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AIG4025 Mastering ISO 42001 for Principal Programmers in Enterprise AI Governance

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Principal Programmers in Enterprise AI Governance

Build an AI governance portfolio that compounds across audits, reviews, and architecture 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.
Audit evidence packages that require last-minute reconciliation across tooling teams

The situation this course is for

Senior engineering roles face recurring time sinks during compliance cycles, particularly when assembling ISO 42001 evidence across distributed teams. The friction isn't failure, it's rework. The cost isn't fines, it's bandwidth. Every cycle demands rebuilding what could be standardized, delaying higher-impact architecture work.

Who this is for

Principal-level programmer in a regulated tech environment, accountable for AI system compliance and cross-functional evidence delivery, operating at the intersection of engineering rigor and governance expectation

Who this is not for

Entry-level developers, standalone contributors without governance scope, or practitioners focused only on non-AI compliance frameworks like SOC 2 or PCI DSS

What you walk away with

  • Produce a reusable Statement of Applicability that passes internal review on first submission
  • Automate evidence collection across CI/CD pipelines for ISO 42001 controls
  • Design an AI governance library that compounds across future audits
  • Reduce audit cycle prep time by 85% using standardized templates
  • Position yourself as the internal reference for AI governance implementation

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 42001 in Enterprise AI Systems
Establish the core principles of ISO 42001 as applied to large-scale AI deployments, focusing on governance structure, risk assessment, and organizational context. This module differentiates ISO 42001 from legacy frameworks and positions it as a strategic engineering asset.
12 chapters in this module
  1. Understanding the scope and intent of ISO 42001 in AI governance
  2. Differentiating ISO 42001 from ISO 27001 and NIST AI profiles
  3. Mapping AI governance to enterprise architecture domains
  4. Identifying organizational roles in AI system oversight
  5. Defining AI system boundaries for compliance scoping
  6. Assessing AI lifecycle stages under ISO 42001 requirements
  7. Integrating AI governance with existing risk frameworks
  8. Establishing governance oversight cadence for AI projects
  9. Documenting AI system purpose and intended use cases
  10. Evaluating third-party AI component compliance exposure
  11. Setting thresholds for AI system criticality classification
  12. Aligning AI governance with corporate ethics review boards
Module 2. Building the AI Governance Portfolio
Shift from one-off compliance to compounding asset creation by designing a living portfolio of governance artefacts. This module teaches how to structure documentation, evidence, and control mappings so they accumulate value across audits.
12 chapters in this module
  1. Defining the components of a reusable AI governance portfolio
  2. Structuring control mappings for future reuse
  3. Creating versioned documentation templates
  4. Automating artefact generation from code repositories
  5. Linking governance outputs to CI/CD pipelines
  6. Standardizing naming conventions for compliance assets
  7. Integrating portfolio updates into sprint planning
  8. Using metadata tagging for artefact discoverability
  9. Designing modular evidence packages
  10. Establishing ownership for portfolio maintenance
  11. Auditing portfolio completeness across AI systems
  12. Scaling portfolio use across engineering teams
Module 3. Automating Evidence Collection Across Toolchains
Eliminate manual reconciliation by embedding evidence collection into existing development workflows. This module focuses on integrating ISO 42001 requirements into Jenkins, GitLab, and internal tooling ecosystems.
12 chapters in this module
  1. Identifying automated evidence opportunities in CI/CD
  2. Mapping ISO 42001 controls to pipeline outputs
  3. Configuring audit trails in version control systems
  4. Extracting metadata for governance reporting
  5. Automating access review logs from identity providers
  6. Integrating static code analysis into compliance flows
  7. Generating control evidence from container registries
  8. Using observability data as compliance support
  9. Validating AI model lineage automatically
  10. Enforcing documentation completeness gates
  11. Building evidence dashboards for regulators
  12. Testing automated evidence under mock audits
Module 4. Designing the Statement of Applicability
Transform the SoA from a static document into a living, versioned artefact that evolves with AI systems. This module teaches how to justify inclusions and exclusions with technical precision and organizational context.
12 chapters in this module
  1. Structuring the SoA for maximum reuse
  2. Documenting control applicability with technical rationale
  3. Justifying exclusions using architecture diagrams
  4. Linking SoA sections to threat models
  5. Versioning SoA updates across AI releases
  6. Automating SoA completeness checks
  7. Using risk assessments to support control decisions
  8. Integrating peer review into SoA validation
  9. Aligning SoA with legal and regulatory obligations
  10. Handling third-party AI component disclosures
  11. Updating SoA during incident response cycles
  12. Archiving historical SoA versions for auditors
Module 5. Control Implementation in AI Development Workflows
Embed ISO 42001 controls directly into AI development practices, from data pipeline design to model deployment. This module focuses on making compliance a natural byproduct of engineering work.
12 chapters in this module
  1. Integrating data provenance into pipeline design
  2. Enforcing model versioning and lineage tracking
  3. Implementing bias detection as a pre-deployment gate
  4. Configuring model monitoring for drift detection
  5. Applying secure coding standards to AI components
  6. Validating model explainability requirements
  7. Enforcing access controls on training data
  8. Implementing model retraining triggers
  9. Documenting model development lifecycle stages
  10. Applying change management to AI system updates
  11. Testing adversarial robustness in staging environments
  12. Auditing model performance degradation thresholds
Module 6. Cross-Functional Governance Orchestration
Lead coordination across security, legal, product, and infrastructure teams without formal authority. This module teaches how to structure governance engagement to minimize friction and maximize buy-in.
12 chapters in this module
  1. Identifying stakeholders in AI governance workflows
  2. Designing governance touchpoints across teams
  3. Creating lightweight review processes for busy teams
  4. Using shared documentation to reduce meetings
  5. Escalating governance issues with technical evidence
  6. Building consensus on control applicability
  7. Integrating legal review into sprint cycles
  8. Coordinating with privacy officers on AI use cases
  9. Aligning with security teams on vulnerability management
  10. Managing scope disagreements with product managers
  11. Documenting cross-functional decisions
  12. Measuring governance process efficiency
Module 7. Risk Assessment for AI Systems
Conduct rigorous, defensible risk assessments that satisfy both auditors and engineers. This module focuses on building assessments that are technically sound and organizationally credible.
12 chapters in this module
  1. Defining AI system risk categories
  2. Assessing bias and fairness risks systematically
  3. Evaluating model transparency and explainability risks
  4. Identifying data privacy and protection risks
  5. Assessing model drift and performance degradation
  6. Evaluating adversarial attack surface
  7. Determining impact levels for AI decisions
  8. Assessing third-party AI component risks
  9. Documenting risk treatment decisions
  10. Using threat modeling outputs in risk assessments
  11. Updating risk assessments after incidents
  12. Automating risk scoring across AI inventory
Module 8. Audit Preparation and Response
Transform audit cycles from bandwidth sinks into credibility builders. This module teaches how to prepare for, respond to, and learn from audits using standardized, reusable processes.
12 chapters in this module
  1. Predicting auditor questions from ISO 42001 clauses
  2. Organizing evidence for efficient retrieval
  3. Preparing technical leads for auditor interviews
  4. Creating audit response templates
  5. Handling auditor findings with evidence
  6. Tracking findings to resolution
  7. Using mock audits to test readiness
  8. Automating audit trail generation
  9. Responding to auditor requests in days not weeks
  10. Building auditor confidence through consistency
  11. Documenting process improvements post-audit
  12. Sharing audit lessons across engineering teams
Module 9. Governance Automation with Scripts and Templates
Leverage scripting and templating to eliminate repetitive governance tasks. This module provides practical tools for automating documentation, evidence, and review workflows.
12 chapters in this module
  1. Creating automated documentation generators
  2. Building evidence collection scripts for APIs
  3. Using YAML templates for control mappings
  4. Automating SoA completeness checks
  5. Generating compliance dashboards from logs
  6. Scripting access review evidence collection
  7. Building model card generators
  8. Automating risk assessment templates
  9. Creating version control hooks for compliance
  10. Using CI/CD variables for governance flags
  11. Testing automation under audit conditions
  12. Maintaining automation scripts across teams
Module 10. Scaling Governance Across AI Portfolio
Extend governance practices from individual AI systems to enterprise-wide consistency. This module focuses on building frameworks that grow with AI adoption.
12 chapters in this module
  1. Assessing AI system inventory for governance coverage
  2. Prioritizing systems by risk and business impact
  3. Creating tiered governance approaches
  4. Standardizing governance across AI use cases
  5. Building central governance support functions
  6. Measuring governance maturity across teams
  7. Sharing governance artefacts across projects
  8. Creating governance onboarding for new teams
  9. Integrating governance into AI platform design
  10. Scaling automation across environments
  11. Managing governance debt
  12. Reporting governance metrics to leadership
Module 11. Continuous Improvement of AI Governance
Establish feedback loops that make governance smarter over time. This module teaches how to learn from audits, incidents, and team feedback to improve processes.
12 chapters in this module
  1. Collecting feedback from audit cycles
  2. Analyzing incident root causes for governance gaps
  3. Gathering team feedback on governance friction
  4. Prioritizing governance improvements
  5. Testing process changes in staging
  6. Measuring governance process efficiency
  7. Updating control mappings based on experience
  8. Improving automation based on usage
  9. Sharing lessons across governance teams
  10. Benchmarking against industry peers
  11. Adjusting governance for new AI capabilities
  12. Documenting governance evolution
Module 12. Building Your Personal Governance Practice
Turn your work into a compounding professional asset. This module focuses on building a portfolio that demonstrates expertise, increases influence, and creates career optionality.
12 chapters in this module
  1. Curating governance artefacts for professional visibility
  2. Documenting decision rationale for credibility
  3. Sharing governance approaches with peers
  4. Presenting at internal tech talks
  5. Writing internal blog posts on governance lessons
  6. Mentoring junior engineers on compliance
  7. Building cross-functional relationships
  8. Tracking governance impact on delivery speed
  9. Measuring personal contribution to audit success
  10. Creating a personal roadmap for governance growth
  11. Positioning yourself for leadership roles
  12. Leveraging governance experience for career moves

How this maps to your situation

  • Audit evidence preparation
  • Cross-functional coordination
  • Regulatory response readiness
  • Career-long governance practice

Before vs. after

Before
Spending 80+ hours per audit cycle reconciling evidence across teams, rebuilding documentation, and responding to auditor questions without standardized support.
After
Reducing audit prep to 6 hours using automated templates, reusable artefacts, and a living governance portfolio that compounds across deliveries.

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 12 weeks, with on-demand access for reference during audit cycles and architecture reviews.

If nothing changes
Without a systematic approach, governance work remains a recurring time sink, limiting capacity for higher-impact architecture leadership and creating missed opportunities to build a compounding professional portfolio.

How this compares to the alternatives

Generic compliance courses offer broad overviews but lack implementation specificity. Internal training is often fragmented. This course provides a complete, reusable system tailored to principal-level engineers in enterprise AI governance roles.

Frequently asked

Is this course focused on ISO 42001 specifically?
Yes, the course is built around ISO 42001 implementation for AI systems, with practical tools for evidence, automation, and cross-functional coordination.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with other frameworks like SOC 2 or NIST AI?
The practices are transferable, but the course focuses on ISO 42001 as the anchor standard for AI governance.
$199 one-time. Approximately 90 minutes per week over 12 weeks, with on-demand access for reference during audit cycles and architecture reviews..

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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