A tailored course, built for your situation
Mastering ISO 42001 for AI Governance Practitioners
Build compliant, auditable AI systems with confidence and control
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Struggling to align AI innovation with compliance guardrails?
The situation this course is for
AI projects stall when governance feels like a bottleneck. Practitioners lack clear authority to classify risk or approve controls, leading to delays and rework.
Who this is for
Mid-career IC in global IT services firm, implementing AI governance within client projects
Who this is not for
Executives seeking board-level overviews or vendors selling AI tools
What you walk away with
- Make final determinations on AI system risk tiering without escalation
- Define required documentation thresholds for AI model registries
- Approve control applicability for AI-specific risks based on deployment context
- Set boundaries for when AI changes trigger re-assessment cycles
- Document decisions in a way that satisfies internal and external auditors
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope in AI Systems
Define which AI applications fall under governance and which are exempt based on organizational impact.
12 chapters in this module
- Identifying AI use cases subject to ISO 42001 oversight
- Mapping AI lifecycle stages to governance checkpoints
- Determining boundary conditions for AI system inclusion
- Classifying AI-enabled tools vs. core AI systems
- Assessing integration points with legacy IT infrastructure
- Evaluating third-party AI components for compliance scope
- Documenting rationale for in-scope and out-of-scope decisions
- Establishing thresholds for model complexity triggers
- Linking AI governance to existing information security policies
- Aligning with client-specific regulatory expectations
- Updating scope definitions during model retraining cycles
- Maintaining audit trail for scope determination decisions
Module 2. AI Risk Classification Framework Design
Create a tiered risk model to guide assessment intensity and resource allocation.
12 chapters in this module
- Defining low, medium, and high-risk AI applications
- Setting criteria based on data sensitivity and model autonomy
- Assigning risk scores using impact and likelihood matrices
- Incorporating ethical considerations into risk ratings
- Determining escalation paths for high-risk classifications
- Validating risk tiers with cross-functional stakeholders
- Adjusting risk levels based on deployment environment
- Documenting assumptions behind each classification
- Using risk tiers to prioritize audit focus areas
- Updating classifications after model updates or drift
- Communicating risk levels to non-technical stakeholders
- Integrating risk classification into CI/CD pipelines
Module 3. Establishing AI System Documentation Requirements
Set expectations for what must be recorded and maintained for each AI system.
12 chapters in this module
- Defining minimum documentation for AI system registries
- Specifying model card contents for transparency reporting
- Determining data lineage requirements for training sets
- Setting standards for algorithmic decision logic disclosure
- Requiring human oversight mechanisms for high-risk models
- Documenting model performance metrics and drift thresholds
- Capturing ethical review board recommendations
- Recording model version history and change logs
- Ensuring documentation accessibility for auditors
- Linking documentation to incident response plans
- Updating records after model retraining events
- Verifying completeness before deployment approval
Module 4. Control Selection and Tailoring Process
Choose appropriate safeguards based on AI system characteristics and deployment context.
12 chapters in this module
- Selecting controls from Annex A based on risk tier
- Adapting generic controls to AI-specific threats
- Justifying control exclusions with documented rationale
- Incorporating model-specific mitigations for bias detection
- Applying security controls to model weights and parameters
- Ensuring explainability mechanisms meet audit needs
- Validating control effectiveness through testing scenarios
- Documenting control implementation evidence
- Aligning with client-specific compliance obligations
- Updating controls after adversarial testing results
- Maintaining control mapping across AI portfolio
- Streamlining control updates during model iterations
Module 5. AI Impact Assessment Procedures
Conduct thorough evaluations before deploying or modifying AI systems.
12 chapters in this module
- Initiating assessments based on risk classification triggers
- Gathering stakeholder input from legal and ethics teams
- Evaluating potential for discriminatory outcomes
- Assessing societal and environmental implications
- Reviewing model interpretability and transparency features
- Examining data provenance and consent mechanisms
- Analyzing cybersecurity vulnerabilities in AI pipelines
- Determining human-in-the-loop requirements
- Documenting findings and mitigation recommendations
- Obtaining necessary approvals before proceeding
- Scheduling follow-up reviews based on risk level
- Archiving assessment records for audit purposes
Module 6. Audit-Ready Evidence Generation
Produce documentation that satisfies internal and external reviewers.
12 chapters in this module
- Identifying evidence requirements for each control
- Creating standardized templates for common artifacts
- Ensuring traceability from policy to implementation
- Verifying evidence completeness before audit cycles
- Preparing auditor walkthrough materials
- Documenting control testing results and exceptions
- Maintaining version control for policy documents
- Capturing screenshots of system configurations
- Generating logs for access and modification events
- Compiling third-party attestation letters
- Organizing evidence in auditor-friendly formats
- Updating evidence packages after system changes
Module 7. Vendor and Third-Party Oversight
Manage external dependencies in AI development and deployment.
12 chapters in this module
- Assessing vendor compliance with ISO 42001 requirements
- Defining contractual obligations for AI governance
- Reviewing third-party model validation reports
- Monitoring ongoing compliance of external providers
- Evaluating open-source AI component risks
- Conducting due diligence on data sourcing practices
- Setting expectations for incident reporting timelines
- Validating security practices for model hosting
- Ensuring right-to-audit clauses are enforceable
- Tracking subcontractor compliance down the chain
- Managing transitions between AI service providers
- Documenting oversight activities for audit trail
Module 8. Change Management for AI Systems
Govern updates and modifications to prevent unintended consequences.
12 chapters in this module
- Defining what constitutes a significant AI system change
- Establishing re-assessment triggers for model updates
- Reviewing drift detection alerts for actionability
- Validating retraining data against original criteria
- Updating documentation after system modifications
- Obtaining approvals for production deployments
- Conducting regression testing for updated models
- Communicating changes to affected stakeholders
- Maintaining rollback capabilities for failed updates
- Logging all change events in central repository
- Aligning update cycles with client requirements
- Documenting rationale for change approvals
Module 9. Incident Response for AI Failures
Respond effectively to AI system malfunctions or unintended outcomes.
12 chapters in this module
- Defining AI-specific incident types and severity levels
- Establishing detection mechanisms for model drift
- Creating escalation paths for ethical concerns
- Documenting root cause analysis procedures
- Implementing containment strategies for faulty models
- Notifying affected parties per policy requirements
- Preserving evidence for post-mortem review
- Updating models to prevent recurrence
- Reporting incidents to regulators when required
- Conducting lessons-learned sessions
- Updating risk assessments based on incidents
- Maintaining incident log for audit purposes
Module 10. Continuous Monitoring and Improvement
Maintain governance effectiveness over time through regular review.
12 chapters in this module
- Setting frequency for AI system reviews
- Monitoring key risk indicators for early warnings
- Tracking model performance degradation trends
- Evaluating effectiveness of current controls
- Updating policies based on new threats
- Incorporating lessons from incident responses
- Benchmarking against industry best practices
- Soliciting feedback from end users
- Adjusting risk classifications as needed
- Validating ongoing compliance with ISO 42001
- Reporting metrics to senior management
- Planning for future governance enhancements
Module 11. Training and Awareness Programs
Ensure organizational understanding of AI governance expectations.
12 chapters in this module
- Identifying roles requiring AI governance training
- Developing role-specific curriculum content
- Creating materials for technical and non-technical audiences
- Establishing onboarding requirements for new hires
- Scheduling refresher training intervals
- Documenting training completion records
- Evaluating knowledge retention through assessments
- Updating materials based on policy changes
- Promoting ethical AI principles organization-wide
- Encouraging reporting of potential issues
- Recognizing model governance behaviors
- Measuring program effectiveness over time
Module 12. Integration with Broader Governance Frameworks
Align AI governance with existing organizational standards.
12 chapters in this module
- Mapping ISO 42001 controls to ISO 27001 requirements
- Aligning with client-specific compliance programs
- Integrating with enterprise risk management processes
- Connecting to data protection frameworks like GDPR
- Supporting SOC 2 compliance efforts
- Coordinating with privacy impact assessments
- Linking to cybersecurity incident response plans
- Feeding into enterprise architecture reviews
- Supporting internal audit functions
- Providing input to executive reporting
- Aligning with ESG disclosure requirements
- Ensuring consistency across global operations
How this maps to your situation
- AI governance implementation
- Compliance with emerging standards
- Technical leadership in regulated environments
- Cross-functional collaboration on risk
Before vs. after
Before
Decisions on AI governance require multiple approvals and escalate quickly.
After
You make clear, documented calls on risk classification, documentation depth, and control applicability without escalation.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 90 minutes per week for 12 weeks, with flexible pacing options.
If nothing changes
Without clear governance authority, AI initiatives remain vulnerable to audit findings and operational disruptions.
How this compares to the alternatives
Unlike generic AI ethics courses, this program delivers actionable decision authority under a recognized international standard.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior knowledge of ISO 42001 required?
No , the course starts with foundational concepts and builds to advanced application.
Can I apply this to client projects?
Yes , the implementation playbook includes client-facing templates and adaptation guidance.
$199 one-time. Approximately 90 minutes per week for 12 weeks, with flexible pacing options..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours