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DAT5928 Mastering ISO 42001 for Engineering Services Leaders in Regulated Environments

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Engineering Services Leaders in Regulated Environments

A structured path from AI policy to auditable implementation for senior technical managers

$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.
Spending cycles revising AI governance documentation because it didn’t survive leadership scrutiny or client audit rounds

The situation this course is for

Engineered controls are getting caught in rework loops because they weren’t built against formalized standards from day one. The result: duplicated effort, delayed sign-offs, and erosion of trust in technical leadership outputs.

Who this is for

Senior technical manager in a global services firm responsible for delivering compliant AI-enabled solutions in regulated sectors

Who this is not for

Individual contributors building isolated proofs of concept; vendors selling AI tools without governance scope

What you walk away with

  • Produce AI governance documentation that passes internal and client review cycles on first submission
  • Structure compliance artefacts so they’re reusable across client engagements and audit cycles
  • Anticipate reviewer expectations using ISO 42001 control mappings aligned to real-world engineering workflows
  • Turn routine deliverables into trusted inputs for leadership decision-making
  • Reduce rework by aligning team output with formal standards before review cycles begin

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001 and Its Role in AI Governance
Establish foundational knowledge of ISO 42001, its structure, and how it integrates with existing engineering service frameworks. Learn why this standard is becoming a baseline for client-facing AI assurance and how it differs from general compliance mandates.
12 chapters in this module
  1. Defining AI governance in the context of international standards
  2. Core components of the ISO 42001 framework
  3. How ISO 42001 differs from ISO 27001 and SOC 2
  4. Mapping ISO 42001 clauses to engineering service deliverables
  5. Why clients now require ISO 42001-aligned documentation
  6. Integrating ISO 42001 with CGI’s service delivery models
  7. The role of engineering leadership in governance adoption
  8. Key overlaps with NIST AI RMF and EU AI Act
  9. Building cross-functional alignment on governance scope
  10. Establishing ownership for AI management system documentation
  11. Common misconceptions about ISO 42001 implementation
  12. Preparing for initial gap assessment with internal audit
Module 2. Scoping the AI Management System for Client Engagements
Learn how to define and document the scope of an AI Management System (AIMS) tailored to specific client projects, ensuring alignment with contractual obligations and regulatory expectations.
12 chapters in this module
  1. Identifying AI systems in current service portfolios
  2. Determining applicability of ISO 42001 per engagement
  3. Documenting boundaries and applicability justifications
  4. Engaging legal and compliance teams early in scoping
  5. Aligning AIMS scope with client SLAs and deliverables
  6. Handling multi-jurisdictional AI compliance requirements
  7. Using risk categorization to inform scoping depth
  8. Avoiding over-scoping that delays project timelines
  9. Integrating scoping outputs with proposal documentation
  10. Maintaining living scope documentation through project life
  11. When to escalate scope conflicts to senior leadership
  12. Template: AIMS Scope Statement for Client-Facing Teams
Module 3. Leadership and Organizational Accountability Frameworks
Define clear roles, responsibilities, and decision rights for AI governance across engineering teams, ensuring leadership commitment is documented and actionable.
12 chapters in this module
  1. Assigning accountability for AI management systems
  2. Designating AI governance champions within delivery teams
  3. Documenting leadership commitment to compliance
  4. Establishing escalation paths for unresolved issues
  5. Integrating governance roles with existing service org structure
  6. Aligning team incentives with compliance performance
  7. Creating transparency between technical and executive teams
  8. Managing cross-team dependencies in AI deployments
  9. Building governance into role descriptions and KPIs
  10. Handling turnover in critical governance roles
  11. Measuring leadership engagement in governance outcomes
  12. Template: RACI Matrix for AI Governance in Services
Module 4. Risk Assessment and Treatment Planning for AI Systems
Develop structured methods to identify, assess, and treat AI-related risks across the service lifecycle, aligned with ISO 42001 requirements and client expectations.
12 chapters in this module
  1. Classifying AI system risk levels based on impact
  2. Using ISO 42001 Annex A for risk identification
  3. Integrating risk assessments into technical design reviews
  4. Engaging data scientists and engineers in risk workshops
  5. Documenting risk treatment plans with clear ownership
  6. Linking risk decisions to control implementation
  7. Maintaining risk registers across multi-client portfolios
  8. Updating assessments as models evolve in production
  9. Using heat maps to communicate risk to leadership
  10. Integrating third-party model risks into assessments
  11. Handling high-risk AI use cases under EU AI Act
  12. Template: AI Risk Assessment Workbench for Services
Module 5. Data Management and Quality Assurance Processes
Implement data governance practices that ensure AI systems are trained and validated on high-quality, compliant data sources.
12 chapters in this module
  1. Defining data requirements for AI model development
  2. Ensuring data provenance and traceability in pipelines
  3. Validating data quality for high-stakes AI applications
  4. Managing bias and fairness assessments in data sets
  5. Documenting data lineage for audit readiness
  6. Integrating data governance with existing ETL workflows
  7. Handling synthetic and augmented data in training
  8. Establishing data retention rules for AI models
  9. Securing sensitive data used in AI development
  10. Auditing data access and usage patterns
  11. Using metadata to support compliance reporting
  12. Template: Data Governance Checklist for AI Projects
Module 6. Model Development Lifecycle Controls
Apply ISO 42001 principles to each phase of the AI model lifecycle, from design to deployment, ensuring consistency and compliance.
12 chapters in this module
  1. Integrating governance into model initiation phases
  2. Documenting model intent and use case boundaries
  3. Reviewing architecture choices for compliance alignment
  4. Validating assumptions during model development
  5. Tracking model versions and dependencies
  6. Conducting pre-deployment compliance checks
  7. Building explainability into model design
  8. Testing for robustness and edge-case behavior
  9. Establishing model documentation standards
  10. Ensuring model cards reflect real-world performance
  11. Handling model updates and retraining cycles
  12. Template: Model Lifecycle Compliance Gate Checklist
Module 7. Human-AI Interaction and Operational Oversight
Design human oversight mechanisms that ensure safe and effective operation of AI systems in production environments.
12 chapters in this module
  1. Defining appropriate levels of human intervention
  2. Mapping AI decision points to human review stages
  3. Designing user interfaces that support oversight
  4. Training staff to monitor AI system behavior
  5. Establishing escalation triggers for AI anomalies
  6. Auditing human-in-the-loop interactions
  7. Ensuring fallback procedures are documented and tested
  8. Evaluating user feedback in AI performance loops
  9. Measuring effectiveness of human oversight
  10. Updating oversight protocols as models evolve
  11. Integrating oversight into incident response plans
  12. Template: Human-AI Interaction Log and Review Form
Module 8. Monitoring, Evaluation, and Continuous Improvement
Set up ongoing monitoring systems to evaluate AI performance, compliance, and fairness, enabling continuous improvement.
12 chapters in this module
  1. Designing KPIs for AI system performance and compliance
  2. Establishing automated monitoring for model drift
  3. Scheduling regular compliance self-assessments
  4. Auditing model outputs for unintended consequences
  5. Using feedback loops to inform model updates
  6. Measuring fairness and bias over time
  7. Reporting monitoring results to governance bodies
  8. Integrating lessons learned into future projects
  9. Benchmarking performance across service lines
  10. Aligning monitoring cadence with regulatory cycles
  11. Responding to audit findings with corrective actions
  12. Template: AI System Monitoring Dashboard Specification
Module 9. Recordkeeping and Audit Preparation Strategies
Build comprehensive records that demonstrate compliance with ISO 42001 and survive external audit scrutiny.
12 chapters in this module
  1. Identifying required records under ISO 42001
  2. Organizing documentation for audit accessibility
  3. Maintaining version control for governance artefacts
  4. Using metadata to streamline record retrieval
  5. Preparing for internal and client-led audits
  6. Conducting mock audits to test record readiness
  7. Training teams to respond to auditor inquiries
  8. Handling document requests under tight deadlines
  9. Protecting confidentiality during audit exchanges
  10. Leveraging automation for record generation
  11. Reusing records across engagements efficiently
  12. Template: Audit Readiness Package for AI Services
Module 10. Internal Audit and Corrective Action Processes
Conduct effective internal audits of AI systems and implement corrective actions that address root causes.
12 chapters in this module
  1. Planning internal audits of AI management systems
  2. Selecting qualified auditors within the organization
  3. Developing checklists based on ISO 42001 clauses
  4. Conducting on-site and remote audit activities
  5. Documenting audit findings clearly and objectively
  6. Prioritizing non-conformities for resolution
  7. Assigning corrective action owners and timelines
  8. Verifying effectiveness of implemented fixes
  9. Reporting audit outcomes to leadership
  10. Using audit data to improve future projects
  11. Avoiding repetitive findings across engagements
  12. Template: Internal Audit Report for AI Systems
Module 11. Certification Readiness and Third-Party Audit Support
Prepare for external certification audits by ensuring full compliance with ISO 42001 and effective coordination with auditors.
12 chapters in this module
  1. Selecting a certification body for ISO 42001
  2. Understanding the two-stage audit process
  3. Preparing documentation for Stage 1 review
  4. Conducting readiness assessments before audit
  5. Coordinating with client stakeholders during audits
  6. Responding to auditor questions efficiently
  7. Addressing non-conformities within deadlines
  8. Maintaining certification through surveillance
  9. Leveraging certification in client proposals
  10. Managing multi-site certification efforts
  11. Budgeting for certification maintenance
  12. Template: Certification Readiness Tracker
Module 12. Scaling AI Governance Across Service Portfolios
Extend ISO 42001 implementation across multiple teams and geographies, creating reusable frameworks and shared best practices.
12 chapters in this module
  1. Developing standardized templates for AI governance
  2. Creating centralized repositories for artefacts
  3. Training new teams on ISO 42001 implementation
  4. Establishing governance champions in each unit
  5. Harmonizing practices across global delivery centers
  6. Integrating governance into onboarding for new hires
  7. Measuring maturity across service lines
  8. Benchmarking performance against industry peers
  9. Updating frameworks as standards evolve
  10. Sharing lessons from client engagements
  11. Building executive confidence in governance scalability
  12. Template: Governance Scaling Roadmap for Services

How this maps to your situation

  • Client-facing engineering leadership
  • Regulated AI deployment in public sector and financial services
  • Multi-jurisdictional compliance coordination
  • Governance maturity scaling across delivery teams

Before vs. after

Before
AI governance efforts are reactive, fragmented, and subject to repeated review cycles.
After
Your team produces structured, standards-aligned artefacts that lead client engagements and pass scrutiny on first submission.

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 90 minutes of focused reading and reflection, designed to fit within a single Sunday morning.

If nothing changes
Without structured governance, AI projects risk delays, client escalations, and reputational exposure due to non-compliance with emerging standards.

How this compares to the alternatives

Unlike generic compliance webinars or slide decks, this course delivers a complete, implementable framework tailored to engineering services leaders managing real-world AI deployments under ISO 42001.

Frequently asked

Is this course specific to my industry?
Yes. It focuses on engineering services in regulated environments, with examples from public sector and financial clients.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get practical templates?
Yes. Every module includes downloadable templates and worked examples you can adapt immediately.
$199 one-time. Approximately 90 minutes of focused reading and reflection, designed to fit within a single Sunday morning..

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
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