A tailored course, built for your situation
Mastering ISO 42001 for QA Automation Engineers
Build auditable, regulator-facing AI governance artefacts with confidence and precision
$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.
Generic AI governance training doesn’t reflect the precision required in QA-led compliance cycles
The situation this course is for
Most ISO 42001 courses are built for auditors or compliance generalists. They miss the QA engineer’s role in proving control effectiveness through automated validation, version-tracked artefacts, and test-backed audit responses. Practitioners like Pankil need a path that honors their technical rigor while expanding their governance authority.
Who this is for
QA Automation Engineer at a global IT services firm, responsible for test frameworks that validate compliance controls, with growing exposure to AI governance and internal audit cycles
Who this is not for
This is not for compliance auditors, risk consultants, or executives seeking board-level narratives. It’s for technical practitioners who own the artefact, not the policy slide.
What you walk away with
- Own the full ISO 42001 control mapping cycle with test-backed validation
- Produce regulator-facing documentation that survives technical scrutiny
- Lead internal audit prep without senior review loops
- Become the default escalation point for peer teams on AI governance issues
- Deliver working statements of applicability that close review cycles faster
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 42001 in AI Governance
Understand the structure and intent of ISO 42001, especially as it applies to automated systems validation and governance by technical teams.
12 chapters in this module
- What ISO 42001 regulates
- AI governance vs traditional compliance
- The role of QA engineers in control ownership
- Mapping controls to automation workflows
- Key differences from ISO 27001
- Auditor expectations for technical teams
- How QA rigor strengthens governance credibility
- Common misconceptions in AI compliance
- Real-world adoption patterns in IT services
- Why automation engineers are best positioned to lead
- Case example: First AI audit package at CGI
- Foundational terms and scope
Module 2. Control Identification and Scoping
Learn how to define the boundary of AI governance for your projects using ISO 42001’s control set, with focus on testable outcomes.
12 chapters in this module
- Defining AI system scope
- Identifying high-risk AI components
- Linking controls to automation test suites
- Avoiding scope creep in governance
- Documenting rationale for exclusions
- Using QA logs as evidence sources
- Versioning control scope over time
- Peer review of scope documents
- Handling vendor-built AI components
- Mapping to internal audit requirements
- Common pitfalls in control scoping
- Worked example: Scope for an NLP pipeline
Module 3. Risk Assessment for AI Systems
Build technical risk assessments grounded in actual QA findings, not hypotheticals, to satisfy ISO 42001 requirements.
12 chapters in this module
- Risk vs control in AI systems
- QA findings as risk inputs
- Classifying model behavior risks
- Data lineage and risk tracing
- Bias detection in test outputs
- Using automation to quantify exposure
- Documenting risk decisions technically
- Linking risk to control design
- Review cycles with compliance teams
- Handling third-party model risks
- Risk register format for auditors
- Case example: Risk assessment for chatbot
Module 4. Designing Testable Controls
Turn ISO 42001 controls into automated validation scripts and documented checkpoints.
12 chapters in this module
- What makes a control testable
- Mapping clause 8 to QA scripts
- Automating fairness checks
- Version-controlled control logic
- Logging control execution results
- Integrating with CI/CD pipelines
- Handling false positives in control tests
- Peer validation of control design
- Maintaining control effectiveness
- Documenting control failure paths
- Audit readiness of control artefacts
- Worked example: Control for model drift
Module 5. Statement of Applicability (SoA)
Produce a technically rigorous SoA that reflects actual system capabilities and QA validation results.
12 chapters in this module
- Structure of the SoA document
- Justifying inclusions and exclusions
- Linking controls to QA evidence
- Using test logs in SoA justification
- Versioning the SoA
- Peer review process for SoA
- Common auditor pushbacks
- How to defend technical decisions
- SoA as living documentation
- Integrating SoA updates into sprints
- Ownership model for ongoing updates
- Case example: SoA for image classifier
Module 6. Internal Audit Preparation
Lead internal audit cycles end to end using artefacts generated directly from QA workflows.
12 chapters in this module
- Audit planning with QA timelines
- Preparing evidence packages
- Responding to auditor questions
- Using automation logs as proof
- Coordinating with peer teams
- Handling gaps in control coverage
- Documenting compensating controls
- Responding to audit findings
- Conducting mock audits
- Building audit resilience into QA
- Audit follow-up tracking
- Worked example: Audit package for NLP tool
Module 7. Regulator-Facing Documentation
Generate documentation that withstands regulatory scrutiny by grounding every claim in QA validation.
12 chapters in this module
- Regulator expectations for AI
- Translating QA findings for regulators
- Avoiding overstatement in narratives
- Using test data as proof
- Version control for regulator docs
- Handling requests for source code
- Documenting model validation processes
- Proving fairness claims technically
- Responding to follow-up questions
- Maintaining independence in reporting
- Common regulatory pushbacks
- Case example: Regulatory submission for chatbot
Module 8. Cross-Functional Escalation Management
Own escalations from peer teams on AI governance by providing authoritative, technically sound direction.
12 chapters in this module
- Why escalations come to QA engineers
- Assessing peer team requests
- Providing actionable guidance
- Documenting escalation decisions
- Building trust with non-technical teams
- Handling pressure to cut corners
- Escalation tracking system
- Proving control effectiveness
- Communicating with legal and compliance
- Owning the vendor review track
- Setting precedent through artefacts
- Worked example: Escalation from data team
Module 9. Continuous Monitoring and Improvement
Implement ongoing control validation to keep ISO 42001 compliance current between audits.
12 chapters in this module
- Automated control monitoring
- Detecting model drift
- Logging control performance
- Alerting on control failures
- Scheduled revalidation cycles
- Updating controls with model changes
- Versioning control logic
- QA-driven continuous improvement
- Feedback loops with development
- Documenting changes over time
- Audit trail for control updates
- Worked example: Drift detection pipeline
Module 10. Vendor Oversight and Third-Party AI
Apply ISO 42001 controls to third-party AI components using QA-led validation.
12 chapters in this module
- Scope of vendor oversight
- Reviewing vendor documentation
- Designing acceptance tests
- Validating vendor claims
- Handling black-box models
- Monitoring vendor performance
- Contractual obligations and QA
- Tracking vendor risks
- Escalating vendor issues
- Own the vendor-review track end to end
- Documenting due diligence
- Case example: Third-party NLP API
Module 11. Change Management in AI Governance
Lead governance through system changes using versioned artefacts and QA validation.
12 chapters in this module
- Governance in agile environments
- Versioning control mappings
- QA sign-off on changes
- Change approval workflows
- Documenting rationale for changes
- Handling emergency changes
- Audit trail for change decisions
- Peer review of change impacts
- Updating risk assessments
- Communicating changes to stakeholders
- Change logs for auditors
- Worked example: Model retraining workflow
Module 12. Sustaining Compliance Through Leadership Transitions
Build documentation and playbooks that survive team changes and leadership shifts.
12 chapters in this module
- Knowledge transfer best practices
- Documenting tacit knowledge
- Building onboarding materials
- Standardizing control design
- Creating reusable templates
- Documenting decision rationale
- Maintaining consistency over time
- Handing over ownership
- Auditing for continuity
- Updating playbooks with lessons
- Ensuring artefacts survive turnover
- Case example: Team reshuffle transition
How this maps to your situation
- Preparing for first internal AI audit
- Responding to peer team escalations on AI compliance
- Supporting M&A due diligence with governance artefacts
- Leading ISO 42001 implementation without external consultants
Before vs. after
Before
Relies on guidance from compliance teams, responds to auditor requests reactively, produces documentation that requires senior review
After
Leads ISO 42001 cycles independently, produces regulator-ready artefacts, becomes the escalation point for peer teams
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, with self-paced delivery and immediate access to all materials upon enrollment.
If nothing changes
Without structured command of ISO 42001, QA engineers risk being sidelined in AI governance decisions, even as their work underpins compliance. The most visible roles go to those who own the artefacts, not just execute tests.
How this compares to the alternatives
Unlike generic ISO 42001 courses aimed at auditors or compliance managers, this course is built for QA engineers who need to prove control effectiveness through automation. It focuses on artefacts, versioning, test logs, and peer escalation , not policy slides or board narratives.
Frequently asked
Is this course suitable for someone without a compliance background?
Yes. It’s designed for technical practitioners like QA engineers who validate controls through automation. No prior compliance certification is required.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead audits independently?
Yes. You’ll learn to produce working statements of applicability, lead internal audit prep, and respond to auditor questions using QA-generated evidence.
$199 one-time. Approximately 3 hours per module, with self-paced delivery and immediate access to all materials upon enrollment..
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