A tailored course, built for your situation
Mastering ISO 42001 for AI Governance Practitioners
A step-by-step system to turn AI governance intent into auditable artefacts in half the time
$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 between policy and proof, this course closes the delivery gap
The situation this course is for
Teams draft controls but struggle to produce working statements of applicability or audit-ready documentation without multiple revisions and cross-functional bottlenecks
Who this is for
Senior AI governance practitioner leading internal red teaming and compliance validation in enterprise tech
Who this is not for
Entry-level auditors, general compliance staff, or those not actively producing AI governance artefacts
What you walk away with
- Produce a complete ISO 42001 Statement of Applicability in under 5 days
- Map AI-specific controls to evidence requirements without rework
- Turn red team findings into structured compliance updates in one pass
- Reduce cross-functional review cycles by 60% with pre-validated templates
- Ship aligned governance artefacts across AI development, security, and audit teams
The 12 modules (with all 144 chapters)
Module 1. Why ISO 42001 Is the Foundation for AI Governance
Understand how ISO 42001’s structure aligns with AI risk domains and why early adopters are gaining execution leverage.
12 chapters in this module
- How ISO 42001 emerged from AI incident response patterns
- Core components of AI governance covered in the standard
- Mapping AI lifecycle stages to ISO 42001 clauses
- Why enterprises are choosing ISO 42001 over internal frameworks
- Key differences between ISO 42001 and SOC 2 for AI systems
- How ISO 27001 controls extend into AI-specific risks
- Real-world examples of ISO 42001 adoption in tech firms
- Timeline of ISO 42001 implementation at global enterprises
- Common misconceptions about AI governance standards
- How red team findings inform ISO 42001 control selection
- Benchmarking your maturity against ISO 42001 readiness
- First steps when initiating an ISO 42001 rollout
Module 2. Setting Up Your ISO 42001 Governance Scope
Define boundaries that include AI development pipelines, red team access, and model deployment workflows.
12 chapters in this module
- Identifying AI systems in scope for ISO 42001 compliance
- Documenting data flows in AI training and inference
- Including third-party AI tools in the governance boundary
- Establishing roles for red team and blue team integration
- Defining ownership for model lifecycle governance
- Scoping multi-cloud AI environments under one framework
- Excluding non-AI systems without weakening coverage
- Aligning scope with existing security architecture
- Versioning your scope document for audit readiness
- Getting sign-off from technical leadership
- Integrating legal and risk team inputs early
- Avoiding scope creep while maintaining coverage
Module 3. Building the AI Risk Assessment Framework
Develop a repeatable process to identify, score, and prioritize AI-specific risks using ISO 42001 guidance.
12 chapters in this module
- Common threat vectors in AI model development
- Classifying data sensitivity in training datasets
- Assessing model explainability as a control gap
- Evaluating bias and fairness risks systematically
- Scoring model drift and concept drift exposure
- Incorporating adversarial testing results into risk logs
- Using red team findings to weight risk severity
- Mapping risks to ISO 42001 control objectives
- Automating risk scoring with templated workflows
- Validating risk assessments with peer review
- Updating risk registers after model retraining
- Producing audit-ready risk documentation
Module 4. Designing AI-Specific Control Objectives
Translate ISO 42001 clauses into actionable controls for model development, testing, and deployment.
12 chapters in this module
- Adapting A.8.1 for AI model access governance
- Implementing A.8.2 for prompt injection defenses
- Extending A.8.3 to cover synthetic data usage
- Applying A.8.4 to model versioning and rollback
- Securing model APIs under A.8.5 controls
- Configuring monitoring for model output anomalies
- Enforcing human-in-the-loop requirements
- Documenting control rationale with red team input
- Linking controls to model risk tiers
- Integrating model cards into control evidence
- Aligning controls with MLOps pipeline stages
- Testing control effectiveness with blue team
Module 5. Evidence Collection for AI Governance
Streamline documentation workflows to meet ISO 42001 audit requirements without slowing development.
12 chapters in this module
- Required artefacts for ISO 42001 certification
- Automating evidence capture from CI/CD pipelines
- Generating logs for model training and validation
- Documenting red team test plans and results
- Capturing model validation reports systematically
- Storing artefacts in version-controlled repositories
- Using templates to standardize evidence formats
- Linking evidence to control mapping spreadsheets
- Ensuring evidence meets external auditor needs
- Reducing evidence collection time with checklists
- Preparing evidence packs for internal review
- Maintaining evidence confidentiality and access
Module 6. Creating a Statement of Applicability for AI
Build a living SoA that reflects AI-specific control decisions and justifications.
12 chapters in this module
- Structure of an ISO 42001 Statement of Applicability
- Including AI-specific controls beyond the base list
- Justifying exclusion of non-relevant clauses
- Linking each control to implementation status
- Incorporating red team findings into justifications
- Versioning the SoA for ongoing updates
- Aligning SoA with model risk appetite statements
- Using SoA to guide blue team validation
- Presenting SoA to internal audit teams
- Updating SoA after major AI incidents
- Integrating SoA into vendor assessment questionnaires
- Automating SoA updates from control dashboards
Module 7. Integrating Red and Blue Team Workflows
Operationalize continuous testing and validation cycles that feed into ISO 42001 compliance.
12 chapters in this module
- Defining red team scope under ISO 42001
- Scheduling adversarial testing aligned with audits
- Documenting attack simulations for evidence
- Feeding red team findings into control updates
- Using blue team validation to confirm fixes
- Tracking remediation timelines in risk registers
- Integrating findings into model retraining gates
- Reporting red team results to compliance leads
- Maintaining independence while sharing data
- Aligning test calendars with audit cycles
- Automating finding ingestion into governance tools
- Reducing time from finding to resolution
Module 8. Auditor Readiness for AI Systems
Prepare for internal and external audits with structured narratives and complete evidence trails.
12 chapters in this module
- Common auditor questions about AI governance
- Preparing responses for model risk oversight
- Organizing evidence by ISO 42001 control clause
- Demonstrating continuous monitoring capabilities
- Showing red team integration in control design
- Explaining model explainability efforts
- Justifying bias testing frequency and scope
- Presenting model drift detection mechanisms
- Documenting human oversight processes
- Providing access to versioned model cards
- Answering follow-ups on training data provenance
- Reducing audit clarification cycles
Module 9. Scaling Governance Across AI Initiatives
Extend ISO 42001 compliance to new AI projects without duplicating effort.
12 chapters in this module
- Creating reusable governance templates
- Standardizing model risk assessments
- Using central control libraries for consistency
- Onboarding new AI teams to the framework
- Integrating ISO 42001 into project kickoffs
- Automating policy dissemination
- Maintaining governance consistency across clouds
- Tracking compliance across business units
- Reducing onboarding time for new projects
- Updating central playbooks from lessons learned
- Scaling red team coverage efficiently
- Measuring governance velocity across teams
Module 10. Maintaining ISO 42001 Compliance Over Time
Keep governance current as AI models evolve and new threats emerge.
12 chapters in this module
- Scheduling periodic control reviews
- Updating risk assessments after model changes
- Revalidating controls post-incident
- Incorporating new red team findings
- Tracking changes in model performance metrics
- Reassessing data sensitivity classifications
- Updating SoA after architecture changes
- Managing version control for governance docs
- Auditing access to model development environments
- Ensuring continuity during team transitions
- Integrating new regulatory guidance
- Planning for recertification cycles
Module 11. Driving Adoption Across Engineering Teams
Gain buy-in from developers and data scientists by aligning governance with delivery goals.
12 chapters in this module
- Framing ISO 42001 as an enabler, not a gate
- Integrating controls into developer workflows
- Reducing friction in model submission processes
- Providing clear guidance for common scenarios
- Using automation to reduce manual steps
- Demonstrating time savings from rework avoidance
- Sharing red team learnings across teams
- Celebrating compliance wins in team forums
- Linking governance to developer incentives
- Reducing cycle time through standardization
- Onboarding new engineers with self-serve tools
- Measuring developer satisfaction with governance
Module 12. Optimizing for Velocity and Audit Success
Balance speed of innovation with compliance rigor using proven patterns.
12 chapters in this module
- Measuring time from model idea to production
- Tracking audit findings resolution time
- Benchmarking against peer organizations
- Reducing rework through upfront design
- Using red team feedback to prevent defects
- Aligning sprint goals with control delivery
- Integrating compliance into CI/CD pipelines
- Automating evidence generation
- Reducing time to close audit findings
- Increasing first-time pass rate for reviews
- Documenting improvements in governance velocity
- Sustaining pace without sacrificing quality
How this maps to your situation
- Defining AI governance scope
- Conducting AI-specific risk assessments
- Implementing controls in MLOps pipelines
- Preparing for ISO 42001 certification
Before vs. after
Before
Spending weeks turning AI governance policies into audit-ready documentation, juggling feedback loops between teams, and facing last-minute requests for evidence.
After
Producing complete, aligned ISO 42001 artefacts in days, not weeks, with templates, checklists, and a field-tested sequence that ships on time, every time.
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 for four weeks, or complete in one intensive weekend.
If nothing changes
Without a streamlined approach, AI governance remains reactive, leading to delayed launches, repeated audit findings, and missed opportunities to shape enterprise AI direction.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for AI governance practitioners who need to deliver ISO 42001 artefacts quickly. No fluff, no theory, just actionable steps used by teams at Fortune 500s to ship faster.
Frequently asked
Who is this course designed for?
Senior AI governance practitioners leading red teaming, compliance, or audit readiness in enterprise environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an ISO 42001 audit?
Yes, this course gives you the templates, workflows, and field-tested sequences used by teams that passed first-time.
$199 one-time. 90 minutes per week for four weeks, or complete in one intensive weekend..
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