A tailored course, built for your situation
Mastering ISO 42001 for Product Owners in Regulated Technology Delivery
Build AI governance artefacts that ship accurate, compliant, and stakeholder-ready the first time
$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.
Rework cycles on AI governance submissions
The situation this course is for
Even strong product owners face delays when AI governance documentation lacks precision or fails audit scrutiny on first review. The cost isn't just time, it's credibility, momentum, and stakeholder trust.
Who this is for
Product owners in regulated tech delivery who own AI governance compliance but lack a repeatable method to produce clean artefacts upfront
Who this is not for
Individuals looking for high-level AI ethics overviews or non-product-focused compliance roles
What you walk away with
- Produce complete ISO 42001 compliance artefacts that pass internal review without rework
- Structure AI governance documentation to align with the firm delivery timelines and audit expectations
- Reference authoritative control mappings and implementation examples for every clause
- Respond confidently to stakeholder feedback using pre-built rationale and evidence templates
- Ship AI governance outputs that are accurate, compliant, and stakeholder-ready the first time
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope and Applicability
Define the boundaries of AI governance for your product context with precision, avoiding over- or under-scoping.
12 chapters in this module
- Scope definition principles
- AI system categorization under ISO 42001
- Mapping product features to governance domains
- Stakeholder alignment on scope
- Exclusion justification framework
- Internal audit expectations
- Regulatory interface planning
- Documentation structure setup
- Baseline control identification
- Gap assessment workflow
- Evidence collection triggers
- Version control for scope documents
Module 2. AI Governance Leadership and Accountability
Establish clear ownership and decision rights within product teams for AI system oversight.
12 chapters in this module
- Governance role definition
- Accountability matrix design
- Decision log implementation
- Escalation path mapping
- Policy ownership assignment
- Cross-functional alignment
- Leadership engagement rhythm
- Responsibility tracking
- Authority documentation
- Stakeholder communication plan
- Audit readiness coordination
- Compliance sign-off workflow
Module 3. Risk Assessment and Treatment Planning
Conduct defensible AI risk assessments that produce actionable treatment plans.
12 chapters in this module
- Hazard identification framework
- Risk scenario modeling
- Impact severity scoring
- Likelihood assessment
- Risk tolerance thresholds
- Treatment options catalog
- Control effectiveness rating
- Residual risk evaluation
- Third-party risk integration
- Risk register structure
- Audit trail setup
- Review cycle automation
Module 4. Data Management for AI Systems
Ensure data quality, provenance, and handling compliance across the AI lifecycle.
12 chapters in this module
- Data quality criteria definition
- Provenance tracking methods
- Bias detection protocols
- Data lineage documentation
- Training data governance
- Personal data handling
- Data access controls
- Versioning strategy
- Retention policies
- Data sharing agreements
- Data inventory setup
- Audit support preparation
Module 5. AI System Design and Development
Integrate governance requirements into system architecture and development workflows.
12 chapters in this module
- Governance-by-design principles
- Architecture review checklist
- Development methodology alignment
- Model documentation standards
- Version control integration
- Testing strategy definition
- Bias mitigation techniques
- Explainability implementation
- Security by design
- Privacy-preserving methods
- Human oversight mechanisms
- Lifecycle phase gates
Module 6. Transparency and Documentation
Create clear, comprehensive documentation for internal and external stakeholders.
12 chapters in this module
- Technical specification standards
- User guide requirements
- System capability disclosure
- Limitations documentation
- Model card creation
- Fact sheet development
- Audit trail design
- Version history tracking
- Change notification process
- Stakeholder communication
- Public disclosure guidelines
- Internal knowledge base
Module 7. Human Oversight of AI Systems
Design effective human-in-the-loop mechanisms for AI decision support.
12 chapters in this module
- Oversight role definition
- Intervention trigger mapping
- Decision escalation paths
- Review frequency planning
- Performance monitoring
- Override mechanism design
- Training for human reviewers
- Bias detection workflow
- Escalation documentation
- Audit support materials
- Feedback loop integration
- Continuous improvement
Module 8. Operational Robustness and Safety
Ensure AI systems operate reliably under expected and edge conditions.
12 chapters in this module
- Failure mode analysis
- Stress testing protocols
- Fallback mechanism design
- Cybersecurity integration
- Adversarial attack resistance
- System monitoring setup
- Performance degradation detection
- Incident response planning
- Recovery procedures
- Safety threshold definition
- Automated alerting
- Root cause investigation
Module 9. Validation and Testing Procedures
Implement rigorous testing to verify AI system performance and compliance.
12 chapters in this module
- Test case development
- Scenario coverage planning
- Bias testing methodology
- Accuracy benchmarking
- Edge case identification
- Stress testing execution
- Performance monitoring
- Compliance verification
- Third-party testing coordination
- Audit readiness checks
- Documentation of results
- Remediation tracking
Module 10. Lifecycle Management and Maintenance
Manage AI systems throughout their operational lifespan with governance integrity.
12 chapters in this module
- Update planning
- Version control process
- Retraining triggers
- Performance monitoring
- Drift detection
- Model retirement
- Change management
- Stakeholder notification
- Audit trail maintenance
- Compliance revalidation
- Knowledge transfer
- Post-mortem review
Module 11. Stakeholder Engagement and Communication
Maintain trust through transparent, timely communication with all parties.
12 chapters in this module
- Stakeholder mapping
- Communication planning
- Expectation management
- Feedback mechanisms
- Transparency reporting
- Incident disclosure
- Regulatory engagement
- Public communication
- Internal updates
- Escalation protocols
- Relationship tracking
- Trust-building activities
Module 12. Continuous Improvement and Learning
Embed learning loops to advance AI governance maturity over time.
12 chapters in this module
- Performance review rhythm
- Incident learning process
- Feedback integration
- Benchmarking against peers
- Control enhancement
- Training updates
- Process refinement
- Tooling improvement
- Knowledge sharing
- Maturity assessment
- Innovation adoption
- Governance evolution
How this maps to your situation
- Initial product launch with AI component
- Mid-cycle governance review
- Pre-audit preparation phase
- Post-deployment monitoring and update
Before vs. after
Before
Spending cycles revising AI governance documentation, facing stakeholder pushback, and scrambling before audits
After
Producing accurate, compliant, and stakeholder-ready ISO 42001 artefacts the first time, with confidence and consistency
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 integrate with active product delivery cycles.
If nothing changes
Continuing with inconsistent or incomplete AI governance practices risks delayed product launches, audit findings, and erosion of cross-functional trust in your delivery leadership.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers actionable, ISO 42001-specific implementation steps tailored to product owners in regulated environments.
Frequently asked
Is this course focused on technical AI development or governance oversight?
It focuses on governance oversight and compliance artefact creation for product owners, not hands-on model development.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will the materials integrate with existing the firm delivery frameworks?
Yes, the implementation playbook is designed to align with common regulated delivery workflows and can be adapted to internal standards.
$199 one-time. Approximately 3 hours per module, designed to integrate with active product delivery cycles..
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