A tailored course, built for your situation
Mastering ISO 42001 for Software Engineers in Regulated Environments
Build compliant AI systems from design to deployment with confidence
$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.
AI governance is no longer a post-deployment checklist, it’s embedded in code, owned by engineers
The situation this course is for
Teams ship AI features that later fail compliance reviews because governance was an afterthought. Engineers lack a clear framework to design with compliance built in from day one.
Who this is for
Mid-seniority software engineer in regulated IT services, delivering systems with AI components under compliance constraints
Who this is not for
Executives looking for board-level summaries, auditors seeking control testing templates, or data scientists focused purely on model accuracy
What you walk away with
- Define AI governance boundaries within engineering , early in the SDLC
- Produce ISO 42001-aligned documentation that satisfies compliance reviewers
- Anticipate auditor questions and embed evidence collection into development workflows
- Position yourself as the engineering anchor for AI governance rollouts
- Reduce rework by aligning code structure with ISO 42001 control objectives
The 12 modules (with all 144 chapters)
Module 1. Why ISO 42001 is engineered for practitioners, not auditors
Understand how ISO 42001 was designed to be implemented in code, not just documented in spreadsheets. This module reframes compliance as a structural engineering task.
12 chapters in this module
- How ISO 42001 differs from traditional compliance frameworks
- The three pillars of AI governance in software delivery
- Mapping controls to code architecture decisions
- Engineering roles that naturally own each clause
- Avoiding compliance theatre in agile environments
- When to escalate versus when to implement locally
- Integrating clause 6.3 into sprint planning
- How clause 8.4 shapes third-party AI component selection
- Designing evidence collection into CI/CD pipelines
- Versioning AI governance decisions with code
- Using ISO 42001 to justify technical debt reduction
- Aligning with security teams without slowing delivery
Module 2. Clause 4.1 and understanding organizational context in AI systems
Go beyond abstract 'context' descriptions and define what it means for your software stack, clients, and deployment model.
12 chapters in this module
- Defining organizational context for AI-enabled services
- Identifying external parties in your AI value chain
- Documenting AI use cases with stakeholder impact
- Mapping regulatory domains to system boundaries
- How CGI’s client portfolio shapes your scope
- Integrating legal agreements into context statements
- Updating context during project lifecycle phases
- Using context to justify architectural choices
- Linking clause 4.1 to data governance policies
- Challenges with multi-jurisdictional AI deployments
- Documenting context without over-engineering
- Versioning context as systems evolve
Module 3. Clause 4.2 and capturing real user needs in AI governance
Move beyond checkbox requirements to embed actual user expectations into system design and validation.
12 chapters in this module
- Differentiating user roles in AI systems
- Capturing transparency expectations in requirements
- Documenting explainability expectations by role
- How users define 'fairness' in your domain
- Integrating feedback mechanisms into AI loops
- Balancing automation with human oversight
- User input on model update frequency
- Defining acceptable error rates by use case
- Handling contested outcomes in production
- Updating user needs as regulations shift
- Linking clause 4.2 to acceptance testing
- Evidence collection for user representation
Module 4. Clause 4.3 defining scope with technical precision
Learn how to draw boundaries that reflect actual system capabilities and avoid over承诺.
12 chapters in this module
- Scoping AI systems without overreach
- Defining in-scope versus out-of-scope components
- Documenting integration points with legacy systems
- Handling third-party AI services in scope definition
- When to split or combine AI management systems
- Using architecture diagrams to support scope claims
- Aligning scope with deployment environments
- Scope updates after system changes
- Avoiding scope inflation in client demands
- Legal implications of scope misrepresentation
- Versioning scope documentation
- Presenting scope to internal audit teams
Module 5. Clause 4.4 building the AI management system into code
Implement ISO 42001 as code structures, not standalone documents.
12 chapters in this module
- Representing AI governance in system architecture
- Using microservices to enforce compliance boundaries
- Embedding logging for auditability by default
- Designing modularity for governance updates
- How configuration management supports clause 4.4
- Version control strategies for governance logic
- Documentation as code in AI systems
- Testing governance components in CI/CD
- Managing technical debt in AI modules
- Handling dependencies in AI frameworks
- Security controls within containerized AI
- Reusability of governance components across projects
Module 6. Clause 5 leadership commitment from engineering leads
Demonstrate leadership accountability through technical decisions, not just policy statements.
12 chapters in this module
- Defining leadership roles in technical governance
- Commitment signals in architecture review outcomes
- How code ownership models reflect clause 5.1
- Technical debt reduction as leadership action
- Enforcing code quality standards as commitment
- Handling exceptions to governance policies
- Mentorship as leadership in AI systems
- Communication of AI principles in team rituals
- Incentivizing compliance-aware development
- Documenting leadership actions in code reviews
- Linking sprint goals to AI governance
- Escalation paths for governance conflicts
Module 7. Clause 6.1 addressing risks and opportunities in AI development
Operationalize risk assessments directly into development workflows and design choices.
12 chapters in this module
- Integrating risk assessment into sprint zero
- Common AI risks in regulated service environments
- Opportunity mapping for AI compliance
- Risk treatment options in software design
- Documenting risk decisions in code comments
- Automating risk control monitoring
- Third-party AI component risk assessment
- Model drift as an ongoing risk
- Human-in-the-loop as risk mitigation
- Risk register integration with Jira
- Reporting risk trends to compliance teams
- Updating risk assessments after incidents
Module 8. Clause 7 support including competence and awareness in teams
Ensure your team has the right skills and knowledge to implement ISO 42001 effectively.
12 chapters in this module
- Identifying required competencies for AI governance
- Onboarding engineers into compliance practices
- Documentation standards for AI systems
- Version control of governance assets
- Internal communication of AI policies
- Training needs for legacy system integration
- Knowledge transfer between projects
- Using templates to maintain consistency
- Storing artifacts for audit readiness
- Access control for governance documentation
- Updating knowledge assets after audits
- Measuring team awareness effectiveness
Module 9. Clause 8.1 operational planning and control in AI workflows
Integrate governance into daily development, not just audits.
12 chapters in this module
- Integrating ISO 42001 into SDLC phases
- Defining controls for model training pipelines
- Versioning AI models and metadata
- Data quality controls in AI systems
- Human review requirements in deployment
- Monitoring AI outputs in production
- Change management for AI components
- Incident handling for AI failures
- Backup and recovery for AI services
- User feedback integration into improvements
- Performance metrics for AI governance
- Alignment with service level agreements
Module 10. Clause 8.2 controlling AI system acquisition and development
Apply ISO 42001 principles to both in-house and third-party AI components.
12 chapters in this module
- Acquisition criteria for third-party AI tools
- Vendor evaluation against ISO 42001
- Contractual requirements for AI compliance
- Due diligence for open-source AI components
- Development standards for internal AI
- Security testing in AI pipelines
- Bias testing methodology in development
- Transparency documentation requirements
- Model validation requirements
- Human oversight integration design
- Acceptance testing with compliance criteria
- Handover processes to operations teams
Module 11. Clause 9.1 monitoring and measuring AI governance performance
Move beyond pass/fail audits to continuous compliance metrics.
12 chapters in this module
- Defining KPIs for AI governance
- Tracking ISO 42001 implementation progress
- Auditing code against controls automatically
- Incident tracking and analysis
- User satisfaction with AI transparency
- Compliance testing frequency metrics
- Rework reduction from early governance
- Time to resolve compliance findings
- Audit readiness scoring
- Benchmarking against peer projects
- Reporting metrics to internal stakeholders
- Using data to justify governance investment
Module 12. Clause 10.2 continual improvement through engineering feedback
Use real-world system behavior to refine governance over time.
12 chapters in this module
- Identifying improvement opportunities in logs
- Root cause analysis of compliance incidents
- Feedback loops from operations teams
- Updating controls after model updates
- Lessons learned from audit findings
- Improvement tracking in project management
- Prioritizing governance enhancements
- Change management for control updates
- Validating improvements in staging
- Communication of improvements to stakeholders
- Versioning control updates
- Sustaining improvement momentum
How this maps to your situation
- Pre-development planning and scoping
- Architecture and design decisions
- Development workflow integration
- Third-party and client delivery governance
Before vs. after
Before
Governance feels like a separate track handled after development, leading to rework and misalignment.
After
You lead governance integration from the start, reducing escalations and expanding your role within engineering.
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 of focused learning, designed for engineers with limited bandwidth.
If nothing changes
Without structured governance, engineers face repeated rework, escalated findings, and missed opportunities to shape AI systems proactively.
How this compares to the alternatives
Generic compliance courses focus on auditors and paperwork. This course speaks your language , code, architecture, and delivery , with actionable steps you can apply immediately.
Frequently asked
Do I need prior ISO knowledge?
No. This course starts from first principles and builds to implementation, tailored for engineers.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I don't work on AI?
Yes , the principles apply to any automated decision system, especially in regulated environments.
$199 one-time. 90 minutes of focused learning, designed for engineers with limited bandwidth..
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