A tailored course, built for your situation
Mastering ISO 42001 for Data Science Practitioners
Build AI governance frameworks that scale across teams and systems.
$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.
Most AI governance efforts stay siloed, this course turns them into shared standards
The situation this course is for
AI governance often fails to scale because it's built in isolation, without playbooks that survive team changes or translate across business units. Practitioners with deep technical knowledge lack the structured frameworks to make their work repeatable and visible beyond immediate projects.
Who this is for
Senior Data Scientist or AI Engineer leading governance initiatives in mid-to-large tech organizations, especially those operating in regulated or multi-region environments.
Who this is not for
Entry-level analysts, tool-specific administrators, or those seeking certification prep without implementation focus.
What you walk away with
- Design ISO 42001-compliant AI governance playbooks deployable across data science teams
- Align stakeholder expectations using standardized control mappings and documentation
- Reduce rework by 50% through reusable templates for risk assessment and model documentation
- Extend influence to adjacent business units by leading cross-functional AI governance rollouts
- Gain recognition as the internal reference for AI accountability decisions
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 42001
Understand the structure, intent, and scope of ISO 42001 as it applies to AI systems in enterprise environments.
12 chapters in this module
- What ISO 42001 governs
- Core principles of AI management systems
- Relationship to NIST AI RMF
- Scope definition for AI projects
- Organizational roles and responsibilities
- Documentation requirements overview
- Linking to data governance standards
- Integration with risk management
- Accountability frameworks
- Compliance vs maturity goals
- Audit readiness expectations
- Implementation planning
Module 2. AI Risk Assessment Design
Build repeatable risk classification workflows aligned with ISO 42001 control objectives.
12 chapters in this module
- Risk taxonomy development
- Stakeholder mapping
- Likelihood impact matrices
- AI-specific risk factors
- Data quality risks
- Bias and fairness assessment
- Transparency thresholds
- Human oversight triggers
- Escalation protocols
- Risk tolerance definition
- Review cycle design
- Documentation standards
Module 3. Control Framework Mapping
Map ISO 42001 controls to existing data science workflows and tooling.
12 chapters in this module
- Control clause translation
- Cross-walk with internal policies
- Toolchain alignment
- Model lifecycle integration
- Version control for controls
- Automated compliance checks
- Exception handling
- Approval workflows
- Audit trail design
- Change management process
- Control ownership
- Performance metrics
Module 4. Governance Playbook Development
Create living documents that operationalize AI governance across teams.
12 chapters in this module
- Playbook structure design
- Decision authority mapping
- Template library creation
- Onboarding workflow
- Training content planning
- Version control strategy
- Feedback loops
- Adoption metrics
- Customization rules
- Localization strategy
- Cross-team coordination
- Maintenance schedule
Module 5. Cross-Functional Implementation
Lead governance rollouts that gain buy-in from engineering, legal, and product teams.
12 chapters in this module
- Stakeholder communication plans
- Alignment workshops
- Pilot program design
- Success criteria definition
- Objection handling
- Change champion recruitment
- Executive sponsorship
- Team onboarding
- Feedback collection
- Iteration planning
- Scaling strategy
- Lessons learned review
Module 6. Model Documentation Standards
Establish consistent AI model documentation that meets ISO 42001 requirements.
12 chapters in this module
- Model card components
- Data lineage tracking
- Performance benchmarks
- Bias disclosure
- Intended use definition
- Limitations reporting
- Version history
- Responsible parties
- Review schedule
- External sharing rules
- Archival policy
- Audit preparation
Module 7. Human Oversight Integration
Design effective human review points in AI workflows.
12 chapters in this module
- Oversight trigger definition
- Role assignment
- Review frequency
- Decision escalation
- Audit trail requirements
- Training for reviewers
- Performance monitoring
- Feedback to developers
- Process improvement
- Compliance verification
- Documentation standards
- Automation boundaries
Module 8. Bias and Fairness Management
Implement proactive bias detection and mitigation aligned with ISO 42001.
12 chapters in this module
- Bias definition framework
- Protected attribute handling
- Disparity measurement
- Testing protocols
- Mitigation techniques
- Documentation standards
- Stakeholder communication
- Appeal processes
- Monitoring frequency
- Threshold setting
- Remediation planning
- Transparency reporting
Module 9. Transparency and Explainability
Build explainable AI systems that satisfy audit and stakeholder demands.
12 chapters in this module
- Explainability requirements
- Stakeholder communication
- Technical documentation
- User-facing disclosures
- Model cards
- Summary reports
- Access controls
- Update protocols
- Third-party sharing
- Regulatory alignment
- Audit preparation
- Version tracking
Module 10. Data Management for AI
Ensure data governance supports ISO 42001 compliance.
12 chapters in this module
- Data quality standards
- Provenance tracking
- Bias in data detection
- Data lifecycle management
- Access controls
- Retention policy
- Anonymization techniques
- Data sharing rules
- Vendor data oversight
- Audit readiness
- Documentation standards
- Incident response
Module 11. AI System Lifecycle
Integrate governance across AI development, deployment, and monitoring.
12 chapters in this module
- Governance gate design
- Development phase controls
- Testing requirements
- Deployment approval
- Monitoring design
- Performance thresholds
- Retraining triggers
- Decommissioning rules
- Version control
- Change management
- Incident response
- Lessons learned
Module 12. Continuous Improvement
Establish feedback loops that refine AI governance over time.
12 chapters in this module
- Performance monitoring
- Audit findings tracking
- Stakeholder feedback
- Incident analysis
- Control effectiveness
- Update planning
- Stakeholder communication
- Training updates
- Policy revisions
- Version control
- Knowledge transfer
- Maturity assessment
How this maps to your situation
- Implementing AI governance in data science teams
- Scaling frameworks across business units
- Preparing for external audits
- Building cross-functional credibility
Before vs. after
Before
AI governance efforts remain siloed, requiring repeated negotiation and customization for each new project.
After
You lead with standardized, reusable frameworks that gain adoption across teams and geographies.
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 4 hours per module, designed to be completed alongside regular work.
If nothing changes
Without structured governance, AI initiatives risk inconsistent oversight, repeated rework, and limited influence beyond immediate teams.
How this compares to the alternatives
Unlike generic AI ethics courses, this program delivers actionable, ISO 42001-aligned frameworks specifically for data science practitioners leading real-world implementations.
Frequently asked
Is this course focused on certification prep?
No. This course is designed for practitioners implementing AI governance in real organizations, not exam preparation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for non-technical leaders?
It's tailored for data science and AI engineering roles. Non-technical leaders may find value but should expect technical depth.
$199 one-time. Approximately 4 hours per module, designed to be completed alongside regular 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