A tailored course, built for your situation
Mastering ISO 42001 for Data Scientists and ML Engineers
Build defensible, high-quality AI governance artefacts from day one.
$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.
Reduce rework on AI governance documentation with outputs that meet compliance standards the first time.
The situation this course is for
Even skilled practitioners waste cycles revising AI governance documentation due to ambiguous controls or misaligned interpretations. The cost isn’t just time, it’s credibility and momentum.
Who this is for
Data Scientists and ML Engineers implementing AI governance frameworks in public sector or regulated environments.
Who this is not for
Executives looking for high-level overviews or consultants seeking client-facing sales materials.
What you walk away with
- Produce ISO 42001 control documentation with fewer review cycles
- Apply AI-specific clauses with greater accuracy and consistency
- Build internal trust through polished, audit-ready deliverables
- Anticipate assessor questions and address them proactively in documentation
- Standardise team outputs to reduce variability and increase defensibility
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 42001 and AI Governance
Establish foundational understanding of ISO 42001’s structure, scope, and relevance to AI systems in public sector environments.
12 chapters in this module
- What ISO 42001 addresses
- How it differs from ISO 27001
- Core principles of AI governance
- Scope definition for AI systems
- Mapping organizational roles
- Linking to municipal data policies
- Key terminology deep dive
- AI system lifecycle stages
- Risk-based approach overview
- Compliance timing benchmarks
- Public sector considerations
- Integration with existing frameworks
Module 2. Clause 6: Organizational Context and Leadership
Define governance scope and secure leadership alignment for AI initiatives under ISO 42001.
12 chapters in this module
- Understanding internal context
- Mapping external pressures
- Stakeholder identification
- Leadership commitment requirements
- Governance structure design
- Role clarity for ML teams
- Oversight mechanisms
- Policy endorsement process
- Resource allocation norms
- Accountability frameworks
- Integration with city data office
- Documenting governance scope
Module 3. Clause 7: Support and Awareness
Develop training and communication plans that ensure team-wide compliance readiness.
12 chapters in this module
- Competency assessment methods
- Training program design
- Awareness material formats
- Documentation standards
- Language clarity for non-technical reviewers
- Feedback loops for improvement
- Version control practices
- Knowledge retention strategies
- Cross-functional onboarding
- AI ethics integration
- Public transparency requirements
- Internal audit preparation
Module 4. Clause 8: Operational Planning and Control
Structure AI development workflows to align with ISO 42001 control requirements.
12 chapters in this module
- AI project intake process
- Risk assessment template setup
- Model documentation standards
- Data lineage requirements
- Versioning for models and datasets
- Change control procedures
- Third-party AI component oversight
- Model monitoring expectations
- Incident response planning
- Bias and fairness evaluation
- Human oversight mechanisms
- Control validation timing
Module 5. Clause 9: Performance Evaluation
Implement internal review processes that validate ongoing compliance with ISO 42001.
12 chapters in this module
- Internal audit schedule design
- Compliance checklist creation
- Evidence collection protocols
- Reviewer assignment logic
- Deficiency tracking system
- Remediation workflow
- Management review meeting agenda
- KPIs for AI governance
- Performance dashboards
- Trend analysis methods
- Stakeholder feedback integration
- Audit trail maintenance
Module 6. Clause 10: Improvement
Establish feedback loops that drive continuous enhancement of AI governance practices.
12 chapters in this module
- Non-conformance logging
- Root cause analysis method
- Corrective action tracking
- Lessons learned repository
- Process update workflow
- Version control for policies
- Change notification system
- Stakeholder communication plan
- Compliance culture indicators
- Benchmarking against peers
- Public reporting standards
- Continuous learning integration
Module 7. Annex A Controls Deep Dive: A.1 Management
Apply governance-level controls for AI system oversight and accountability.
12 chapters in this module
- AI governance policy content
- Leadership accountability norms
- Risk appetite definition
- Resource allocation standards
- Vendor governance rules
- Third-party due diligence
- Audit rights negotiation
- Contractual compliance terms
- Performance monitoring clauses
- Exit strategy requirements
- Liability allocation
- Insurance considerations
Module 8. Annex A Controls Deep Dive: A.2 Technical
Implement technical safeguards for data quality, model transparency, and system robustness.
12 chapters in this module
- Data quality assurance methods
- Model documentation standards
- Explainability requirements
- Bias detection protocols
- Human oversight mechanisms
- Model performance thresholds
- Security testing frequency
- Penetration testing norms
- Access control design
- Incident logging standards
- Fail-safe mechanisms
- Model validation timing
Module 9. Annex A Controls Deep Dive: A.3 Ethical
Embed ethical principles into AI system design and deployment processes.
12 chapters in this module
- Fairness evaluation framework
- Transparency disclosure levels
- Human dignity considerations
- Autonomy protection mechanisms
- Environmental impact assessment
- Social consequence analysis
- Stakeholder consultation norms
- Public feedback integration
- Bias mitigation strategies
- Redress mechanisms
- Ethics review board role
- Public reporting templates
Module 10. Annex A Controls Deep Dive: A.4 Legal
Ensure AI systems comply with relevant laws, regulations, and contractual obligations.
12 chapters in this module
- Jurisdictional compliance mapping
- PIPEDA alignment checks
- Accessibility law compliance
- Procurement regulation adherence
- Privacy impact assessments
- Data sovereignty rules
- Retention period enforcement
- Consent management protocols
- Public records obligations
- Enforcement action readiness
- Regulatory reporting standards
- Cross-border data transfer rules
Module 11. SoA Development and Review
Build a Statement of Applicability that clearly justifies control inclusion or exclusion.
12 chapters in this module
- SoA structure overview
- Control selection rationale
- Justification writing standards
- Exclusion criteria application
- Evidence reference format
- Version control rules
- Internal review checklist
- Stakeholder feedback integration
- Public disclosure readiness
- Audit preparation steps
- Common assessor questions
- Final sign-off workflow
Module 12. Internal Audit and Readiness Validation
Prepare for certification audits with realistic internal assessments and gap remediation.
12 chapters in this module
- Audit simulation design
- Evidence collection drills
- Interview preparation materials
- Deficiency logging format
- Remediation tracking
- Stakeholder coordination
- Documentation completeness check
- Policy alignment verification
- Control effectiveness testing
- Public accountability readiness
- Lessons learned integration
- Continuous improvement loop
How this maps to your situation
- AI system governance in municipal environments
- ML engineer responsibilities in compliance delivery
- Audit preparation for public sector AI systems
- Defensible documentation for external review
Before vs. after
Before
Spending extra cycles revising AI governance documentation due to unclear control interpretations and inconsistent outputs.
After
Producing polished, accurate, and defensible ISO 42001 documentation on the first attempt, reducing review burden and increasing trust.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 for completion over 4 weeks with flexible pacing.
If nothing changes
Continuing to produce inconsistent or rework-heavy documentation erodes credibility, slows AI project velocity, and increases audit risk.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses on high-quality, first-time-right outputs for ISO 42001 in real-world data science and ML engineering contexts.
Frequently asked
Who is this course designed for?
Data Scientists, ML Engineers, and Analytics professionals responsible for implementing AI governance in public sector or regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover ISO 42001 certification?
Yes, it prepares you to produce the documentation and controls needed for successful certification.
$199 one-time. Approximately 3 hours per module, designed for completion over 4 weeks with flexible pacing..
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