A tailored course, built for your situation
Mastering ISO 42001 for Senior AI Engineering Leaders
Build auditable, sustainable AI governance that scales with technical ownership
$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 technical leaders get second-guessed when AI governance lacks structure
The situation this course is for
Without a repeatable method, sign-off authority becomes a bottleneck. Peers question scope, auditors challenge evidence, and momentum stalls, even when leadership trusts your judgment. The gap isn't expertise; it's documented decision logic.
Who this is for
Senior engineering leader owning AI governance in regulated or scale-driven environments
Who this is not for
Individual contributors without scope authority, compliance generalists without technical depth, or non-AI-focused teams
What you walk away with
- Own final approval on AI system inclusions and exclusions under ISO 42001
- Drive evidence collection with clear ownership rules that survive team changes
- Document control mapping decisions so updates don’t require re-review
- Respond confidently when peer leads challenge risk scoring or scope boundaries
- Ship compliant AI systems faster by reducing rework from late-stage audits
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 42001 in AI Development
Establish alignment between AI engineering goals and ISO 42001 requirements. Understand how the standard applies to training data pipelines, model deployment, and feedback loops. Learn to map organizational roles to control ownership and avoid overlap or gaps in responsibility.
12 chapters in this module
- Overview of ISO 42001's purpose in AI governance
- Key differences between ISO 42001 and prior AI ethics frameworks
- How AI automation increases need for formalized control ownership
- Structure of AI system boundaries under the standard
- Roles and responsibilities for engineering leadership
- Control applicability scoring for custom AI pipelines
- Integration points with existing security frameworks
- Evidence expectations for initial certification
- Common missteps in scoping AI systems
- Versioning control for model updates and drift
- Documenting rationale for control exclusions
- Preparing for first internal audit cycle
Module 2. Defining AI System Boundaries
Learn how to draw clear, defensible lines around what is in and out of scope for ISO 42001 compliance. Focus on technical systems, data flows, and human oversight layers. Use templates to document decisions that hold up during external audits.
12 chapters in this module
- Identifying core AI components requiring certification
- Mapping data ingestion and preprocessing stages
- Determining model training environment inclusion
- When inference APIs become part of the boundary
- Handling third-party model dependencies
- Scope for fine-tuning versus full retraining
- Human-in-the-loop touchpoints and thresholds
- Model monitoring as a control boundary
- Logging and audit trail inclusions
- Version control and rollback mechanisms
- Exclusions for research or sandbox environments
- Documenting scope rationale for auditor review
Module 3. Assigning Control Ownership
Allocate responsibility for each AI governance control with precision. Avoid handoffs and ambiguity by matching ownership to team structure and expertise. Learn to justify assignments based on operational reality, not org charts.
12 chapters in this module
- Control-by-control ownership mapping exercise
- Why engineering leads should own monitoring controls
- Security team role in access and confidentiality
- Data governance team responsibilities
- Vendor oversight ownership model
- Change management and deployment approvals
- Escalation paths for unresolved control gaps
- Documenting cross-functional dependencies
- How to handle shared responsibility zones
- Role-based access review integration
- Tracking control ownership over time
- Updating assignments after team reorgs
Module 4. Evidence Design for Technical Systems
Design evidence that proves compliance without creating technical debt. Use logs, configuration snapshots, and audit trails that integrate seamlessly into existing CI/CD and monitoring workflows.
12 chapters in this module
- Types of acceptable evidence under ISO 42001
- Automated logging for model validation steps
- Version-controlled configuration as evidence
- Integrating evidence collection into CI pipelines
- Audit trail requirements for model updates
- Data quality checks as repeatable proof
- Human review logs and timestamping
- Monitoring false positive rates over time
- Bias detection report retention policies
- Secure storage and access for evidence files
- Chain of custody for third-party inputs
- Template library for standard evidence formats
Module 5. Risk Assessment Methodology
Implement a consistent, defensible process for scoring AI risks. Focus on impact, likelihood, and mitigation feasibility. Align with engineering reality while satisfying auditor expectations.
12 chapters in this module
- Structuring the risk register for AI systems
- Impact categories specific to AI applications
- Likelihood scoring based on deployment scale
- Mitigation feasibility assessment criteria
- Incorporating feedback from model monitoring
- Thresholds for high-risk AI categorization
- Documentation of risk treatment decisions
- Reassessing risk after major updates
- Integrating risk scores into sprint planning
- Peer validation of risk assessments
- Handling disputed risk ratings
- Evidence for risk treatment decisions
Module 6. Policy Development in Engineering Contexts
Write effective AI policies that engineers will follow. Move beyond generic statements to actionable rules integrated into development workflows and code review standards.
12 chapters in this module
- Translating ISO 42001 clauses to engineering policy
- Policy structure for readability and enforcement
- Incorporating policies into onboarding materials
- Code comment requirements for model documentation
- PR checklist integration for policy compliance
- Versioning and change tracking for policies
- Enforcement mechanisms without slowing delivery
- Handling policy exceptions safely
- Rollout planning for large teams
- Metrics to track policy adoption
- Updating policies after incident reviews
- Archiving obsolete policies
Module 7. Audit Readiness Preparation
Prepare for internal and external audits with confidence. Use checklists and walkthroughs to test readiness. Train teams on what to expect and how to respond.
12 chapters in this module
- Internal audit vs external certification differences
- Preparing the audit evidence package
- Scheduling dry-run walkthroughs
- Training developers for auditor interactions
- Common auditor questions and how to answer
- Addressing control gaps pre-audit
- Version control for audit responses
- Timeline for corrective action plans
- Post-audit review and improvement cycle
- Sharing findings without exposing risk
- Building institutional memory from audits
- Template for audit follow-up tracking
Module 8. Third-Party AI Risk Management
Assess and govern AI components from external sources. Apply ISO 42001 requirements to vendor models, APIs, and pre-trained weights with clear ownership and due diligence.
12 chapters in this module
- Vendor risk classification framework
- Due diligence questions for AI providers
- Contractual obligations for model updates
- Monitoring third-party model performance
- Fallback plans for API deprecation
- Security review of external model outputs
- Data leakage prevention for vendor models
- Bias and fairness expectations for third-party models
- Ownership of compliance evidence
- Incident response coordination with vendors
- Exit strategies for problematic providers
- Template for vendor risk assessment
Module 9. Change Management for AI Systems
Implement structured processes for updating AI models and pipelines. Ensure changes are reviewed, tested, and documented without slowing innovation.
12 chapters in this module
- Types of changes requiring formal review
- Change approval workflow design
- Emergency change handling procedures
- Rollback readiness assessment
- Testing requirements before deployment
- Documentation of model version transitions
- Notification system for downstream users
- Human oversight thresholds for changes
- Monitoring new models post-deployment
- Incident review after failed changes
- Version compatibility tracking
- Automated change tracking in CI/CD
Module 10. Incident Response for AI Failures
Plan and execute response to AI system failures, bias incidents, or security breaches. Integrate AI-specific considerations into existing incident frameworks.
12 chapters in this module
- Defining AI-specific incident types
- Detection mechanisms for model drift
- Escalation paths for bias complaints
- Communication plan for affected users
- Forensic data preservation steps
- Root cause analysis for model failures
- Regulatory reporting thresholds
- Corrective action tracking system
- Post-mortem process with engineering teams
- Updating training data after incidents
- Public statement coordination
- Template for incident response playbook
Module 11. Continuous Monitoring Framework
Implement ongoing oversight of AI systems to ensure sustained compliance. Use automated alerts, periodic reviews, and human checks to maintain trust and detect issues early.
12 chapters in this module
- Key metrics for AI system health
- Automated drift detection thresholds
- Bias monitoring across demographic groups
- Performance degradation alerts
- Model retraining triggers
- Human review sampling intervals
- Third-party model monitoring
- Logging for explainability requests
- Feedback loop integration from users
- Dashboard design for oversight teams
- Review cycle for control effectiveness
- Updating monitoring rules after audits
Module 12. Sustaining Governance Over Time
Ensure AI governance evolves with your organization. Build playbooks that survive leadership changes and scale across teams. Institutionalize knowledge so decisions compound rather than reset.
12 chapters in this module
- Onboarding new team members to governance
- Knowledge transfer planning for departures
- Versioning governance artifacts
- Centralized repository for policies and evidence
- Cross-team alignment workshops
- Updating governance after M&A activity
- Lessons learned from certification cycles
- Benchmarking against industry peers
- Succession planning for ownership roles
- Measuring maturity over time
- Sharing best practices across divisions
- Template for annual governance review
How this maps to your situation
- Defining AI system boundaries
- Assigning ownership of controls
- Designing evidence workflows
- Sustaining governance through team changes
Before vs. after
Before
Decisions on AI governance scope require constant justification and re-review
After
You own final sign-off with documented rationale that holds across cycles
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: 90 minutes per week for four weeks, with flexible access and downloadable resources
If nothing changes
Without structured governance, even high-performing teams face repeated auditor challenges, peer disputes over scope, and delays from rework, eroding trust in technical leadership when it's needed most
How this compares to the alternatives
Unlike generic compliance courses, this is built for senior AI engineering leaders who must balance innovation with accountability. No theoretical overviews, only actionable frameworks used in real certification efforts.
Frequently asked
Is this relevant if we're not certified yet?
Yes, it prepares you to lead certification with confidence and avoid costly rework.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I share this with my team?
Each purchase is for individual use, but templates can be adapted for team adoption.
$199 one-time. 90 minutes per week for four weeks, with flexible access and downloadable resources.
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