A tailored course, built for your situation
Mastering ISO 42001 for Engineering Portfolio Leaders
Turn AI governance from a siloed compliance task into a cross-portfolio strategic lever.
$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 remains fragmented across teams, limiting visibility and control at portfolio scale.
The situation this course is for
Even with strong individual project oversight, AI governance often fails to scale across engineering units. Without a unified framework, decisions become inconsistent, audits grow more complex, and leadership visibility erodes. The result is reactive oversight, duplicated effort, and missed opportunities to align AI risk with strategic delivery.
Who this is for
Engineering Portfolio Manager in a regulated technology or defense environment, managing multiple concurrent technical programs and accountable for cross-team governance consistency.
Who this is not for
This is not for individual contributors focused on single-project implementation or auditors seeking checklist training. It’s for leaders shaping governance across programs.
What you walk away with
- Lead ISO 42001 adoption across multiple engineering teams with confidence
- Standardize AI risk assessments that repeat across business units
- Produce audit-ready statements of applicability (SoA) that reflect portfolio-wide controls
- Negotiate vendor AI governance terms with engineering and legal stakeholders
- Build stakeholder trust by demonstrating consistent, documented governance across regions
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 42001 in Engineering Contexts
Understand how ISO 42001 applies to engineering portfolios, not just standalone AI systems. Map governance to development lifecycle stages.
12 chapters in this module
- Scope definition for multi-team AI systems
- Identifying AI system boundaries
- Linking governance to devops pipelines
- Key roles in AI governance teams
- Risk-based approach to AI oversight
- Distinguishing ISO 42001 from general AI ethics
- Integration with existing engineering standards
- Regulatory alignment with DORA and NIST
- Timing governance in program lifecycles
- Stakeholder mapping for engineering governance
- Documentation expectations for auditors
- Common misapplications of the standard
Module 2. Portfolio-Wide Scoping and Applicability
Define what parts of ISO 42001 apply to which teams without overburdening development groups.
12 chapters in this module
- Segmenting AI systems by risk class
- Creating reusable scoping templates
- Working with regional compliance leads
- Aligning with cloud and data governance
- Determining internal vs external AI use
- Managing third-party AI components
- Exemption justification protocols
- Version control for SoA documents
- Cross-team applicability workshops
- Handling legacy AI deployments
- Documentation flow from team to portfolio
- Audit trail requirements for scope
Module 3. Stakeholder Alignment Across Functions
Secure buy-in from engineering, legal, security, and product leaders using ISO 42001 as a common language.
12 chapters in this module
- Translating controls into engineering terms
- Running governance alignment sessions
- Addressing security team concerns
- Legal framing of AI accountability
- Procurement integration for vendor AI
- Product team collaboration models
- Handling pushback on documentation load
- Executive communication cadence
- Regional compliance liaison roles
- Escalation paths for non-compliance
- Documentation ownership models
- Feedback loops from audit results
Module 4. Designing AI Governance Processes
Build repeatable workflows that scale across project teams without central bottlenecks.
12 chapters in this module
- Embedding governance in sprint planning
- Pre-release AI governance gates
- Automated control checks in CI/CD
- Documentation templates for engineers
- Peer review mechanisms for AI systems
- Versioning governance artefacts
- Handling model drift detection
- Incident response integration
- Retraining triggers and governance
- Audit log requirements for AI models
- Data lineage tracking workflows
- Change control for AI model updates
Module 5. Risk Assessment at Portfolio Scale
Standardize risk evaluation across teams while allowing for context-specific adjustments.
12 chapters in this module
- Common risk taxonomy for AI systems
- Scoring model for AI risk levels
- Risk assessment templates by use case
- Handling high-risk AI classifications
- Working with legal on risk thresholds
- Documentation of risk decisions
- Reassessment triggers
- Third-party AI risk evaluation
- Supply chain transparency requirements
- Bias detection thresholds
- Human oversight requirements
- Post-deployment monitoring plans
Module 6. Implementing Human Oversight Controls
Define meaningful human-in-the-loop requirements that scale across diverse AI applications.
12 chapters in this module
- Determining appropriate human oversight level
- Designing intervention points
- Training requirements for human reviewers
- Escalation procedures for AI decisions
- Documentation of human review
- Response time expectations
- Automated alerting for human review
- Audit trail for override decisions
- Role-based access to AI systems
- Monitoring for human-in-the-loop compliance
- Adjusting oversight for risk level
- Review frequency based on AI impact
Module 7. Data Governance for AI Systems
Ensure data quality and provenance practices meet ISO 42001 requirements across the portfolio.
12 chapters in this module
- Data quality validation workflows
- Provenance tracking for training data
- Bias mitigation in data sourcing
- Documentation of data preprocessing
- Data retention for AI models
- Third-party data governance
- Sensitive data handling protocols
- Data versioning for reproducibility
- Data drift detection methods
- Labeling quality assurance
- Synthetic data governance
- Audit trail for data pipeline changes
Module 8. Model Development and Documentation
Ensure consistent model development practices and comprehensive documentation across teams.
12 chapters in this module
- Model documentation standards
- Version control for AI models
- Reproducibility requirements
- Hyperparameter tracking
- Validation dataset documentation
- Testing protocols for AI models
- Bias testing methodology
- Performance monitoring thresholds
- Explainability requirements
- Model card implementation
- Technical debt tracking for AI
- Retirement planning for models
Module 9. AI System Deployment and Monitoring
Establish governance controls for deployment and ongoing monitoring of AI systems.
12 chapters in this module
- Pre-deployment validation checklist
- Model performance benchmarks
- Real-time monitoring requirements
- Alerting thresholds for model drift
- Human oversight integration
- Audit log content standards
- Incident response for AI failures
- Post-deployment review cadence
- User feedback integration
- Model retraining triggers
- Performance reporting templates
- Decommissioning procedures
Module 10. Cross-Regional Governance Coordination
Harmonize ISO 42001 implementation across regions with different compliance expectations.
12 chapters in this module
- Mapping ISO 42001 to regional laws
- Handling data sovereignty requirements
- Local compliance team coordination
- Standardizing across regional variations
- Documentation translation protocols
- Audit readiness across jurisdictions
- Vendor management across regions
- Incident reporting across borders
- Time zone challenges for oversight
- Legal review processes by region
- Consistency vs customization balance
- Central reporting with local adaptation
Module 11. Internal Audit and Continuous Improvement
Prepare for and lead internal audits while building mechanisms for continuous governance improvement.
12 chapters in this module
- Preparing for ISO 42001 internal audits
- Audit sampling methodology
- Evidence collection workflows
- Remediation tracking systems
- Management review meetings
- Continuous improvement cycles
- Lessons learned documentation
- Benchmarking against peers
- Updating governance based on audit
- Training updates from findings
- Policy review cadence
- Stakeholder feedback integration
Module 12. Scaling Governance Across the Portfolio
Expand ISO 42001 adoption to new teams and systems using proven scaling strategies.
12 chapters in this module
- Governance onboarding for new teams
- Mentorship models for governance leads
- Knowledge sharing mechanisms
- Automated compliance checks
- Dashboarding for governance metrics
- Celebrating compliance successes
- Resource allocation for scaling
- Lessons from early adopters
- Handling resistance to governance
- Evolution to enterprise-wide AI governance
- Succession planning for leadership
- Future-proofing for AI regulation
How this maps to your situation
- New ISO 42001 mandate across engineering teams
- Expanding AI governance from pilot to portfolio
- Preparing for internal audit across multiple units
- Aligning distributed teams on common AI standards
Before vs. after
Before
AI governance feels fragmented across teams, with inconsistent documentation, varying risk thresholds, and limited visibility at the portfolio level.
After
You lead cohesive, scalable ISO 42001 implementation across engineering units, with standardized artefacts, clear accountability, and growing influence across business lines.
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 8-10 hours over 4-6 weeks, with self-paced completion possible in 2 weeks.
If nothing changes
Without a unified governance approach, AI initiatives will continue to create compliance risk, audit complexity, and missed opportunities for strategic alignment across the portfolio.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on portfolio-level ISO 42001 implementation with real-world engineering context, stakeholder alignment tactics, and scalable governance models tailored to complex technical organizations.
Frequently asked
Is this course focused on technical implementation or management oversight?
It's designed for technical leaders who need to implement governance across teams, not hands-on coders, nor pure executives. You'll learn how to structure, align, and verify governance at scale.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with upcoming audits?
Yes. You'll build audit-ready statements of applicability and evidence trails specifically designed for portfolio-level review.
$199 one-time. Approximately 8-10 hours over 4-6 weeks, with self-paced completion possible in 2 weeks..
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