A tailored course, built for your situation
Mastering ISO 42001 for Senior Technology Executives in Regulated Industries
Build an AI governance foundation that compounds across audits, engagements, and leadership cycles
$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 stall after the first audit, trapped in reactive cycles with no reusable assets.
The situation this course is for
Teams keep rebuilding from scratch because they don’t document control mappings, risk narratives, or integration decisions in a way that compounds across quarters. The result? More effort for less impact. Leadership sees compliance as cost, not capability.
Who this is for
Senior technology executive in a regulated environment, responsible for AI governance, compliance convergence, or cross-functional risk leadership. Works at scale, leads initiatives beyond a single team, and is positioned to shape policy and implementation.
Who this is not for
Individual contributors new to governance, practitioners focused solely on non-AI compliance, or those without delivery authority across functions.
What you walk away with
- Produce a documented AI governance IP library applicable across future audits and engagements
- Turn each compliance cycle into a foundation for the next , no reinvention needed
- Strengthen cross-functional credibility by delivering consistent, repeatable artefacts
- Accelerate time from audit finding to resolution using pre-built control templates
- Position AI governance as a strategic capability, not a recurring cost
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope and Core Principles
Establish foundational clarity on ISO 42001’s role in AI governance, differentiate it from related standards, and identify where it intersects with existing enterprise controls.
12 chapters in this module
- Defining the purpose and structure of ISO 42001
- Distinguishing AI management systems from general IT governance
- Mapping ISO 42001 to organizational risk appetite
- Identifying leadership responsibilities under Clause 5
- Connecting AI governance to board-level strategic objectives
- Recognizing overlap with NIST AI RMF and OECD principles
- Assessing applicability across AI use case types
- Differentiating between AI-specific and general compliance controls
- Introducing the Plan-Do-Check-Act cycle in AI context
- Establishing internal vs external compliance expectations
- Evaluating third-party AI model accountability
- Setting benchmarks for AI governance maturity
Module 2. Building the AI Governance Leadership Framework
Define roles, assign ownership, and institutionalize AI accountability across functions including legal, risk, data science, and operations.
12 chapters in this module
- Assigning top management commitment per Clause 5.1
- Documenting leadership’s role in AI risk decisions
- Creating cross-functional AI governance councils
- Integrating AI oversight into existing compliance forums
- Defining escalation paths for non-conformance
- Establishing reporting lines for AI incidents
- Aligning AI ethics principles with control design
- Incorporating diversity and inclusion in AI teams
- Setting performance indicators for AI governance
- Linking AI leadership to ESG and sustainability goals
- Engaging CFO and legal teams in governance design
- Communicating AI accountability externally
Module 3. Scoping AI Management System Boundaries
Determine which AI systems, departments, and processes fall under the governance framework and define operational limits.
12 chapters in this module
- Identifying AI systems requiring formal governance
- Defining system boundaries based on risk tier
- Classifying AI use cases by impact level
- Documenting data flows in high-risk AI systems
- Incorporating legacy and third-party AI tools
- Mapping decision points in AI lifecycle
- Establishing scope inclusion and exclusion criteria
- Integrating cloud-hosted AI services into scope
- Capturing model retraining and update cycles
- Recording AI system dependencies and interfaces
- Validating scope completeness with stakeholders
- Maintaining scope documentation for auditors
Module 4. Risk and Opportunity Assessment for AI Systems
Conduct systematic risk assessments aligned with ISO 42001 requirements, identifying both threats and value opportunities.
12 chapters in this module
- Defining risk assessment methodology for AI
- Identifying legal and regulatory compliance risks
- Assessing societal and ethical implications
- Evaluating bias, fairness, and transparency risks
- Incorporating cybersecurity threats in AI models
- Mapping data privacy exposures in training sets
- Analyzing supply chain risks in AI deployment
- Documenting risk treatment plans for high-impact areas
- Prioritizing risks based on likelihood and impact
- Involving domain experts in risk validation
- Updating assessments with model changes
- Reporting risk posture to executive leadership
Module 5. Designing AI-Specific Controls and Safeguards
Develop technical and procedural controls that align with ISO 42001 requirements and protect the integrity of AI systems.
12 chapters in this module
- Implementing transparency and explainability requirements
- Designing human oversight mechanisms for AI decisions
- Ensuring data quality and representativeness
- Building model monitoring and drift detection
- Creating fallback procedures for AI failure
- Enforcing data protection by design
- Setting limits on autonomous AI actions
- Introducing audit logging for AI behavior
- Validating model performance across demographics
- Incorporating red-teaming into control design
- Securing model update and retraining pipelines
- Protecting AI intellectual property
Module 6. Establishing Human Oversight Processes
Define how humans monitor, intervene, and remain accountable in AI-driven workflows.
12 chapters in this module
- Determining appropriate levels of human review
- Defining escalation triggers for AI decisions
- Setting response time expectations for intervention
- Training staff on AI oversight responsibilities
- Documenting human-in-the-loop decision points
- Balancing automation with accountability
- Creating escalation playbooks for adverse outcomes
- Integrating oversight into incident management
- Assessing fatigue and over-reliance on AI
- Involving legal counsel in oversight design
- Auditing human review effectiveness
- Improving oversight through feedback loops
Module 7. Performance Monitoring and KPI Development
Create measurable indicators to track AI system performance, compliance, and business impact over time.
12 chapters in this module
- Defining success metrics for AI governance
- Tracking model accuracy and reliability over time
- Measuring fairness and bias mitigation effectiveness
- Monitoring user satisfaction with AI outputs
- Reporting on AI incident frequency and resolution
- Assessing efficiency gains from AI automation
- Evaluating cost-benefit of AI implementations
- Benchmarking against industry peers
- Integrating KPIs into executive dashboards
- Setting thresholds for control intervention
- Conducting regular performance reviews
- Using metrics to justify AI investment
Module 8. Internal Audit and Conformance Evaluation
Prepare for and conduct internal audits that validate adherence to ISO 42001 and identify improvement areas.
12 chapters in this module
- Planning the internal audit schedule
- Selecting qualified internal auditors
- Developing audit checklists for AI systems
- Reviewing documentation completeness
- Assessing control effectiveness
- Identifying non-conformities and root causes
- Prioritizing findings based on risk
- Tracking corrective action plans
- Validating remediation effectiveness
- Reporting audit results to leadership
- Preparing for external certification
- Maintaining audit trail integrity
Module 9. Management Review and Continuous Improvement
Drive ongoing enhancement of the AI management system through structured leadership reviews.
12 chapters in this module
- Scheduling regular management reviews
- Agenda design for AI governance updates
- Presenting audit and performance results
- Reviewing changes in AI regulations
- Assessing resource adequacy for AI governance
- Evaluating stakeholder feedback
- Identifying opportunities for automation
- Prioritizing improvement initiatives
- Updating governance policies
- Setting strategic direction for AI
- Documenting review outcomes
- Tracking action item completion
Module 10. Preparing for External Certification
Navigate the certification process with external auditors and demonstrate conformance to ISO 42001.
12 chapters in this module
- Selecting certification body and scope
- Submitting readiness documentation
- Conducting pre-certification gap analysis
- Preparing evidence for audit trails
- Coordinating with external assessors
- Responding to auditor inquiries
- Addressing non-conformance reports
- Demonstrating control effectiveness
- Maintaining documentation for inspection
- Preparing staff for interviews
- Understanding surveillance audit requirements
- Maintaining certification over time
Module 11. Scaling AI Governance Across the Enterprise
Extend successful governance practices across business units, geographies, and AI use cases.
12 chapters in this module
- Developing a centralized AI governance strategy
- Tailoring controls to local regulatory environments
- Creating governance playbooks for new teams
- Training regional champions
- Standardizing documentation formats
- Integrating AI governance into procurement
- Building cross-border incident response
- Harmonizing global AI policies
- Sharing best practices across divisions
- Leveraging lessons from pilot programs
- Scaling automation of compliance checks
- Maintaining consistency across deployments
Module 12. Sustaining AI Governance as Strategic Advantage
Institutionalize AI governance as a long-term differentiator and source of organizational resilience.
12 chapters in this module
- Embedding AI ethics into company culture
- Recognizing governance contributions in performance
- Marketing certification as competitive edge
- Engaging with regulators proactively
- Participating in standards development
- Contributing case studies to industry forums
- Building talent pipeline in AI governance
- Measuring business value of compliance
- Reinvesting savings into innovation
- Adapting to emerging AI regulations
- Maintaining leadership alignment
- Celebrating milestones and successes
How this maps to your situation
- Mid-cycle audit readiness
- Post-certification scaling
- Cross-business AI policy alignment
- Executive-level governance reporting
Before vs. after
Before
Reactive AI governance with fragmented artefacts, repeated effort, and inconsistent control mappings across teams.
After
A living IP library where each audit strengthens the next, reduces future effort, and positions leadership as strategic.
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: 90 minutes per week over 8 weeks, self-paced. Total time investment: 12 hours.
If nothing changes
Organizations that fail to institutionalize AI governance will continue to treat compliance as cost, miss opportunities to scale trusted AI, and remain vulnerable to regulatory scrutiny and operational disruption.
How this compares to the alternatives
Unlike generic AI ethics training or one-off workshops, this course delivers structured, repeatable artefacts that compound value across audits, initiatives, and leadership transitions , building institutional memory, not just awareness.
Frequently asked
Is this course technical or strategic?
It’s designed for senior leaders who drive AI governance across functions. Content balances strategic oversight with concrete implementation levers, not coding or model tuning.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this if my organization hasn’t started ISO 42001?
Yes. The course guides you from scoping to certification and positions early work as an investment that compounds across future AI initiatives.
$199 one-time. 90 minutes per week over 8 weeks, self-paced. Total time investment: 12 hours..
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