A tailored course, built for your situation
Mastering ISO 42001 for Platform Engineers in Global Systems Integration
Build an AI governance asset that compounds across every delivery
$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.
Who this is for
Platform Engineer at a global systems integrator working on AI governance integration projects
Who this is not for
Engineers focused solely on local infrastructure without cross-client delivery exposure
What you walk away with
- Turn individual ISO 42001 implementation tasks into a repeatable IP library
- Produce artefacts that become the default in future proposals and audits
- Reduce time to framework compliance by 40% on subsequent engagements
- Gain recognition from delivery leadership as the source of scalable solutions
- Document a personal playbook that survives team reshuffles and client changes
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Role in AI Governance
Lays the foundation by defining ISO 42001’s scope, objectives, and relationship to AI risk management in enterprise systems. Explores how platform engineers are positioned to shape implementation through architecture decisions.
12 chapters in this module
- Defining ISO 42001 and its governance context
- How AI governance differs from general AI policy
- The role of platform engineers in compliance architecture
- Mapping ISO 42001 clauses to integration touchpoints
- Identifying client-driven compliance requirements
- Common misconceptions about AI management systems
- How ISO 42001 interfaces with other frameworks
- Vendor obligations under AI governance standards
- Timing considerations for early adoption
- Regulator expectations in AI audit scenarios
- Internal stakeholder alignment priorities
- Baseline assessment for current AI posture
Module 2. Establishing Leadership and Organizational Context
Covers how to define governance boundaries, identify interested parties, and document decision rights. Focuses on how platform engineers can lead without authority by shaping implementation design early.
12 chapters in this module
- Defining organizational context for AI systems
- Identifying internal and external stakeholders
- Determining compliance boundaries and scope
- Documenting leadership intent and oversight
- Assigning roles in AI governance structure
- Integrating AI policy with existing frameworks
- Aligning with enterprise risk management goals
- Clarifying responsibilities across teams
- Setting expectations for cross-functional input
- Managing conflicting priorities in governance
- Building credibility as technical authority
- Securing early buy-in from delivery leads
Module 3. Developing an AI Governance Policy
Guides the creation of a clear, enforceable AI governance policy aligned with ISO 42001 requirements. Emphasizes language that platform teams can implement consistently across projects.
12 chapters in this module
- Core principles of an enforceable AI policy
- Setting intent for ethical AI use cases
- Defining prohibited and high-risk applications
- Incorporating human oversight requirements
- Aligning policy with client contractual terms
- Handling data provenance and bias controls
- Policy documentation standards
- Approval workflows for governance updates
- Version control for policy changes
- Integrating policy with DevOps pipelines
- Training requirements for policy adoption
- Auditing policy adherence across deployments
Module 4. Planning for AI Risk and Opportunity Assessment
Provides tools to assess AI-related risks and opportunities systematically. Shows how to embed assessments into delivery lifecycles and avoid rework.
12 chapters in this module
- Framework for AI risk categorization
- Identifying high-risk AI use cases
- Defining risk tolerance thresholds
- Assessing societal and operational impacts
- Evaluating explainability and transparency needs
- Documenting risk treatment plans
- Integrating risk assessment into sprint planning
- Creating risk registers for client reporting
- Validating assumptions with real data
- Benchmarking against industry baselines
- Updating risk profiles over time
- Using historical data to refine future assessments
Module 5. Implementing AI Management System Controls
Details technical and procedural controls required by ISO 42001. Focuses on implementation patterns that scale across environments and teams.
12 chapters in this module
- Control selection based on risk profile
- Data management and quality assurance
- Model development lifecycle controls
- Versioning and reproducibility standards
- Validation and testing requirements
- Human oversight integration points
- Performance monitoring and logging
- Incident response for AI failures
- Change management for model updates
- Security controls for model deployment
- Access control and role-based permissions
- Control documentation for audit readiness
Module 6. Documenting and Maintaining AI Information
Teaches how to create and maintain documentation that satisfies auditors and supports continuity. Emphasizes automation and integration with existing toolchains.
12 chapters in this module
- Required documentation under ISO 42001
- Automating artefact generation from code
- Maintaining model lineage records
- Storing training data provenance
- Logging decisions in governance repositories
- Integrating documentation into CI/CD flow
- Standardizing naming and metadata
- Version control for governance assets
- Accessing documentation across teams
- Audit trail requirements for regulators
- Retention policies for AI records
- Exporting documentation for client handover
Module 7. Operating AI Governance Processes
Covers day-to-day operation of AI governance, including monitoring, incident response, and continuous improvement. Shows how to make compliance sustainable.
12 chapters in this module
- Daily monitoring of AI system behavior
- Alerting on performance degradation
- Handling model drift and concept shift
- Incident reporting and escalation paths
- Post-incident review procedures
- Updating models based on feedback
- User support for AI-related issues
- Change request management process
- Handling model decommissioning
- Scheduling periodic control reviews
- Updating documentation after changes
- Ensuring continuity across team changes
Module 8. Evaluating Performance and Conformance
Provides methods to measure the effectiveness of AI governance controls. Includes metrics, audit preparation, and feedback loops.
12 chapters in this module
- Defining KPIs for AI governance
- Measuring control effectiveness over time
- Auditor expectations for evidence
- Preparing for internal audits
- Responding to auditor inquiries
- Using metrics for continuous improvement
- Benchmarking against peer organizations
- Gathering stakeholder feedback
- Identifying gaps in implementation
- Updating controls based on findings
- Reporting results to leadership
- Maintaining independence in assessment
Module 9. Conducting Internal Audits
Guides how to plan and execute internal audits of AI governance systems. Emphasizes objectivity, evidence collection, and reporting.
12 chapters in this module
- Planning the internal audit schedule
- Selecting audit scope and objectives
- Assembling audit teams and roles
- Collecting evidence from systems
- Interviewing process owners
- Reviewing documentation completeness
- Assessing control implementation
- Identifying non-conformities
- Reporting audit findings clearly
- Tracking corrective actions
- Verifying closure of findings
- Maintaining auditor independence
Module 10. Managing Nonconformities and Corrective Actions
Covers how to identify, document, and resolve nonconformities. Focuses on root cause analysis and preventing recurrence.
12 chapters in this module
- Identifying and logging nonconformities
- Classifying severity and impact
- Initiating corrective action workflows
- Conducting root cause analysis
- Developing corrective action plans
- Assigning responsibility for resolution
- Tracking progress on actions
- Verifying effectiveness of fixes
- Preventing recurrence through design
- Updating policies based on findings
- Reporting status to leadership
- Archiving records for future reference
Module 11. Leading Management Reviews
Prepares platform engineers to contribute meaningfully to management reviews of AI governance. Focuses on data-driven insights and strategic recommendations.
12 chapters in this module
- Scheduling management review cycles
- Agenda development for governance reviews
- Compiling performance metrics
- Highlighting key risks and issues
- Presenting audit results and trends
- Proposing improvements to governance
- Aligning with business objectives
- Assessing resource needs
- Evaluating external changes
- Documenting review outcomes
- Tracking decisions and follow-ups
- Ensuring executive engagement
Module 12. Continual Improvement of AI Governance
Teaches how to institutionalize ongoing improvement. Shows how to leverage past work to reduce effort in future projects.
12 chapters in this module
- Establishing feedback loops for improvement
- Using lessons learned from audits
- Benchmarking against emerging practices
- Adopting new tools and techniques
- Sharing best practices across teams
- Updating governance based on incidents
- Aligning with regulatory changes
- Scaling improvements across clients
- Reducing effort through reuse
- Building organizational memory
- Recognizing contributors publicly
- Maintaining momentum over time
How this maps to your situation
- Initial client onboarding and scoping
- Architecture design phase with governance input
- First audit preparation and evidence gathering
- Post-engagement review and knowledge transfer
Before vs. after
Before
Working through each AI governance project from scratch, recreating solutions and responses without a unified library.
After
Leveraging a growing IP library that accelerates compliance, reduces rework, and establishes you as the source of truth across engagements.
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 six weeks, with flexible access to all materials
If nothing changes
Without a structured approach, each new project demands full reinvention, increasing effort, inconsistency, and exposure to audit findings due to fragmented controls.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to platform engineers integrating AI governance into delivery projects. It focuses on practical implementation, not theory, and builds assets that compound value over time.
Frequently asked
Who is this course designed for?
Platform engineers leading or contributing to AI governance integration in systems delivery projects, especially those working across multiple clients or engagements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an audit?
Yes, by teaching you how to build artefacts and processes that consistently meet ISO 42001 requirements, and by giving you a reusable library for future audits.
$199 one-time. 90 minutes per week over six weeks, with flexible access to all materials.
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