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AIG0060 Mastering ISO 42001 for AI Governance Product Leaders

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for AI Governance Product Leaders

Deliver AI governance frameworks that meet global standards and ship with 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.
Most AI governance initiatives stall in review because they lack a recognized control framework foundation.

The situation this course is for

Teams build AI governance policies that sound strong but fail under auditor scrutiny, missing traceability, falling short on documentation standards, or requiring rework because they weren’t aligned with ISO 42001 from the start. This delays go-lives, increases cost, and erodes trust with compliance and security stakeholders.

Who this is for

Senior product leaders building AI governance tooling who need to deliver frameworks that pass internal and external validation cycles the first time.

Who this is not for

Individual contributors focused on coding AI models, or compliance officers implementing existing frameworks without product design authority.

What you walk away with

  • Ship AI governance controls that align with ISO 42001 without external consultants
  • Produce documentation that passes auditor review the first time
  • Cut review cycle time by standardizing control mapping and evidence collection
  • Lead cross-functional alignment using a globally recognized AI governance standard
  • Build reusable implementation templates that scale across product lines

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 42001 in AI Systems
Introduce the structure, intent, and core clauses of ISO 42001 as applied to AI-powered platforms, focusing on governance integration points for product teams.
12 chapters in this module
  1. Understanding the scope of AI governance under ISO 42001
  2. Mapping AI system boundaries to control applicability
  3. Differentiating ISO 42001 from general AI ethics frameworks
  4. Key roles and responsibilities in governance implementation
  5. Establishing accountability for AI system decisions
  6. Integrating human oversight into automated workflows
  7. Defining AI system lifecycle stages for audit readiness
  8. Documenting training data sourcing and lineage
  9. Setting expectations for model performance monitoring
  10. Ensuring transparency in model versioning and updates
  11. Managing risk ratings across deployment environments
  12. Aligning AI governance with enterprise risk management
Module 2. Governance Framework Design for AI Products
Walk through designing a scalable governance architecture tailored to AI product suites, with emphasis on pre-audit readiness and stakeholder alignment.
12 chapters in this module
  1. Structuring governance for multi-tenant SaaS environments
  2. Embedding governance into continuous delivery pipelines
  3. Designing role-based access for AI system oversight
  4. Creating audit trails for model training and deployment
  5. Integrating governance checks into CI/CD workflows
  6. Balancing governance rigor with developer velocity
  7. Establishing approval workflows for model promotion
  8. Defining escalation paths for control violations
  9. Documenting governance decisions for external reviewers
  10. Versioning policy artifacts alongside code changes
  11. Automating evidence collection for recurring audits
  12. Building governance dashboards for leadership review
Module 3. Risk Assessment and Control Mapping
Detail the process of conducting ISO 42001-aligned risk assessments and translating findings into technical and procedural controls.
12 chapters in this module
  1. Identifying AI-specific risks in data and model layers
  2. Conducting stakeholder impact analysis for AI systems
  3. Classifying risks by severity and likelihood of occurrence
  4. Mapping control objectives to identified risk scenarios
  5. Selecting appropriate control types: technical, process, human
  6. Documenting control rationale and implementation scope
  7. Establishing control performance indicators and thresholds
  8. Integrating third-party risk into control design
  9. Assessing supply chain risks for AI components
  10. Linking control effectiveness to operational metrics
  11. Updating control mappings after incident review
  12. Maintaining control inventory for audit transparency
Module 4. Data Governance for AI Systems
Cover data-specific controls including provenance, quality, bias detection, and lifecycle management in alignment with ISO 42001.
12 chapters in this module
  1. Establishing data lineage tracking across model pipelines
  2. Defining data quality thresholds for training sets
  3. Implementing bias detection during data preprocessing
  4. Documenting data exclusion criteria and rationale
  5. Managing personal data in AI training workflows
  6. Ensuring data retention and deletion compliance
  7. Auditing data access and modification events
  8. Validating synthetic data use against governance rules
  9. Controlling data sharing with external partners
  10. Maintaining metadata standards across data sources
  11. Tracking data versioning for model reproducibility
  12. Reporting data drift to model monitoring systems
Module 5. Model Development Lifecycle Controls
Address governance integration points from ideation to deployment, ensuring auditability and compliance throughout the model lifecycle.
12 chapters in this module
  1. Requiring documented business justification for new models
  2. Enforcing model design reviews before development
  3. Setting version control standards for model code
  4. Validating training data provenance and quality
  5. Requiring bias and fairness testing before evaluation
  6. Documenting model evaluation metrics and thresholds
  7. Automating model card generation for each release
  8. Establishing human-in-the-loop requirements
  9. Setting thresholds for model accuracy and drift
  10. Creating rollback procedures for model failures
  11. Auditing model update approvals and deployment
  12. Preserving model artifacts for future review
Module 6. Transparency and Explainability Implementation
Detail methods to meet ISO 42001 transparency requirements through documentation, UI patterns, and reporting.
12 chapters in this module
  1. Defining explainability expectations by use case
  2. Selecting appropriate model interpretability techniques
  3. Generating user-facing model decision summaries
  4. Documenting model limitations and known biases
  5. Providing access to model performance metrics
  6. Creating model cards for internal and external use
  7. Publishing data and methodology summaries
  8. Designing interfaces for user contestation
  9. Logging user interactions with AI outputs
  10. Reporting model confidence levels in real time
  11. Updating transparency documentation with each release
  12. Training support teams on explaining AI decisions
Module 7. Human Oversight and Intervention Mechanisms
Design effective human-in-the-loop systems that satisfy ISO 42001 requirements for oversight and error correction.
12 chapters in this module
  1. Identifying critical decision points for human review
  2. Setting thresholds for automatic human escalation
  3. Designing efficient review workflows for operators
  4. Providing decision context for human reviewers
  5. Logging human override decisions and rationale
  6. Measuring time-to-intervention for high-risk cases
  7. Retraining models based on override patterns
  8. Defining escalation paths for unresolved conflicts
  9. Ensuring reviewer qualifications and training
  10. Auditing oversight performance metrics
  11. Balancing automation with required oversight
  12. Reporting human-AI interaction trends to leadership
Module 8. Monitoring and Performance Validation
Implement ongoing monitoring systems to detect model drift, performance decay, and unintended behavior.
12 chapters in this module
  1. Defining KPIs for model operational success
  2. Setting up real-time performance dashboards
  3. Detecting statistical drift in input data
  4. Monitoring prediction confidence distributions
  5. Alerting on anomalous output patterns
  6. Conducting periodic model re-evaluation
  7. Validating model fairness over time
  8. Measuring business impact of AI decisions
  9. Integrating feedback loops from end users
  10. Tracking model usage across customer segments
  11. Automating model health reports
  12. Triggering retraining based on performance thresholds
Module 9. Third-Party and Supply Chain Risk Management
Cover controls for managing external AI vendors, open-source components, and cloud infrastructure risks.
12 chapters in this module
  1. Assessing vendor compliance with ISO 42001
  2. Requiring third-party audit evidence for AI tools
  3. Controlling use of open-source AI frameworks
  4. Auditing cloud provider configurations for AI workloads
  5. Managing dependencies on external APIs
  6. Evaluating pre-trained models for bias and drift
  7. Screening AI components for license compliance
  8. Establishing vendor incident response coordination
  9. Requiring data processing agreements with suppliers
  10. Conducting security reviews of AI APIs
  11. Managing model update approvals for third-party tools
  12. Documenting supply chain risks in governance reports
Module 10. Incident Response and Model Reversion
Build incident response protocols specific to AI system failures, bias incidents, and unintended consequences.
12 chapters in this module
  1. Defining AI incident classification and severity levels
  2. Creating response playbooks for model failures
  3. Establishing communication protocols for bias incidents
  4. Activating human review during crisis events
  5. Rolling back models to last stable version
  6. Notifying affected users of AI errors
  7. Conducting root cause analysis for AI incidents
  8. Updating training data to prevent recurrence
  9. Reporting incidents to compliance and legal teams
  10. Documenting response actions for auditor review
  11. Updating model monitoring based on incident data
  12. Conducting post-mortems across engineering teams
Module 11. Audit Preparation and Evidence Packaging
Teach how to package documentation, logs, and control evidence for internal and external audits.
12 chapters in this module
  1. Organizing documentation for ISO 42001 review
  2. Generating control implementation reports
  3. Compiling model cards and data lineage records
  4. Preparing access logs for auditor review
  5. Demonstrating control effectiveness with metrics
  6. Responding to auditor inquiries with evidence
  7. Automating evidence collection workflows
  8. Versioning audit packages for consistency
  9. Conducting pre-audit readiness checks
  10. Training teams on audit response procedures
  11. Maintaining evidence repositories for retention
  12. Reporting on continuous improvement from audit feedback
Module 12. Continuous Improvement and Governance Scaling
Implement feedback loops and scaling strategies to evolve AI governance as products grow.
12 chapters in this module
  1. Collecting feedback from audit and incident reviews
  2. Updating governance policies based on findings
  3. Scaling controls across new product lines
  4. Integrating lessons into training for new hires
  5. Monitoring governance maturity over time
  6. Benchmarking against industry peers
  7. Adopting new control frameworks as needed
  8. Aligning governance updates with product roadmaps
  9. Measuring time-to-compliance for new features
  10. Reducing governance overhead through automation
  11. Building cross-functional governance councils
  12. Publishing annual AI governance reports

How this maps to your situation

  • Aligning AI product design with ISO 42001 from initial concept
  • Meeting auditor expectations with pre-packaged evidence
  • Reducing governance rework through standardized templates
  • Leading cross-functional alignment on AI control ownership

Before vs. after

Before
AI governance efforts stall in review due to misaligned controls, missing documentation, or lack of standardization, leading to rework and delayed shipping.
After
Ship compliant, defensible AI systems on schedule with ISO 42001-aligned frameworks that pass auditor scrutiny the first time.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • 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, designed for senior practitioners balancing delivery and strategy.

If nothing changes
Without a recognized governance foundation, AI products face delays, compliance risk, and erosion of cross-functional trust, jeopardizing leadership credibility and market timing.

How this compares to the alternatives

Unlike generic AI ethics courses, this program delivers actionable ISO 42001 implementation patterns tailored to product leaders building real systems, complete with audit-ready templates and deployment checklists.

Frequently asked

Who is this course best suited for?
AI product leaders and technical program managers responsible for shipping governance-compliant AI systems in enterprise environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior knowledge of ISO 42001 required?
No. The course is designed to bring product leaders up to speed with practical implementation patterns, not theoretical overview.
$199 one-time. Approximately 90 minutes per week over six weeks, designed for senior practitioners balancing delivery and strategy..

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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