A tailored course, built for your situation
Mastering ISO 42001 for IT Project Managers
Build defensible AI governance decisions with source-backed reasoning and specific implementation examples
$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.
Who this is for
IT Project Manager implementing AI governance frameworks within regulated environments
Who this is not for
This is not for engineers focused solely on model tuning or data pipeline optimization. It’s for project leaders accountable for structured, auditable governance outcomes.
What you walk away with
- Walk through the 'why' of any AI governance decision using ISO 42001 control logic
- Cite implementation precedents from certified organizations when proposing design trade-offs
- Respond to peer challenges with specific examples tied to audit requirements
- Reference documented sources for each control decision in deployment planning
- Lead discussions with confidence when governance requirements evolve
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope and Intent
Establish foundational clarity on how ISO 42001 defines AI governance and where it intersects with project delivery.
12 chapters in this module
- What ISO 42001 aims to govern
- How AI systems are classified
- Key differences from ISO 27001
- Organizational context mapping
- Determining control applicability
- First-party vs third-party AI use
- AI lifecycle stages covered
- Boundaries of human oversight
- Documentation expectations
- Alignment with NIST AI RMF
- Common misconceptions clarified
- Preparing for internal review
Module 2. Control Objective 1: AI Data Management
Break down requirements for data provenance, quality, and bias mitigation with real project examples.
12 chapters in this module
- Source documentation for training data
- Bias detection thresholds
- Data lineage tracking methods
- Version control for datasets
- Labeling accuracy standards
- Synthetic data governance
- Privacy-preserving techniques
- Data retention policies
- Cross-border data flow rules
- Vendor data handling checks
- Audit evidence collection
- Common gaps in practice
Module 3. Control Objective 2: AI System Documentation
Build comprehensive technical documentation that meets ISO 42001 requirements and withstands internal scrutiny.
12 chapters in this module
- Required elements of AI documentation
- Model cards and datasheets
- Version-controlled architecture diagrams
- Intended use statements
- Performance benchmarks defined
- Uncertainty and confidence reporting
- Change logging standards
- Human review points
- Failure mode documentation
- Interpretability requirements
- Stakeholder communication plan
- Template for audit submission
Module 4. Control Objective 3: AI System Validation
Implement testing protocols that demonstrate reliability and robustness under ISO 42001 criteria.
12 chapters in this module
- Testing against intended use
- Robustness under edge cases
- Adversarial testing methods
- Drift detection mechanisms
- Accuracy thresholds by use case
- False positive/negative tolerance
- Human override testing
- Scenario-based validation
- Third-party validation readiness
- Test result documentation
- Continuous monitoring setup
- Validation frequency planning
Module 5. Control Objective 4: AI Risk Management
Apply ISO 42001’s risk assessment framework to prioritize and mitigate AI-specific risks in project planning.
12 chapters in this module
- Identifying high-impact AI use cases
- Hazard analysis techniques
- Risk scoring methodology
- Human oversight requirements
- Failure impact categorization
- Dynamic risk reassessment
- Stakeholder risk communication
- Incident response planning
- Escalation triggers defined
- Mitigation control mapping
- Residual risk documentation
- Audit trail for decisions
Module 6. Control Objective 5: AI Transparency
Design reporting and disclosure practices that meet transparency obligations without overexposing IP.
12 chapters in this module
- What must be disclosed
- User-facing explanations
- Model behavior summaries
- Limitations communication
- Stakeholder-specific reporting
- Balancing transparency and IP
- Explanation methods by model type
- Human-readable summaries
- Feedback loop mechanisms
- Audit-ready disclosure logs
- Version comparison tracking
- Public communication templates
Module 7. Control Objective 6: Human Oversight
Define clear human-in-the-loop requirements and escalation paths for AI decision support systems.
12 chapters in this module
- When human review is required
- Review authority levels
- Override process design
- Decision logging standards
- Escalation trigger definition
- Training for human reviewers
- False confidence detection
- Time-to-intervention metrics
- Monitoring for automation bias
- Review frequency planning
- Audit trail for interventions
- Scalability of oversight model
Module 8. Control Objective 7: AI System Lifecycle
Map governance controls across development, deployment, monitoring, and decommissioning phases.
12 chapters in this module
- Lifecycle stage definitions
- Control handoff between teams
- Change approval workflows
- Version transition planning
- Monitoring maturity levels
- Incident response integration
- Decommissioning checklist
- Knowledge transfer requirements
- Lessons learned documentation
- Post-deployment review process
- Archiving standards
- Vendor exit planning
Module 9. Control Objective 8: AI Procurement
Evaluate third-party AI solutions against ISO 42001 requirements and enforce contract terms.
12 chapters in this module
- Vendor documentation requirements
- Pre-contract risk assessment
- Due diligence checklist
- Third-party audit rights
- Liability for AI failures
- Transparency expectations
- Oversight integration planning
- Performance monitoring clauses
- Exit strategy terms
- Compliance verification process
- Contract enforcement examples
- Common vendor gaps
Module 10. Implementation Planning and Governance
Develop a project-specific roadmap to achieve ISO 42001 compliance with clear ownership and milestones.
12 chapters in this module
- Gap assessment methodology
- Control implementation prioritization
- Resource allocation planning
- Cross-functional alignment
- Timeline development
- Stakeholder communication
- Pilot project design
- Metrics for success
- Audit preparation steps
- Continuous improvement loop
- Lessons from certified organizations
- Scaling across projects
Module 11. Audit Readiness and Evidence Collection
Prepare for internal and external audits with complete, well-organized documentation packages.
12 chapters in this module
- Audit scope definition
- Evidence collection plan
- Control mapping to ISO 42001
- Document version control
- Interview preparation
- Common auditor questions
- Corrective action planning
- Management review input
- Internal audit coordination
- External audit readiness
- Post-audit follow-up
- Continuous compliance tracking
Module 12. Sustaining Compliance and Continuous Improvement
Establish ongoing monitoring, review, and update processes to maintain ISO 42001 alignment.
12 chapters in this module
- Monitoring frequency planning
- KPIs for governance health
- Change impact assessment
- Review meeting cadence
- Update workflow design
- Lessons learned integration
- Training for new staff
- Stakeholder feedback collection
- Benchmarking against peers
- Regulatory change tracking
- Annual review process
- Governance maturity assessment
How this maps to your situation
- Starting an AI governance initiative
- Responding to internal audit findings
- Onboarding third-party AI tools
- Scaling AI governance across teams
Before vs. after
Before
Approaching AI governance with general best practices and reactive documentation
After
Leading with structured, defensible decisions backed by ISO 42001 control logic and implementation precedents
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 2 hours per week over 12 weeks to complete all modules and apply templates to current work.
If nothing changes
Without structured governance, AI projects risk delays, rework, or rejection during audit cycles, especially as standards bodies formalize expectations.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses exclusively on ISO 42001 implementation for project managers, with concrete examples and documented reasoning to support peer discussions.
Frequently asked
Is this course technical or managerial?
It's designed for project leaders managing AI governance. It balances technical control details with practical implementation strategy.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it cover other standards like NIST or GDPR?
It references NIST AI RMF and GDPR where they intersect with ISO 42001, but focuses on ISO 42001 control implementation.
$199 one-time. Approximately 2 hours per week over 12 weeks to complete all modules and apply templates to current 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