A tailored course, built for your situation
Mastering ISO 42001 for Testing Engineering Specialist Advisors
Build authoritative AI governance artefacts that align with global standards and elevate your technical influence.
$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 isn't theoretical, it's landing in your backlog as evidence requests, control validations, and audit timelines.
The situation this course is for
Teams are scrambling to produce compliant outputs without deep understanding of ISO 42001’s technical clauses. Too many practitioners are reacting, not leading.
Who this is for
Senior technical specialist advising on testing frameworks within regulated, large-scale service environments.
Who this is not for
Junior auditors, entry-level compliance staff, or practitioners focused solely on non-AI quality assurance workflows.
What you walk away with
- Produce a complete Statement of Applicability (SoA) aligned to ISO/IEC 42001 controls
- Structure vendor assessment workflows using clause 8.4 requirements
- Lead internal audit preparation with ready-to-submit documentation packs
- Map AI governance controls directly to existing testing engineering pipelines
- Anticipate executive questions on AI risk posture using standardized reporting templates
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO/IEC 42001 and the AI Governance Landscape
Establish foundational knowledge of ISO/IEC 42001’s structure, intent, and relationship to emerging AI regulations across global markets.
12 chapters in this module
- Understanding the scope and purpose of ISO/IEC 42001
- How ISO 42001 differs from general AI ethics frameworks
- Core terminology used in AI management systems
- Linking ISO 42001 to existing quality assurance practices
- The role of the testing specialist in governance rollout
- Case study: First mover adoption in Japan-based IT services
- Organizational context analysis for AI systems
- Identifying interested parties and their expectations
- Defining boundaries for AI management systems
- Documenting compliance objectives for audit readiness
- Preparing for leadership team engagement on AI risks
- Baseline assessment checklist for current controls
Module 2. Leadership Commitment and Governance Structure Design
Learn how to draft leadership responsibilities, define governance roles, and build accountability frameworks that align with ISO 42001 clause 5.
12 chapters in this module
- Defining top management responsibilities under clause 5.1
- Creating the AI governance steering committee charter
- Assigning roles: AI owner, data steward, testing lead
- Developing policies for ethical AI use and oversight
- Integrating AI governance into existing QA leadership forums
- Establishing communication protocols across engineering teams
- Documenting strategic direction for AI initiatives
- Aligning AI goals with organizational values and values
- Setting performance metrics for governance effectiveness
- Reporting progress to senior technical advisors
- Conducting governance health checks quarterly
- Updating governance structure based on audit outcomes
Module 3. Planning the AI Risk Management Process
Build a risk treatment plan aligned to ISO 42001 clause 6, integrating with existing risk registers and testing engineering workflows.
12 chapters in this module
- Assessing risks and opportunities in AI system deployment
- Defining risk criteria and tolerance thresholds
- Mapping AI-specific risks to business impact levels
- Integrating AI risk assessments into existing QA cycles
- Creating risk treatment plans using clause 6.1.3
- Selecting controls from Annex A based on risk profile
- Prioritizing high-impact, high-likelihood risk scenarios
- Documenting risk acceptance decisions with justification
- Establishing ongoing risk monitoring mechanisms
- Linking risk findings to test case development
- Updating risk landscape with model retraining cycles
- Reporting risk status to technical advisory boards
Module 4. Support Processes and Resource Allocation
Ensure adequate resources, competence, awareness, and documentation are in place to sustain AI governance efforts.
12 chapters in this module
- Identifying necessary resources for AI governance support
- Assessing current team skills against ISO 42001 needs
- Developing training plans for engineering teams
- Creating internal awareness campaigns on AI standards
- Establishing communication methods for policy updates
- Maintaining documented information per clause 7.5
- Version control for AI governance artefacts
- Secure storage and access controls for documentation
- Defining retention periods for audit evidence
- Using templates for consistent artefact creation
- Building internal knowledge repositories
- Measuring effectiveness of support processes
Module 5. Operational Controls for AI System Lifecycle
Implement controls across AI system design, development, deployment, and monitoring phases in alignment with ISO 42001 clause 8.
12 chapters in this module
- Applying operational planning to AI projects
- Defining criteria for AI system approval
- Integrating testing checkpoints into development sprints
- Validating data quality and bias mitigation steps
- Monitoring AI outputs during production use
- Establishing feedback loops for model performance
- Handling incidents and anomalies in AI behavior
- Conducting periodic reviews of AI system effectiveness
- Managing changes to AI models and infrastructure
- Documenting decisions in the change management log
- Aligning incident response with corporate protocols
- Producing operational reports for technical review boards
Module 6. Vendor and Third-Party Risk Integration
Apply ISO 42001 clause 8.4 to manage risks from external suppliers of AI components, tools, and services.
12 chapters in this module
- Identifying AI-relevant third-party relationships
- Assessing vendor compliance with ISO 42001 requirements
- Drafting procurement language for AI governance adherence
- Conducting due diligence on AI model providers
- Evaluating transparency of vendor documentation
- Reviewing model cards and system cards for completeness
- Establishing service-level agreements for AI performance
- Monitoring vendor compliance throughout contract term
- Managing sub-processors and downstream dependencies
- Auditing third-party AI systems remotely
- Handling contract termination for non-compliance
- Updating internal risk register based on vendor findings
Module 7. Performance Evaluation and Internal Audit Preparation
Prepare for internal audits by establishing KPIs, monitoring performance, and ensuring continual improvement.
12 chapters in this module
- Defining key performance indicators for AI governance
- Measuring conformance to internal policies
- Tracking audit readiness across business units
- Scheduling internal audit cycles per ISO 42001
- Selecting qualified internal auditors for AI systems
- Developing audit checklists based on clause 9.2
- Conducting opening and closing audit meetings
- Documenting non-conformities and observations
- Prioritizing findings for remediation tracking
- Verifying effectiveness of corrective actions
- Reporting audit results to technical leadership
- Using audit outcomes to improve testing strategies
Module 8. Statement of Applicability (SoA) Development
Build a defensible, audit-ready Statement of Applicability by selecting and justifying controls from Annex A.
12 chapters in this module
- Understanding the purpose of the SoA in ISO 42001
- Listing all applicable controls from Annex A
- Justifying inclusion of each selected control
- Documenting rationale for excluding any controls
- Obtaining approval signatures from technical leads
- Versioning the SoA for audit trails
- Linking SoA controls to existing testing procedures
- Mapping controls to responsibility assignment matrices
- Integrating SoA updates into change management
- Preparing SoA for external certification bodies
- Aligning SoA with client-specific requirements
- Maintaining SoA as a living document
Module 9. Compliance Demonstration and Certification Readiness
Prepare for external audits by organizing documentation, running mock assessments, and refining compliance narratives.
12 chapters in this module
- Understanding ISO 42001 certification process
- Selecting an accredited certification body
- Conducting pre-certification gap assessments
- Running internal mock audits with peer reviewers
- Compiling evidence dossiers for auditors
- Training staff on audit response protocols
- Developing executive summaries for compliance status
- Responding to auditor questions effectively
- Addressing minor and major non-conformities
- Implementing corrective actions before closure
- Celebrating certification achievement internally
- Maintaining compliance between audit cycles
Module 10. AI Incident Response and Corrective Action Management
Establish protocols for detecting, reporting, and resolving AI system failures or ethical breaches.
12 chapters in this module
- Defining AI incident types and severity levels
- Creating reporting pathways for anomalous behavior
- Activating incident response teams for AI events
- Documenting root causes using structured analysis
- Implementing containment measures for AI outages
- Notifying stakeholders of AI incidents appropriately
- Reviewing incidents in technical advisory forums
- Updating controls to prevent recurrence
- Tracking corrective action completion status
- Integrating lessons learned into training programs
- Auditing incident response effectiveness
- Reporting trends to senior engineering leadership
Module 11. Continual Improvement and Change Adaptation
Embed feedback loops that ensure AI governance evolves with technology, regulation, and organizational needs.
12 chapters in this module
- Collecting feedback from testing cycles
- Analyzing audit findings for systemic improvement
- Updating AI policies based on lessons learned
- Incorporating new regulatory expectations
- Revising risk assessments after major incidents
- Enhancing training materials with real examples
- Optimizing documentation workflows
- Reducing time to produce compliance artefacts
- Benchmarking against peer organizations
- Driving innovation in AI assurance practices
- Measuring maturity progression over time
- Presenting improvement metrics to advisory boards
Module 12. Integration with Broader Compliance Frameworks
Align ISO 42001 efforts with SOC 2, GDPR, and other standards to avoid duplication and increase efficiency.
12 chapters in this module
- Mapping ISO 42001 controls to SOC 2 Trust Criteria
- Aligning AI governance with GDPR data protection principles
- Integrating with ISO 27001 ISMS frameworks
- Harmonizing audits across multiple standards
- Reducing workload through shared evidence
- Creating unified compliance dashboards
- Coordinating cross-functional audit schedules
- Training teams on multi-standard requirements
- Developing common terminology across domains
- Streamlining reporting to executive leadership
- Demonstrating efficiency gains from integration
- Positioning AI governance as a strategic enabler
How this maps to your situation
- New AI governance mandates affecting global IT services
- Increasing executive scrutiny on AI risk management
- Growing need to document compliance decisions
- Integration challenges between legacy QA and AI systems
Before vs. after
Before
Receiving requests for AI governance inputs without clear frameworks or templates.
After
Producing standards-aligned artefacts confidently, with reusable documentation structures.
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 12 weeks, or intensive 12-hour weekend path.
If nothing changes
Without structured guidance, teams may produce inconsistent or audit-deficient outputs, delaying client readiness and reducing technical influence.
How this compares to the alternatives
Generic AI ethics courses lack ISO 42001 specificity. Public webinars don’t provide templates. This course delivers precise, actionable steps for specialists.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior ISO experience required?
No, this course starts from foundational concepts and builds to advanced implementation.
Will I get access to templates?
Yes, each module includes downloadable, editable templates and real-world examples.
$199 one-time. 90 minutes per week over 12 weeks, or intensive 12-hour weekend path..
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