ Uncertain
If yes, describe:
[Describe fairness concerns]
Protected characteristics that could be affected:
☐ Race/Ethnicity
☐ Gender
☐ Age
☐ Disability
☐ Religion
☐ Sexual Orientation
☐ Other: ___________
Mitigation measures:
[Describe how fairness will be ensured]
3.2 Transparency and Explainability
Can users understand how the system makes decisions?
☐ Yes ☐ Partially ☐ No
Explanation mechanisms provided:
☐ User-facing explanations
☐ Technical documentation
☐ Audit trails
☐ Other: ___________
Transparency limitations:
[Describe any limitations to transparency and justification]
3.3 Human Autonomy and Oversight
Does the system support human decision-making or replace it?
☐ Supports ☐ Replaces ☐ Mixed
Human oversight mechanisms:
☐ Human in the loop (human makes final decision)
☐ Human on the loop (human can intervene)
☐ Human in command (human sets parameters)
Can users override or appeal system decisions?
☐ Yes ☐ No ☐ N/A
If yes, describe process:
[Describe override/appeal mechanism]
3.4 Privacy and Data Protection
Types of personal data processed:
☐ Contact information
☐ Demographic data
☐ Behavioral data
☐ Biometric data
☐ Sensitive personal data
☐ None
Legal basis for processing:
☐ Consent
☐ Contract
☐ Legal obligation
☐ Legitimate interest
☐ Other: ___________
Data minimization applied:
☐ Yes ☐ No
Privacy risks identified:
[Describe privacy risks]
Privacy controls:
[Describe privacy protection measures]
4. TECHNICAL CONSIDERATIONS
4.1 Accuracy and Performance
Expected accuracy/performance: [%]
Acceptable threshold: [%]
Testing approach:
[Describe how accuracy will be verified]
4.2 Robustness and Reliability
Robustness testing planned:
☐ Edge cases
☐ Adversarial attacks
☐ Out-of-distribution data
☐ Stress testing
Failure handling:
[Describe how system failures will be handled]
4.3 Security
Security risks identified:
[List security risks]
Security controls:
[List security measures implemented]
5. LEGAL AND REGULATORY CONSIDERATIONS
5.1 Applicable Regulations
☐ GDPR/Data Protection
☐ Consumer Protection
☐ Non-discrimination laws
☐ Sector-specific regulations: ___________
☐ Other: ___________
5.2 Compliance Assessment
Does the system comply with all applicable regulations?
☐ Yes ☐ No ☐ Assessment ongoing
If no, describe gaps and remediation plan:
[Describe compliance gaps]
6. SOCIAL AND ENVIRONMENTAL CONSIDERATIONS
6.1 Social Impact
Broader social impacts (positive and negative):
[Describe social impacts]
Vulnerable groups considerations:
[Describe any particular considerations for vulnerable populations]
6.2 Environmental Impact
Environmental considerations (e.g., energy consumption):
[Describe environmental impacts]
Sustainability measures:
[Describe measures to minimize environmental impact]
7. RISK ASSESSMENT SUMMARY
| Risk Area | Severity | Mitigation | Residual Risk |
|-----------|----------|------------|---------------|
| Fairness | [Low/Med/High] | [Measures] | [Low/Med/High] |
| Privacy | [Low/Med/High] | [Measures] | [Low/Med/High] |
| Security | [Low/Med/High] | [Measures] | [Low/Med/High] |
| Accuracy | [Low/Med/High] | [Measures] | [Low/Med/High] |
| Transparency | [Low/Med/High] | [Measures] | [Low/Med/High] |
| Other: | [Low/Med/High] | [Measures] | [Low/Med/High] |
8. MONITORING AND REVIEW
8.1 Ongoing Monitoring
Metrics to be monitored:
[List key metrics]
Monitoring frequency: [Daily/Weekly/Monthly]
Alert thresholds:
[Define thresholds that trigger review]
8.2 Review Schedule
Periodic review frequency: [Quarterly/Annually]
Triggers for ad-hoc review:
☐ Significant incidents
☐ Regulatory changes
☐ System modifications
☐ Stakeholder complaints
9. RECOMMENDATIONS
9.1 Deployment Decision
☐ APPROVE: System may proceed to deployment
☐ CONDITIONAL: System may proceed with conditions (specify below)
☐ DEFER: Additional work required before deployment
☐ REJECT: System should not be deployed
Conditions or required actions:
[List any conditions or actions required]
9.2 Special Considerations
[Any special monitoring, restrictions, or requirements for this system]
10. SIGN-OFF
Assessed by: _______________________ Date: _______
[Name, Title]
Reviewed by: _______________________ Date: _______
[AI Ethics Officer]
Approved by: _______________________ Date: _______
[AI Director]
---
This assessment should be reviewed and updated:
- Annually
- When significant changes occur to the system
- When incidents or issues arise
- When regulatory requirements change
Section 4: Implementation Guides
4.1 Internal Audit Program Setup Guide
AIMS INTERNAL AUDIT PROGRAM - IMPLEMENTATION GUIDE
1. ESTABLISH AUDIT PROGRAM OBJECTIVES
Define what your audit program should achieve:
- Verify conformity to ISO 42001 requirements
- Assess AIMS effectiveness
- Identify improvement opportunities
- Prepare for certification audits
- Build organizational audit capability
2. DEFINE AUDIT SCOPE AND FREQUENCY
2.1 Audit Scope
- All AIMS processes (all ISO 42001 clauses)
- All locations within AIMS scope
- All AI systems (sampled)
- All organizational units involved in AI
2.2 Audit Frequency (Risk-Based)
High-Risk Areas (Quarterly):
- AI system development and testing
- Data governance and privacy
- AI deployment and operations
- Incident management
Medium-Risk Areas (Semi-Annually):
- Risk management
- Monitoring and measurement
- Training and competence
- Management review
Low-Risk Areas (Annually):
- Document control
- Communication
- Internal audit process itself
3. DEVELOP AUDIT SCHEDULE
Create annual audit schedule ensuring:
- All processes covered over 12-month period
- High-risk areas audited more frequently
- Resources available
- Auditees not overburdened
- Time for corrective actions before certification
Sample Annual Schedule:
Q1:
- Week 1-2: AI Development Process
- Week 3-4: Data Governance
- Week 5-6: Risk Management
Q2:
- Week 1-2: AI Deployment
- Week 3-4: Monitoring & Measurement
- Week 5-6: Training & Competence
Q3:
- Week 1-2: Impact Assessment
- Week 3-4: Incident Management
- Week 5-6: Vendor Management
Q4:
- Week 1-2: Document Control
- Week 3-4: Management Review
- Week 5-6: Full System Audit (pre-certification)
4. BUILD AUDITOR COMPETENCE
4.1 Identify Auditor Requirements
- Understanding of ISO 42001 standard
- Knowledge of AI technology and risks
- Audit methodology and techniques
- Communication and interpersonal skills
- Independence and objectivity
4.2 Select Auditors
- Internal staff from different departments (independence)
- Mix of AI technical knowledge and audit skills
- Lead auditor with formal training
- 3-5 auditors for typical organization
4.3 Train Auditors
Foundation Training:
- ISO 19011:2018 (Audit Management Systems)
- ISO 42001:2023 (AI Management Systems)
- Internal audit procedures
- Audit tools and templates
Practical Development:
- Shadow experienced auditors
- Co-audit with supervision
- Feedback and coaching
- Progress to lead auditor
5. DEVELOP AUDIT PROCEDURES AND TOOLS
5.1 Audit Procedure
Document your internal audit process:
- Planning and scheduling
- Audit conduct
- Finding documentation
- Reporting
- Follow-up and verification
5.2 Audit Tools
Create standardized tools:
- Audit checklists (by ISO clause)
- Finding report templates
- Audit report template
- Corrective action forms
- Audit schedule template
5.3 Audit Checklist Development
For each ISO 42001 clause:
- List requirements
- Define checkpoints
- Identify evidence needed
- Include reference to organizational procedures
6. ESTABLISH AUDIT ADMINISTRATION
6.1 Audit Coordinator
Appoint someone responsible for:
- Maintaining audit schedule
- Coordinating audit logistics
- Managing audit records
- Tracking corrective actions
- Reporting to management
6.2 Audit Management System
Set up system (can be simple spreadsheet or software):
- Audit schedule tracking
- Audit report repository
- Finding and corrective action tracking
- Auditor competence records
- Management reporting
7. CONDUCT AUDITS
7.1 First Audit Cycle
Start with:
- Manageable scope (don't try to audit everything)
- Process area with good documentation
- Supportive auditees
- Experienced lead auditor
Learn and improve:
- Gather feedback after each audit
- Refine checklists and templates
- Build auditor capability
- Expand scope progressively
7.2 Audit Conduct
Follow consistent process:
- Send audit notification (2-3 weeks advance)
- Conduct opening meeting
- Gather evidence (documents, records, observations, interviews)
- Document findings clearly
- Hold closing meeting
- Issue audit report within 5 days
8. MANAGE CORRECTIVE ACTIONS
8.1 Corrective Action Process
For each nonconformity:
- Root cause analysis by auditee
- Corrective action plan development
- Management approval
- Implementation
- Verification by auditor
8.2 Tracking
- Monitor corrective action status
- Escalate overdue actions
- Verify effectiveness
- Close out when verified
9. REPORT TO MANAGEMENT
Provide regular reports including:
- Audit completion status
- Findings summary (major/minor NCs, observations)
- Corrective action status
- Trends and patterns
- Overall AIMS effectiveness assessment
- Recommendations for improvement
Frequency:
- Detailed reports: After each audit
- Summary reports: Quarterly to management
- Annual report: Comprehensive year-end review
10. CONTINUOUS IMPROVEMENT
Continuously improve audit program:
- Review audit program effectiveness annually
- Gather feedback from auditees
- Update checklists based on findings
- Develop auditor competence
- Benchmark against industry practices
- Incorporate lessons learned
TIMELINE FOR AUDIT PROGRAM SETUP
Month 1:
- Define objectives and scope
- Develop annual schedule
- Identify and train auditors (start training)
Month 2:
- Complete auditor training
- Develop procedures and tools
- Set up administration
Month 3:
- Conduct first pilot audit
- Refine processes and tools
- Begin regular audit schedule
Month 4+:
- Execute audit schedule
- Continuous improvement
- Build toward certification readiness
RESOURCES REQUIRED
People:
- Audit Coordinator: 20% FTE
- Lead Auditor: 40 hours/year
- Additional Auditors: 20 hours/year each
- Auditees: 10-20 hours per audit
Budget:
- Auditor training: $3K-5K per auditor
- Audit software (optional): $1K-5K/year
- External audit support (optional): $5K-10K
- Total initial investment: $10K-$30K
SUCCESS CRITERIA
Your audit program is successful when:
✓ All AIMS processes audited annually
✓ Audits conducted on schedule
✓ Findings are meaningful and actionable
✓ Corrective actions completed and effective
✓ AIMS effectiveness improving over time
✓ Organization ready for certification audit
✓ Audit program adding value (not just compliance)
Section 5: Quick Reference Guides
5.1 ISO 42001 Requirements Checklist
ISO 42001:2023 REQUIREMENTS QUICK REFERENCE
Use this checklist to verify your AIMS addresses all requirements:
CLAUSE 4: CONTEXT OF THE ORGANIZATION
☐ 4.1 External and internal issues identified and documented
☐ 4.2 Interested parties and their requirements identified
☐ 4.3 AIMS scope defined and documented
☐ 4.4 AIMS established, implemented, maintained, and continually improved
CLAUSE 5: LEADERSHIP
☐ 5.1 Leadership and commitment demonstrated
☐ 5.2 AI policy established and communicated
☐ 5.3 Roles, responsibilities, and authorities assigned and communicated
CLAUSE 6: PLANNING
☐ 6.1.1 Actions to address risks and opportunities
☐ 6.1.2 AI impact assessment process established
☐ 6.2 AI objectives established and plans to achieve them
CLAUSE 7: SUPPORT
☐ 7.1 Resources provided for AIMS
☐ 7.2 Competence determined, ensured, and demonstrated
☐ 7.3 Awareness of AI policy, objectives, and responsibilities
☐ 7.4 Internal and external communication determined and implemented
☐ 7.5 Documented information created, updated, and controlled
CLAUSE 8: OPERATION
☐ 8.1 Operational planning and control
☐ 8.2 AI system lifecycle management (development through decommissioning)
☐ 8.3 Data management
☐ 8.4 AI impact assessment conducted
☐ 8.5 Human oversight implemented
☐ 8.6 Supplier relationships managed
☐ 8.7 Incident management process established
CLAUSE 9: PERFORMANCE EVALUATION
☐ 9.1 Monitoring, measurement, analysis, and evaluation
☐ 9.2 Internal audit program established and conducted
☐ 9.3 Management review conducted at planned intervals
CLAUSE 10: IMPROVEMENT
☐ 10.1 Nonconformity and corrective action process
☐ 10.2 Continual improvement
ANNEX A: AI MANAGEMENT SYSTEM CONTROLS
Review and implement applicable controls from Annex A based on risk assessment
DOCUMENTATION CHECK
☐ AIMS Scope document
☐ AI Management System Policy
☐ AI objectives
☐ Risk assessments and treatment plans
☐ Competence records
☐ Communication records
☐ AI system lifecycle documentation
☐ Data management records
☐ Impact assessments
☐ Monitoring and measurement results
☐ Internal audit program and reports
☐ Management review records
☐ Nonconformity and corrective action records
☐ Procedures (as appropriate)
Summary
This documentation pack provides ready-to-use templates and guides for implementing ISO 42001. Key resources include:
- Policy Documents: AI Management System Policy and AI Ethics Policy
- Procedures: Risk Assessment and AI Development procedures with detailed steps
- Form Templates: Risk Register and AI Impact Assessment templates
- Implementation Guides: Internal Audit Program setup guide with timeline
- Quick Reference: ISO 42001 requirements checklist
Using These Resources:
- Customize templates to your organization's context
- Adapt detail level to your organization size
- Ensure management review and approval
- Train personnel on new documents
- Continuously improve based on usage
Remember: These templates provide a starting point. The most effective AIMS documentation reflects your organization's actual practices and culture, not just compliance with requirements.
Next Steps
In the final lesson of this module, you'll complete a Final Assessment that tests your comprehensive understanding of ISO 42001 and your readiness to implement an AI Management System in your organization.