A tailored course, built for your situation
Mastering ISO 42001 for Software Engineering Leaders
A step-by-step path to owning AI governance scope in your current role
$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 decisions are being made without your input, despite your team’s delivery ownership
The situation this course is for
Engineering leaders are expected to deliver AI systems that comply with emerging standards, yet often lack formal authority over governance frameworks, leading to misalignment, rework, and unclear accountability
Who this is for
Software Engineering Manager in government technology or defense contracting, leading teams building AI-integrated systems under compliance mandates
Who this is not for
Individual contributors without team leadership responsibilities, or practitioners focused solely on non-AI software maintenance
What you walk away with
- Lead ISO 42001 compliance efforts with confidence in your current role
- Own end-to-end AI governance review cycles without escalation
- Produce complete Statements of Applicability on schedule
- Streamline vendor assessments using standardised evaluation matrices
- Establish repeatable control mapping processes across projects
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 Scope and Relevance
Clarify the boundaries of AI management systems and how they integrate with existing engineering workflows in regulated environments.
12 chapters in this module
- Defining AI under ISO 42001
- Mapping organisational context
- Identifying interested parties
- Determining scope boundaries
- Linking to existing compliance frameworks
- Assessing AI system maturity
- Classifying AI risk levels
- Documenting assumptions
- Establishing governance foundation
- Aligning with NIST AI RMF
- Integrating with DevOps pipelines
- Setting measurable objectives
Module 2. Leadership Commitment and Policy Development
Build executive-grade AI governance policies that reflect technical reality and secure lasting organisational buy-in.
12 chapters in this module
- Securing leadership endorsement
- Writing AI principles
- Developing enforceable policies
- Setting accountability structures
- Defining decision rights
- Creating version control
- Aligning with ethics guidelines
- Integrating with vendor contracts
- Establishing review cycles
- Documenting policy exceptions
- Training rollout plans
- Measuring policy adherence
Module 3. Planning AI Risk Management
Implement a systematic approach to identifying, assessing, and treating AI-specific risks across the lifecycle.
12 chapters in this module
- Identifying AI hazards
- Threat modelling techniques
- Risk assessment criteria
- Determining risk appetite
- Developing treatment plans
- Assigning risk owners
- Creating risk registers
- Linking to SOC 2 controls
- Validating mitigation efficacy
- Updating risk profiles
- Automating risk detection
- Reporting to oversight groups
Module 4. Building Organisational Capabilities
Strengthen team structure, roles, and competence frameworks to support sustained AI governance.
12 chapters in this module
- Defining governance roles
- Establishing RACI matrices
- Assessing skill gaps
- Planning training initiatives
- Documenting knowledge transfer
- Creating playbooks
- Setting up working groups
- Running tabletop exercises
- Evaluating vendor expertise
- Managing third-party risk
- Tracking certification paths
- Improving cross-functional coordination
Module 5. Resource Planning and Allocation
Ensure appropriate infrastructure, data, and computational resources support compliant AI development.
12 chapters in this module
- Assessing hardware needs
- Securing data pipelines
- Validating data quality
- Managing compute costs
- Ensuring reproducibility
- Planning for scalability
- Documenting dependencies
- Establishing monitoring
- Setting performance baselines
- Allocating budget reserves
- Optimising cloud usage
- Maintaining audit trails
Module 6. Competence Development Framework
Design structured paths for building and verifying team capabilities in AI ethics, safety, and compliance.
12 chapters in this module
- Defining role competencies
- Assessing baseline skills
- Creating learning plans
- Validating knowledge
- Running certification programs
- Establishing mentoring
- Tracking progress
- Auditing team readiness
- Integrating with performance reviews
- Updating training content
- Recognising expertise
- Maintaining accreditation
Module 7. Awareness and Communication Strategy
Drive organisational adoption of AI governance principles through effective internal communication.
12 chapters in this module
- Identifying audiences
- Crafting messaging
- Choosing channels
- Scheduling rollouts
- Creating FAQs
- Running workshops
- Tracking engagement
- Gathering feedback
- Addressing concerns
- Promoting best practices
- Recognising champions
- Maintaining momentum
Module 8. Data Management for AI Systems
Implement compliant data handling, lineage tracking, and quality assurance specific to AI workloads.
12 chapters in this module
- Establishing data provenance
- Ensuring representativeness
- Managing bias testing
- Documenting preprocessing
- Validating labelling accuracy
- Securing datasets
- Establishing retention rules
- Implementing consent checks
- Auditing data changes
- Integrating with Databricks
- Tracking model drift
- Reporting data incidents
Module 9. AI System Lifecycle Controls
Apply ISO 42001 requirements across development, deployment, and decommissioning phases.
12 chapters in this module
- Defining development standards
- Implementing code reviews
- Validating model performance
- Setting deployment gates
- Monitoring in production
- Creating rollback plans
- Managing updates
- Tracking version lineage
- Conducting post-mortems
- Decommissioning protocols
- Archiving artefacts
- Reviewing system impact
Module 10. Verification and Validation Processes
Build confidence in AI system behaviour through structured testing, monitoring, and audit readiness.
12 chapters in this module
- Designing test suites
- Validating against requirements
- Assessing fairness
- Testing robustness
- Evaluating explainability
- Measuring reliability
- Running stress tests
- Creating monitoring dashboards
- Setting alert thresholds
- Preparing for audits
- Documenting test results
- Reporting validation status
Module 11. Monitoring and Performance Evaluation
Establish continuous oversight mechanisms to ensure AI systems operate as intended.
12 chapters in this module
- Defining KPIs
- Tracking performance degradation
- Monitoring for bias shifts
- Logging decision outcomes
- Analysing user feedback
- Reviewing incident reports
- Updating risk assessments
- Running periodic audits
- Benchmarking against peers
- Reporting to governance boards
- Updating control effectiveness
- Planning improvement cycles
Module 12. Continual Improvement and Audit Readiness
Create sustainable improvement loops and prepare for internal and external ISO 42001 audits.
12 chapters in this module
- Collecting improvement inputs
- Prioritising actions
- Implementing changes
- Measuring impact
- Updating documentation
- Preparing for audits
- Responding to findings
- Creating action plans
- Demonstrating compliance
- Maintaining records
- Renewing certification
- Sharing learnings
How this maps to your situation
- Leading AI governance without formal authority
- Aligning engineering delivery with compliance mandates
- Responding to auditor requests with confidence
- Expanding influence across cross-functional teams
Before vs. after
Before
AI governance decisions happen outside your team's influence, despite your delivery ownership.
After
Your processes are the reference standard, auditors come to you, peers defer to your framework, and your documentation sets the pace.
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: Approximately 3 hours per module, designed to be completed alongside regular responsibilities over 6-8 weeks.
If nothing changes
Continuing without structured AI governance leaves your team exposed to rework, compliance gaps, and loss of influence on critical system decisions.
How this compares to the alternatives
Unlike generic compliance training, this course delivers role-specific implementation patterns for software engineering leads in regulated environments, focused on actionable artefacts rather than theoretical frameworks.
Frequently asked
Who is this course designed for?
Software Engineering Managers in government, defense, or regulated sectors leading teams that develop or deploy AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get promoted?
The focus is expanding your mandate in your current role, owning decisions, setting standards, and gaining trusted authority without waiting for a title change.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside regular responsibilities over 6-8 weeks..
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