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DAT6188 Mastering ISO 42001 for Senior Product Owners in Enterprise Technology

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Senior Product Owners in Enterprise Technology

Build AI governance expertise that earns peer authority and shapes cross-functional roadmap decisions

$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 initiatives stall when governance is an afterthought

The situation this course is for

Teams waste cycles reworking AI features because governance wasn't aligned early. Product owners lose influence when they can't speak the language of controls and audits. The result: delayed launches, strained cross-team relationships, and missed opportunities to lead.

Who this is for

Senior Product Owner in enterprise tech, leading AI or data-intensive features and needing to align engineering, compliance, and leadership stakeholders

Who this is not for

Entry-level product managers, non-technical stakeholders, or teams not actively shipping AI-enabled features

What you walk away with

  • Produce AI governance documentation that aligns engineering and compliance teams on first review
  • Lead roadmap conversations with confidence using ISO 42001 control language
  • Anticipate audit and risk review requirements before they become blockers
  • Position yourself as the internal reference for AI governance decisions across product and engineering
  • Ship AI-powered features faster by embedding governance into product sprints

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 differentiates from other AI ethics and governance frameworks. Learn why product owners are now expected to operationalize its principles.
12 chapters in this module
  1. Defining AI governance in the context of international standards
  2. Overview of ISO 42001 scope and structure
  3. How ISO 42001 complements existing internal governance models
  4. Differences between AI ethics, bias mitigation, and formalized control frameworks
  5. Mapping ISO 42001 to product lifecycle stages
  6. Understanding the role of product leadership in governance adoption
  7. Key stakeholders influenced by ISO 42001 implementation
  8. How ISO 42001 reduces friction in cross-functional approvals
  9. Common misconceptions about AI governance standards
  10. Preparing for internal questions about certification readiness
  11. How ISO 42001 supports scalable AI deployment
  12. Relating ISO 42001 to broader digital trust initiatives
Module 2. Identifying AI Systems in Your Product Portfolio
Learn to classify which features and systems in your product suite qualify as AI under ISO 42001 and require formal governance documentation.
12 chapters in this module
  1. Defining AI systems based on functionality and decision impact
  2. Cataloging AI-powered features in current product roadmaps
  3. Assessing autonomy and influence levels of AI components
  4. Determining system boundaries for governance inclusion
  5. Working with engineering to identify training data sources
  6. Classifying systems by risk level under ISO 42001 guidelines
  7. Documenting AI system purpose and intended use cases
  8. Identifying third-party AI dependencies
  9. Mapping AI components to user interaction points
  10. Creating an inventory of AI systems for internal audit
  11. Prioritizing systems for governance rollout
  12. Integrating classification into sprint planning
Module 3. Establishing Governance Roles and Responsibilities
Clarify ownership across product, engineering, legal, and compliance teams to prevent governance gaps and decision delays.
12 chapters in this module
  1. Defining the product owner's role in AI governance
  2. Mapping RACI for AI system approvals
  3. Engaging legal and compliance early in feature design
  4. Setting expectations with engineering leads
  5. Integrating governance roles into product team structure
  6. Documenting escalation paths for control conflicts
  7. Establishing cross-functional governance committees
  8. Assigning oversight for ongoing monitoring
  9. Clarifying responsibilities for model updates
  10. Handling ownership across shared platforms
  11. Managing vendor-supplied AI components
  12. Communicating governance roles to stakeholders
Module 4. Documenting AI System Context and Boundaries
Structure clear artefacts that define what an AI system does, where it operates, and how it interacts with other systems.
12 chapters in this module
  1. Writing system purpose statements aligned with business goals
  2. Defining operational domains and constraints
  3. Mapping data flows in and out of AI components
  4. Documenting user roles and access levels
  5. Identifying integration points with non-AI systems
  6. Establishing performance expectations and limits
  7. Recording assumptions about model behavior
  8. Maintaining system context documentation over time
  9. Linking system context to control objectives
  10. Using diagrams to clarify system boundaries
  11. Versioning system context documentation
  12. Sharing context with audit and compliance teams
Module 5. Conducting Risk Assessments for AI Systems
Apply ISO 42001 risk assessment methodology to identify, evaluate, and prioritize risks associated with AI features.
12 chapters in this module
  1. Understanding risk types in AI systems
  2. Developing a risk taxonomy for product teams
  3. Identifying potential harms to users and business
  4. Assessing bias and fairness risks in training data
  5. Evaluating model transparency and explainability
  6. Considering safety and security implications
  7. Scoring risks based on likelihood and impact
  8. Documenting risk treatment plans
  9. Involving diverse stakeholders in risk review
  10. Integrating risk assessment into product backlog
  11. Updating assessments after model changes
  12. Reporting risk status to leadership
Module 6. Implementing Transparency and Explainability Controls
Design features and documentation that support user understanding and internal accountability for AI decisions.
12 chapters in this module
  1. Defining transparency goals for different user types
  2. Communicating model limitations to end users
  3. Providing meaningful explanations for AI outputs
  4. Designing user-facing model cards
  5. Creating internal model documentation for auditors
  6. Documenting training data provenance and quality
  7. Recording model development choices
  8. Explaining feature importance and decision logic
  9. Supporting user requests for AI decision rationale
  10. Balancing transparency with IP protection
  11. Integrating explainability into product design
  12. Testing explainability with real users
Module 7. Ensuring Human Oversight and Control
Structure human involvement in AI systems to maintain accountability and prevent unintended behavior.
12 chapters in this module
  1. Defining when human review is required
  2. Designing human-in-the-loop decision points
  3. Setting thresholds for automated vs manual action
  4. Training staff to monitor AI outputs
  5. Creating escalation procedures for anomalies
  6. Documenting human oversight responsibilities
  7. Designing user override capabilities
  8. Monitoring intervention frequency and trends
  9. Evaluating effectiveness of human control
  10. Updating oversight based on performance data
  11. Integrating oversight into incident response
  12. Reporting human control metrics to leadership
Module 8. Managing Data and Model Lifecycle
Apply ISO 42001 principles to data sourcing, model development, deployment, and retirement.
12 chapters in this module
  1. Establishing data provenance and lineage
  2. Documenting data collection methods
  3. Ensuring data quality and representativeness
  4. Managing data privacy and consent
  5. Tracking model versions and changes
  6. Defining model validation and testing standards
  7. Documenting model performance over time
  8. Planning for model retraining
  9. Handling model drift detection
  10. Establishing criteria for model retirement
  11. Archiving models and data securely
  12. Auditing model lifecycle decisions
Module 9. Implementing Robustness and Security Measures
Ensure AI systems operate reliably and resist attacks or unintended failures.
12 chapters in this module
  1. Defining robustness requirements for AI components
  2. Testing model resilience to edge cases
  3. Protecting against adversarial attacks
  4. Monitoring for data poisoning risks
  5. Implementing input validation
  6. Establishing fail-safe behaviors
  7. Documenting security controls
  8. Integrating with existing security infrastructure
  9. Responding to AI-specific security incidents
  10. Updating models after security findings
  11. Sharing security practices with customers
  12. Auditing robustness controls
Module 10. Designing for Privacy and Fairness
Embed privacy and fairness considerations into AI system design and deployment.
12 chapters in this module
  1. Conducting privacy impact assessments
  2. Minimizing data collection and retention
  3. Implementing data anonymization techniques
  4. Detecting and mitigating bias
  5. Testing for disparate impact
  6. Involving diverse teams in fairness review
  7. Documenting fairness mitigation actions
  8. Providing user control over data
  9. Responding to fairness complaints
  10. Updating models to address fairness issues
  11. Reporting on privacy and fairness metrics
  12. Aligning with regulatory expectations
Module 11. Monitoring and Performance Evaluation
Set up ongoing monitoring to ensure AI systems perform as intended and remain compliant.
12 chapters in this module
  1. Defining key performance indicators
  2. Establishing monitoring thresholds
  3. Tracking model accuracy over time
  4. Detecting concept drift
  5. Monitoring for unintended consequences
  6. Collecting user feedback
  7. Generating compliance reports
  8. Integrating monitoring into DevOps
  9. Alerting on performance degradation
  10. Reviewing model performance regularly
  11. Documenting monitoring findings
  12. Adjusting models based on performance data
Module 12. Preparing for Internal and External Audits
Assemble the documentation and artefacts needed to demonstrate compliance with ISO 42001 during audits.
12 chapters in this module
  1. Understanding audit expectations for AI governance
  2. Organizing documentation for easy retrieval
  3. Preparing for auditor questions
  4. Demonstrating control implementation
  5. Providing evidence of risk assessments
  6. Showing oversight of model updates
  7. Documenting corrective actions
  8. Maintaining audit trails
  9. Coordinating with compliance teams
  10. Using audit findings to improve governance
  11. Sharing best practices across teams
  12. Maintaining certification readiness

How this maps to your situation

  • Product-led AI governance in enterprise environments
  • Cross-functional alignment on AI control decisions
  • Roadmap influence through structured compliance artefacts
  • Scaling AI responsibly within complex product portfolios

Before vs. after

Before
AI governance feels like an external requirement that slows down delivery and creates rework.
After
You lead governance discussions, align teams proactively, and ship AI features with confidence and reduced friction.

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 per week over six weeks, with flexible access to materials.

If nothing changes
Without structured governance, AI initiatives face delays, compliance gaps, and loss of influence when critical decisions are made without your input.

How this compares to the alternatives

Unlike generic AI ethics courses, this program focuses on ISO 42001 implementation with product-specific templates and real-world examples tailored to senior product owners in tech enterprises.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior experience with ISO standards required?
No. The course starts with foundational concepts and builds to advanced application.
Will this help me influence roadmap decisions?
Yes. You'll gain the language and documentation tools to lead AI governance discussions and shape technical direction.
$199 one-time. Approximately 90 minutes per week over six weeks, with flexible access to materials..

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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