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DAT8153 Mastering ISO 42001 for Senior GenAI Technology Leaders

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Senior GenAI Technology Leaders

Build governance-ready AI systems with documented assurance that scales across teams and audits

$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 work gets done, but it's invisible to leadership, buried in tickets and technical notes, never credited in strategy reviews.

The situation this course is for

Even as AI systems go mainstream, the teams building them rarely get recognized for the rigor behind the scenes. Controls are implemented, audits pass, but the work stays operational, never elevated to strategic. That invisibility stalls career momentum and undercuts influence, especially when leadership seeks accountability for AI outcomes.

Who this is for

Senior GenAI technology leaders in large-scale AI product organizations, responsible for model delivery, system integrity, and cross-functional alignment with risk and compliance teams.

Who this is not for

Junior engineers looking for technical deep dives; executives seeking high-level overviews; professionals outside AI systems delivery or governance.

What you walk away with

  • Structured ISO 42001-aligned documentation that makes your team's AI governance efforts visible to leadership
  • Clear mapping of technical controls to executive accountability frameworks
  • Templates for generating compliance evidence without rework during audit cycles
  • Ability to proactively position AI initiatives as governance-strong during leadership reviews
  • Recognition as the internal reference for AI system assurance across engineering and risk functions

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 42001 and AI Governance
Build foundational knowledge of ISO 42001's structure, intent, and alignment with GenAI system development. Understand how it differentiates from other standards and why it matters for engineering leadership.
12 chapters in this module
  1. Overview of AI governance landscape and market demand
  2. Key principles of ISO 42001 and how they apply to GenAI
  3. Relationship between AI management systems and organizational risk
  4. Scope and boundaries of ISO 42001 implementation
  5. How ISO 42001 complements existing security and privacy frameworks
  6. Executive expectations from AI governance frameworks
  7. Stakeholder mapping for AI assurance initiatives
  8. Role of the technical leader in governance adoption
  9. Common misconceptions about ISO 42001 in AI teams
  10. Preparing your team for framework integration
  11. Early signals of governance maturity in AI delivery
  12. Aligning ISO 42001 with product development lifecycle
Module 2. Leadership and Organizational Context
Define leadership responsibility under ISO 42001 and align AI governance with business objectives. Establish clear ownership and accountability across technical and risk teams.
12 chapters in this module
  1. Defining leadership commitment to AI governance
  2. Assigning roles and responsibilities for AI management
  3. Integrating AI objectives with business strategy
  4. Establishing governance as a leadership-driven initiative
  5. Communicating AI assurance vision across levels
  6. Linking AI risk tolerance to organizational culture
  7. Setting measurable objectives for AI systems
  8. Documenting leadership intent for audit readiness
  9. Balancing innovation speed with governance rigor
  10. Creating feedback channels between engineering and executives
  11. Ensuring board-relevant reporting from technical outcomes
  12. Onboarding new leaders into the AI governance framework
Module 3. AI Risk Assessment and Treatment
Systematically identify, analyze, and treat AI-specific risks using ISO 42001 guidelines. Translate technical risk patterns into documented treatment plans.
12 chapters in this module
  1. Identifying AI-specific hazards in model development
  2. Classifying risks by impact and likelihood
  3. Involving cross-functional teams in risk workshops
  4. Documenting risk assessment methodology
  5. Establishing risk acceptance criteria
  6. Mapping technical controls to risk treatments
  7. Using model cards and data sheets in risk documentation
  8. Tracking risk treatment effectiveness over time
  9. Updating risk registers during model iteration
  10. Aligning with NIST AI RMF where applicable
  11. Risk communication to non-technical stakeholders
  12. Audit trails for risk decision-making
Module 4. Data and Model Management
Implement governance for training data, model development, and versioning processes in line with ISO 42001 requirements.
12 chapters in this module
  1. Defining data quality and provenance standards
  2. Documenting data sourcing and preprocessing steps
  3. Version control for datasets and models
  4. Metadata tracking for model lineage
  5. Ensuring reproducibility in model training
  6. Managing synthetic data usage and disclosure
  7. Bias detection and mitigation documentation
  8. Data retention and deletion policies
  9. Model performance monitoring baselines
  10. Handling model updates and retraining
  11. Integrating human oversight mechanisms
  12. Audit-ready model documentation templates
Module 5. Transparency and Explainability
Operationalize transparency requirements for AI systems. Develop user-facing and internal explainability practices that meet ISO 42001 criteria.
12 chapters in this module
  1. Defining transparency obligations for user interfaces
  2. Creating user guidance for AI capabilities
  3. Documenting system limitations and usage boundaries
  4. Developing model explainability reports
  5. Balancing IP protection with disclosure needs
  6. Adapting explanations for different stakeholder groups
  7. Logging user interactions with generative systems
  8. Handling edge cases and hallucinations transparently
  9. Disclosure of synthetic content generation
  10. Interface design for informed user consent
  11. Maintaining public documentation for trust
  12. Updating disclaimers with model changes
Module 6. Human Oversight and Monitoring
Design effective human oversight mechanisms for AI systems. Implement monitoring that ensures ongoing compliance and performance.
12 chapters in this module
  1. Defining roles for human-in-the-loop decisions
  2. Setting thresholds for human intervention
  3. Monitoring for model drift and degradation
  4. Logging oversight actions and rationale
  5. Establishing response protocols for AI failures
  6. Training staff on AI monitoring responsibilities
  7. Auditing human review effectiveness
  8. Integrating feedback into model improvement
  9. Scaling oversight across global deployments
  10. Reporting oversight metrics to leadership
  11. Balancing automation with accountability
  12. Designing escalation paths for critical incidents
Module 7. Performance Evaluation and Testing
Establish robust evaluation methods for AI systems. Align testing practices with ISO 42001 requirements for reliability and safety.
12 chapters in this module
  1. Defining performance metrics for generative models
  2. Developing test datasets and scenarios
  3. Measuring output quality and consistency
  4. Evaluating fairness and bias in production
  5. Conducting adversarial testing for robustness
  6. Benchmarking against peer models
  7. Documenting test methodology and results
  8. Versioning test suites with model updates
  9. Integrating testing into CI/CD pipelines
  10. Third-party validation readiness
  11. Reporting evaluation outcomes to non-technical leaders
  12. Maintaining test evidence for auditor access
Module 8. Documentation and Evidence Management
Create and maintain ISO 42001-compliant documentation. Ensure artifacts are audit-ready and accessible to stakeholders.
12 chapters in this module
  1. Identifying required governance documentation
  2. Structuring the AI management system manual
  3. Maintaining control implementation records
  4. Documenting risk treatment outcomes
  5. Storing model validation reports
  6. Versioning policies and procedures
  7. Classifying document sensitivity and access
  8. Ensuring availability during audits
  9. Automation of evidence collection
  10. Retention schedules for AI artifacts
  11. Cross-referencing controls to framework clauses
  12. Preparing documentation for external review
Module 9. Compliance and Regulatory Alignment
Align ISO 42001 implementation with other regulatory requirements and standards. Navigate overlapping expectations efficiently.
12 chapters in this module
  1. Mapping ISO 42001 to GDPR and privacy laws
  2. Integrating with SOC 2 control frameworks
  3. Aligning with NIST AI RMF components
  4. Meeting sector-specific regulations for AI
  5. Preparing for EU AI Act readiness
  6. Cross-walking controls to multiple standards
  7. Avoiding duplication in compliance efforts
  8. Engaging with legal and compliance teams
  9. Updating policies as regulations evolve
  10. Reporting compliance status to executives
  11. Handling jurisdiction-specific requirements
  12. Maintaining alignment across global operations
Module 10. Internal Audit and Continuous Improvement
Conduct effective internal audits of the AI management system. Drive continuous improvement based on findings.
12 chapters in this module
  1. Planning the internal audit schedule
  2. Selecting qualified internal auditors
  3. Developing audit checklists for AI systems
  4. Conducting process walkthroughs
  5. Reviewing evidence for control effectiveness
  6. Reporting audit findings to leadership
  7. Tracking corrective actions to closure
  8. Using audit results to refine governance
  9. Benchmarking against industry practices
  10. Preparing for external certification audits
  11. Scaling audit practices across teams
  12. Institutionalizing audit learning
Module 11. Third-Party and Supply Chain Management
Extend ISO 42001 governance to third-party vendors and supply chain partners involved in AI development.
12 chapters in this module
  1. Assessing vendor AI governance maturity
  2. Defining contractual obligations for AI assurance
  3. Reviewing third-party model documentation
  4. Auditing external AI providers
  5. Managing open-source model risks
  6. Ensuring transparency in vendor relationships
  7. Handling data flow with external parties
  8. Monitoring vendor performance and compliance
  9. Establishing escalation paths for issues
  10. Maintaining oversight of outsourced development
  11. Integrating vendor audits into internal program
  12. Documenting third-party risk treatment
Module 12. Certification and Stakeholder Confidence
Prepare for ISO 42001 certification and build stakeholder trust through verified AI governance practices.
12 chapters in this module
  1. Choosing a certification body for ISO 42001
  2. Preparing documentation for external audit
  3. Conducting pre-certification readiness review
  4. Addressing auditor findings
  5. Maintaining certification over time
  6. Communicating certification to stakeholders
  7. Leveraging certification in market positioning
  8. Integrating feedback from certification process
  9. Aligning leadership messaging with certification
  10. Scaling certified practices across products
  11. Renewal planning and timeline management
  12. Demonstrating continuous compliance

How this maps to your situation

  • After first AI system audit
  • During governance framework selection
  • Before external compliance review
  • When expanding AI team responsibilities

Before vs. after

Before
AI governance work is reactive, buried in technical detail, and invisible to leadership despite high effort.
After
Your team's AI systems are built with documented assurance, recognized as governance-strong, and showcased in strategic reviews.

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 6 weeks, or self-paced access for up to 90 days.

If nothing changes
Without structured governance, your AI initiatives remain vulnerable to scrutiny, delays, and leadership skepticism , even when technically sound.

How this compares to the alternatives

Unlike generic AI ethics guides or high-level compliance overviews, this course delivers executable, clause-by-clause implementation guidance specific to ISO 42001 and GenAI systems , with templates you can deploy immediately.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for someone already using other AI frameworks?
Yes , the course shows how to align ISO 42001 with NIST, OECD, or internal standards without duplicating effort.
$199 one-time. 90 minutes per week over 6 weeks, or self-paced access for up to 90 days..

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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