A tailored course, built for your situation
Mastering ISO 42001 for Lead Data Science Practitioners
Build an AI governance asset that compounds across projects
$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.
Stop rebuilding AI governance from scratch each time
The situation this course is for
Most data science leaders waste cycles reinventing documentation, control mappings, and risk assessments for each new AI project. Without a compounding system, influence stalls and compliance remains reactive. The cost isn't just time, it's missed authority on strategic decisions.
Who this is for
Lead Data Scientists and Senior AI Engineers who lead cross-functional teams and own governance deliverables across AI deployments
Who this is not for
Junior analysts, individual contributors without delivery ownership, or practitioners focused solely on infrastructure without governance scope
What you walk away with
- Produce ISO 42001-compliant documentation that reuses and evolves across projects
- Build a personal library of control mappings and risk registers that compound in value
- Deploy a living, adaptable implementation playbook for repeated use
- Demonstrate consistent, auditable governance positioning across stakeholders
- Accelerate time from project kickoff to sign-off using pre-validated templates
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 in Practice
Foundations of AI governance under ISO 42001 with emphasis on real-world implementation by data science leaders. Learn how the standard maps to cross-functional team workflows and model delivery cycles.
12 chapters in this module
- What ISO 42001 means for AI teams
- The core principles of AI management systems
- How ISO 42001 differs from other standards
- Governance vs ethics vs compliance
- Roles in AI governance deployment
- Cross-functional alignment points
- Common misconceptions clarified
- Scope definition for data science projects
- Boundaries and applicability
- Integration with existing frameworks
- Regulatory context and expectations
- First steps in real organizations
Module 2. Scoping AI Management Systems
Define project boundaries with precision using ISO 42001. Learn how to isolate AI components, set governance perimeters, and avoid overreach while maintaining compliance.
12 chapters in this module
- Identifying AI components in workflows
- Defining system boundaries
- Mapping data flows to scope
- Excluding non-applicable clauses
- Documentation of scope decisions
- Stakeholder alignment on scope
- Avoiding scope creep
- Integration with model inventory
- Scope updates over time
- Versioning scope statements
- Linking scope to team ownership
- Case example from a supply chain AI rollout
Module 3. Leadership and Governance Accountability
Establish clear ownership and decision rights aligned with ISO 42001. Learn how technical leads can drive governance without executive titles.
12 chapters in this module
- Leadership roles under ISO 42001
- Defining accountability without authority
- Governance cadence design
- Escalation protocols
- Cross-team decision rights
- Documenting leadership commitment
- Internal sign-off workflows
- Engaging non-technical stakeholders
- Maintaining governance momentum
- Balancing agility and compliance
- Updating governance roles
- Case example from a data science team
Module 4. Risk Assessment and Treatment Planning
Build repeatable risk assessment workflows tailored to AI systems, aligned with ISO 42001. Learn to standardize risk registers across projects.
12 chapters in this module
- AI-specific risk categories
- Identifying inherent risks
- Risk likelihood and impact scoring
- Treatment options: avoid transfer mitigate accept
- Documenting risk decisions
- Risk register structure
- Linking risks to controls
- Updating assessments over time
- Team-wide risk literacy
- Risk communication templates
- Audit readiness for risk files
- Example register from a real deployment
Module 5. Control Mapping Across AI Lifecycle
Map ISO 42001 controls to data science workflows from ideation to deployment. Create living control mappings that evolve.
12 chapters in this module
- Control objectives in context
- Linking controls to phases
- Data collection controls
- Model development safeguards
- Validation and testing requirements
- Deployment checklists
- Monitoring and logging expectations
- Human oversight mechanisms
- Version control integration
- Updating control mappings
- Cross-functional control ownership
- Example mapping from a retail AI use case
Module 6. Documentation That Compounds
Design documentation systems that reduce rework. Learn how to build templates and repositories that grow stronger with each use.
12 chapters in this module
- The cost of recreating docs
- Template design principles
- Version control for policies
- Living documents vs static files
- Modular documentation strategy
- Automated doc generation
- Ownership and maintenance
- Reusability scoring
- Knowledge transfer design
- Searchable internal libraries
- Documentation audit trails
- Case study from a scaling AI team
Module 7. Internal Audit Preparation
Prepare for internal audits using ISO 42001 with confidence. Learn how to assemble evidence packages that tell a consistent story.
12 chapters in this module
- Audit scope definition
- Evidence collection workflow
- Common findings to avoid
- Preparing audit narratives
- Cross-team coordination
- Audit scheduling cadence
- Finding remediation tracking
- Audit report structure
- Follow-up workflows
- Leveraging audit outcomes
- Building audit relationships
- Example prep timeline
Module 8. Continuous Improvement Cycles
Institutionalize feedback loops that improve AI governance over time. Learn how to adapt systems without starting over.
12 chapters in this module
- Identifying improvement triggers
- Post-deployment review design
- Root cause analysis techniques
- Change approval workflows
- Updating AI management systems
- Measuring improvement impact
- Feedback integration from users
- Versioning system updates
- Communication of changes
- Avoiding governance drift
- Automation of improvement tracking
- Example cycle from a financial AI project
Module 9. Training and Awareness Execution
Scale AI governance through team enablement. Learn how to design training that sticks and awareness that spreads.
12 chapters in this module
- Assessing team readiness
- Training needs analysis
- Curriculum design for engineers
- Hands-on workshop structure
- Awareness campaign tactics
- Role-specific materials
- Documentation literacy
- Governance onboarding
- Tracking participation
- Evaluating effectiveness
- Refresher cycles
- Example rollout plan
Module 10. Third-Party and Vendor Oversight
Extend governance to external partners without expanding overhead. Learn how to standardize vendor review workflows.
12 chapters in this module
- Vendor risk classification
- Pre-contract governance checks
- Questionnaire design
- Due diligence process
- Contractual obligations
- Ongoing monitoring
- Exit planning
- Documentation of oversight
- Case review methodology
- Handling non-compliance
- Scaling vendor governance
- Example vendor assessment
Module 11. Implementation Playbook Development
Build a custom, reusable implementation playbook that captures institutional knowledge and accelerates future deployments.
12 chapters in this module
- Playbook structure design
- Capturing tacit knowledge
- Version control strategy
- Team access and permissions
- Integration with project management
- Updating the playbook
- Onboarding with the playbook
- Measuring playbook usage
- Feedback loops for improvement
- Adaptation for new projects
- Security and access controls
- Example playbook from a lead data scientist
Module 12. Sustaining Governance Velocity
Maintain momentum in AI governance as teams scale and mandates evolve. Learn how to future-proof governance systems.
12 chapters in this module
- Governance debt tracking
- Cadence for system review
- Scaling team structures
- Handling leadership changes
- Budgeting for governance
- Tooling investment strategy
- Talent development plans
- External benchmarking
- Regulatory horizon scanning
- Change management planning
- Celebrating governance wins
- Next steps after certification
How this maps to your situation
- New AI governance mandate in data science team
- Scaling AI deployments across business units
- Preparing for internal audit or external certification
- Reducing rework in governance documentation
Before vs. after
Before
Rebuilding governance from scratch on every project, struggling to maintain consistency, and reacting to audits.
After
A living library of reusable artefacts that compound across deliveries, reduce rework, and strengthen influence.
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 for staggered completion over 4-6 weeks.
If nothing changes
Continuing to rebuild governance from scratch leads to missed leadership opportunities, inconsistent compliance, and growing technical debt in documentation and control design.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for lead data scientists deploying AI systems, with ISO 42001 implementation grounded in real cross-functional workflows. No other course combines technical depth, repeatability, and asset compounding for practitioners in your role.
Frequently asked
Is this course only for organizations pursuing ISO 42001 certification?
No. The framework is used to build stronger governance systems, whether or not certification is pursued. The focus is on creating reusable, compounding assets for your team.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help reduce rework across multiple AI projects?
Yes. You’ll build a personal library of templates, control mappings, and documentation that compound in value with each delivery.
$199 one-time. Approximately 3 hours per module, designed for staggered completion over 4-6 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