A tailored course, built for your situation
Mastering ISO 42001 for IT Product Owners in Technology-Driven Organizations
Turn AI governance from an overhead into a strategic lever with structured, auditable decision rights
$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.
AI governance decisions are escalating to leadership, unless you claim ownership now
Who this is for
IT Product Owner in a regulated or technology-forward organization, responsible for delivering compliant, auditable AI systems
Who this is not for
Engineers focused only on model tuning, compliance staff without product ownership, or leadership looking for high-level summaries
What you walk away with
- Own and finalize the ISO 42001 Statement of Applicability for your AI systems
- Set binding data provenance rules for training and validation datasets
- Approve AI model documentation templates without escalation
- Lead vendor assessments for AI tools under ISO 42001 without oversight
- Produce audit-ready artifacts that survive regulator follow-ups
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Impact on Product Ownership
Establish the link between AI governance standards and frontline product decisions, focusing on real-world applicability for IT Product Owners.
12 chapters in this module
- Origins of ISO 42001
- AI governance versus traditional compliance
- Role of product owners in governance
- Scope of AI systems under ISO 42001
- Mapping controls to product lifecycle
- Key obligations for technical leads
- How ISO 42001 differs from ISO 27001
- Integration with existing development frameworks
- Documentation expectations
- Audit readiness fundamentals
- Vendor management under ISO 42001
- Governance escalation paths
Module 2. Establishing Decision Rights in AI Governance
Define and document which decisions you own, which require collaboration, and which remain out of scope, tailored to your product context.
12 chapters in this module
- Identifying key governance decisions
- Setting data lineage thresholds
- Approving model monitoring rules
- Owning model documentation templates
- Finalizing audit artifacts
- Vendor pre-qualification authority
- Change control for AI systems
- Model deployment approvals
- Incident response ownership
- Version control policies
- Retraining triggers
- Documentation ownership
Module 3. Building the Statement of Applicability
Walk through constructing a product-specific SoA that reflects actual implementation choices and stands up to auditor scrutiny.
12 chapters in this module
- Purpose of the SoA
- Determining applicability of controls
- Justifying exclusions
- Documenting implementation methods
- Linking controls to product features
- Maintaining version history
- Stakeholder review process
- Updating the SoA post-deployment
- Auditor-facing formatting
- Cross-referencing with risk register
- Handling partial implementations
- SoA maintenance cadence
Module 4. Data Provenance and Traceability Frameworks
Implement systems to track data from source to model input, ensuring compliance with ISO 42001 traceability requirements.
12 chapters in this module
- Defining data lineage scope
- Tracking training data sources
- Validation data provenance
- Metadata tagging standards
- Data retention rules
- Data quality thresholds
- Bias detection triggers
- Data versioning practices
- Audit trail requirements
- Third-party data vetting
- Data refresh protocols
- Documentation automation
Module 5. Model Documentation Standards
Develop comprehensive, reusable templates for model cards and technical specifications that meet ISO 42001 expectations.
12 chapters in this module
- Required model metadata
- Performance metrics reporting
- Intended use definition
- Bias and fairness assessments
- Security testing results
- Explainability protocols
- Model version tracking
- Architecture diagrams
- Training pipeline details
- Monitoring setup
- Retraining criteria
- Documentation review cycle
Module 6. Vendor Selection and Management
Apply ISO 42001 criteria to third-party AI tools and services, with documented evaluation workflows and approval thresholds.
12 chapters in this module
- Vendor risk classification
- Assessment checklist development
- Due diligence requirements
- Contractual obligations
- Audit rights negotiation
- Performance monitoring
- Incident reporting expectations
- Compliance verification
- Right to terminate clauses
- Sub-processor oversight
- Renewal review process
- Exit strategy planning
Module 7. AI Risk Assessment Methodology
Adapt ISO 42001 risk frameworks to product-specific AI use cases with documented scoring and mitigation strategies.
12 chapters in this module
- Risk identification techniques
- Likelihood and impact scoring
- AI-specific risk categories
- Stakeholder input collection
- Risk treatment options
- Mitigation implementation
- Residual risk documentation
- Risk register maintenance
- Escalation thresholds
- Review frequency
- Scenario testing
- Third-party risk integration
Module 8. Internal Audit and Readiness Preparation
Prepare for internal and external audits with structured artifact collection, gap analysis, and response workflows.
12 chapters in this module
- Audit planning
- Document collection framework
- Gap identification process
- Internal review cycle
- Response drafting
- Evidence organization
- Timeline management
- Audit follow-up process
- Corrective action tracking
- Audit communication plan
- Stakeholder coordination
- Post-audit review
Module 9. Change Management for AI Systems
Define controlled processes for updating, retraining, or replacing AI models while maintaining ISO 42001 compliance.
12 chapters in this module
- Change identification
- Impact assessment
- Approval workflows
- Testing requirements
- Deployment protocols
- Rollback procedures
- Documentation updates
- Stakeholder notification
- Version control
- Audit trail maintenance
- Post-implementation review
- Change frequency limits
Module 10. Monitoring and Performance Validation
Establish ongoing monitoring rules that satisfy ISO 42001 requirements for model drift, bias, and operational integrity.
12 chapters in this module
- Performance metrics selection
- Drift detection thresholds
- Bias monitoring frequency
- Accuracy tracking
- False positive analysis
- User feedback integration
- Automated alerting
- Review meeting cadence
- Incident logging
- Remediation workflows
- Escalation paths
- Reporting templates
Module 11. Incident Response and Breach Management
Develop an ISO 42001-aligned incident response plan specific to AI system failures, data poisoning, or misuse.
12 chapters in this module
- Incident classification
- Detection mechanisms
- Initial reporting
- Response team activation
- Containment procedures
- Forensic data preservation
- Communication protocols
- Regulatory reporting triggers
- Post-mortem analysis
- Remediation tracking
- Legal counsel coordination
- Recovery validation
Module 12. Sustaining Compliance Across Product Lifecycle
Embed ISO 42001 practices into sprint planning, retrospectives, and product roadmaps for lasting impact.
12 chapters in this module
- Compliance in backlog grooming
- Sprint goal integration
- Definition of Done updates
- Retrospective feedback
- Roadmap alignment
- Resource allocation
- Team training plan
- Knowledge transfer
- Leadership reporting
- Continuous improvement
- Compliance debt tracking
- Annual review cycle
How this maps to your situation
- When launching a new AI feature
- During vendor selection for AI tools
- Ahead of internal audit cycles
- When updating model versions
Before vs. after
Before
AI governance decisions require frequent escalation and lack consistent documentation, slowing delivery
After
You own and execute key ISO 42001 decisions independently, with documented processes that accelerate audits and strengthen your authority
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 3 hours per module, designed to be completed in parallel with active product work
If nothing changes
Without clear ownership, AI governance decisions will continue to bottleneck at leadership level, eroding your strategic influence and creating rework during audits
How this compares to the alternatives
Unlike generic compliance trainings or certification prep courses, this course delivers actionable, product-specific workflows that integrate directly into your current sprint cycles and decision responsibilities.
Frequently asked
Is this course aligned with other ISO standards?
Yes, the content connects ISO 42001 with ISO 27001 and ISO 22301 where relevant, but focuses specifically on AI governance requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive templates I can use immediately?
Yes, each module includes downloadable templates and real-world examples tailored to IT Product Owners.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with active product work.
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