A tailored course, built for your situation
Mastering ISO 42001 for Senior AI Governance Practitioners
A step-by-step path to owning the AI governance artefacts that cascade across high-impact engagements
$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.
Even strong AI governance teams face rework when documentation lacks standardisation or fails to align with auditor expectations.
The situation this course is for
Without a clear, consistent framework, AI governance work becomes reactive, chasing requests, revising deliverables, and deferring to others on sign-off. That delays impact and dims visibility on what you've built.
Who this is for
Senior practitioner in governance, risk, or compliance leading AI policy, audit, or control implementation across complex engagements
Who this is not for
Entry-level analysts, tool-specific implementers, or teams focused solely on AI model development without governance scope
What you walk away with
- Own the full ISO 42001 Statement of Applicability with confidence and precision
- Produce regulator-facing documentation that withstands follow-up scrutiny
- Become the first point of contact for M&A due diligence requests involving AI systems
- Lead cross-functional escalations with pre-built templates and documented rationale
- Ship board-prep materials faster using repeatable, audit-ready artefacts
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope and Boundaries
Define the applicability of AI governance controls to specific business units, data flows, and system types.
12 chapters in this module
- What ISO 42001 covers and what it excludes
- Mapping organisational structure to AI governance scope
- Identifying AI system inventory sources
- Classifying AI systems by risk tier
- Setting boundaries for external vs internal AI use
- Documenting data lineage for training sets
- Defining system development lifecycle stages
- Linking AI use cases to business functions
- Establishing user roles and access levels
- Tracking third-party AI component dependencies
- Determining reporting lines for AI oversight
- Creating a living boundary document
Module 2. Leadership and Governance Accountability
Structure executive sponsorship and internal oversight mechanisms that meet ISO 42001 requirements.
12 chapters in this module
- Defining roles for AI governance committee
- Assigning AI system owner responsibilities
- Setting escalation paths for ethical concerns
- Documenting leadership training completion
- Creating minutes for governance meetings
- Tracking policy exception approvals
- Maintaining oversight of vendor AI tools
- Reporting AI incidents to senior leaders
- Scheduling recurring compliance reviews
- Managing documentation access controls
- Integrating AI risk into ERM framework
- Building audit trail for leadership actions
Module 3. Organisational Structures and Resource Allocation
Align internal teams and budget to support sustainable AI governance operations.
12 chapters in this module
- Forming dedicated AI governance team roles
- Budgeting for ongoing compliance activities
- Allocating time for cross-functional reviews
- Hiring or contracting specialist roles
- Training staff on AI ethics principles
- Creating onboarding checklists for new hires
- Establishing communication protocols
- Maintaining version control for policies
- Scheduling annual refresh cycles
- Tracking tooling and infrastructure costs
- Planning for future AI adoption waves
- Measuring team capacity against workload
Module 4. AI Risk Management Framework Design
Develop a repeatable process for identifying, assessing, and treating AI-specific risks.
12 chapters in this module
- Identifying inherent AI risks by use case
- Creating risk likelihood and impact scales
- Conducting stakeholder risk interviews
- Documenting risk tolerance thresholds
- Mapping risks to ISO 42001 control objectives
- Prioritising high-impact risk scenarios
- Designing risk mitigation workflows
- Setting risk escalation criteria
- Integrating risk register with GRC tools
- Reviewing risk treatment effectiveness
- Updating risk profiles quarterly
- Reporting risk posture to leadership
Module 5. AI System Documentation Standards
Build comprehensive, standardised documentation for all AI systems in scope.
12 chapters in this module
- Creating AI system narrative templates
- Recording model development approach
- Describing training data provenance
- Documenting testing and validation results
- Capturing version control history
- Listing intended use and limitations
- Including human-in-the-loop designs
- Reporting performance metrics over time
- Tracking model drift detection methods
- Maintaining update and retraining logs
- Archiving decommissioned models
- Securing documentation access
Module 6. Human Oversight Controls
Implement effective monitoring and intervention mechanisms for AI decision-making.
12 chapters in this module
- Defining human review thresholds
- Designing override procedures
- Setting escalation paths for anomalies
- Documenting oversight shift schedules
- Creating incident reporting forms
- Training reviewers on bias detection
- Logging intervention decisions
- Auditing oversight effectiveness
- Measuring time-to-intervention
- Benchmarking oversight cost per case
- Improving feedback loops
- Updating protocols after incidents
Module 7. Technical Robustness and Safety
Ensure AI systems operate reliably and safely under expected conditions.
12 chapters in this module
- Testing for model stability
- Validating input data integrity
- Designing fallback mechanisms
- Monitoring for unexpected outputs
- Assessing cybersecurity resilience
- Conducting penetration testing
- Tracking system uptime and latency
- Evaluating stress test results
- Measuring reproducibility of outputs
- Logging system errors and warnings
- Updating recovery procedures
- Reviewing third-party component security
Module 8. Privacy and Data Governance
Integrate data protection principles into AI system design and operation.
12 chapters in this module
- Conducting privacy impact assessments
- Mapping data flows for GDPR compliance
- Implementing data minimisation practices
- Ensuring lawful basis for processing
- Managing consent mechanisms
- Anonymising training data sets
- Securing personal data storage
- Tracking data retention timelines
- Responding to DSARs involving AI
- Auditing access to sensitive data
- Reporting data breaches
- Updating policies after regulatory changes
Module 9. Transparency and Explainability
Provide clear, accessible information about AI system capabilities and limitations.
12 chapters in this module
- Creating user-facing documentation
- Writing plain language summaries
- Developing model cards
- Publishing accuracy metrics
- Disclosing limitations to users
- Creating technical white papers
- Updating documentation after changes
- Providing access to explanations
- Designing user feedback channels
- Measuring user understanding
- Benchmarking transparency against peers
- Improving disclosure formats
Module 10. Diversity, Non-Discrimination, and Fairness
Embed fairness checks and inclusive design principles into AI development.
12 chapters in this module
- Identifying protected attributes
- Testing for disparate impact
- Documenting bias mitigation steps
- Engaging diverse stakeholder groups
- Auditing outcomes by demographic
- Setting fairness thresholds
- Creating redress mechanisms
- Training teams on unconscious bias
- Reviewing model assumptions
- Improving dataset representativeness
- Tracking fairness metrics over time
- Reporting fairness posture to leadership
Module 11. Societal and Environmental Impact
Assess broader consequences of AI deployment on communities and sustainability.
12 chapters in this module
- Evaluating job displacement risks
- Measuring carbon footprint of AI models
- Assessing energy consumption
- Reviewing societal benefit claims
- Consulting community stakeholders
- Reporting ESG metrics
- Setting sustainability targets
- Monitoring long-term impacts
- Updating impact assessments
- Aligning with UN SDGs
- Publishing impact reports
- Engaging ethics advisory boards
Module 12. Continuous Monitoring and Improvement
Establish ongoing review processes to keep AI governance current and effective.
12 chapters in this module
- Scheduling system audits
- Tracking KPIs over time
- Collecting user feedback
- Reviewing incident logs
- Updating risk registers
- Revising policies after changes
- Conducting penetration tests
- Benchmarking against peers
- Improving documentation quality
- Training new team members
- Refreshing training data
- Planning for sunset of legacy systems
How this maps to your situation
- Handling M&A due diligence requests involving AI systems
- Responding to regulator inquiries about AI governance
- Leading internal audit readiness cycles
- Supporting board-level risk disclosures
Before vs. after
Before
Reactive, fragmented AI governance efforts with inconsistent documentation and limited influence beyond immediate team.
After
Proactive ownership of high-impact AI governance deliverables with trusted outputs that cascade across M&A, audits, and leadership reviews.
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, 4 hours per module, designed to fit alongside active engagements.
If nothing changes
Continuing with ad hoc or siloed AI governance practices increases exposure to regulatory scrutiny, delays in M&A due diligence, and missed opportunities to lead strategic initiatives.
How this compares to the alternatives
Unlike generic AI ethics guides or tool-specific certifications, this course delivers actionable, standards-aligned frameworks that produce artefacts directly usable in audits, M&A, and executive reviews , tailored for consultants operating at enterprise scale.
Frequently asked
Is this course relevant if my client uses a different AI governance framework?
Yes , ISO 42001 aligns closely with NIST AI RMF, EU AI Act, and other major standards. The skills transfer directly.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-ISO clients?
Absolutely , the documentation templates and control mappings are adaptable to most regulatory or internal audit environments.
$199 one-time. Approximately 3, 4 hours per module, designed to fit alongside active engagements..
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