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DAT1848 Mastering ISO 42001 for Web Developers in High-Growth Tech

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Web Developers in High-Growth Tech

Build AI governance into your development workflow with confidence and clarity

$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 packages needing rework due to inconsistent AI documentation

The situation this course is for

Engineering teams spend weeks reconstructing evidence trails after the fact, scrambling to meet compliance expectations on AI use. The burden lands on developers to document decisions post-launch, creating friction between innovation velocity and governance standards.

Who this is for

Web Developers in fast-moving tech environments who integrate AI features and need to demonstrate governance alignment without slowing delivery

Who this is not for

Executives seeking board-level oversight frameworks, consultants selling maturity assessments, or compliance officers focused on policy drafting rather than implementation

What you walk away with

  • Own the call on whether an AI model stays in or gets rewritten pre-deployment
  • Document integrations once, use across audit cycles without rework
  • Ship AI features with built-in ISO 42001 alignment from day one
  • Reduce time from code commit to audit-ready evidence from days to hours
  • Become the go-to developer for AI governance questions on your team

The 12 modules (with all 144 chapters)

Module 1. Why ISO 42001 Matters for Developers, Not Just Auditors
Understand how AI governance shifts from abstract policy to code-level accountability, focusing on developer-specific controls and responsibilities in ISO 42001.
12 chapters in this module
  1. How AI governance impacts your daily development workflow
  2. The difference between organizational and developer-level compliance
  3. Mapping ISO 42001 clauses to real integration decisions
  4. Why documentation starts at the pull request, not the audit
  5. Developer-specific risks in AI model selection and training
  6. How Shopify’s ecosystem shapes AI governance expectations
  7. When to escalate versus when to decide locally
  8. Version control as a compliance asset
  9. Integrating ISO 42001 checks into your CI/CD pipeline
  10. Documenting AI decisions for non-developer reviewers
  11. Avoiding rework by aligning early with compliance expectations
  12. Common pitfalls when developers inherit AI models without governance
Module 2. AI Asset Register: Building It Once, Using It Forever
Create a living inventory of AI components in your applications that satisfies both technical and audit needs.
12 chapters in this module
  1. Defining what counts as an AI asset in your stack
  2. Naming conventions for AI components that survive team changes
  3. Linking model versions to deployment environments
  4. Automating asset discovery from code repositories
  5. Tracking third-party AI libraries and dependencies
  6. Documenting training data sources and lineage
  7. Maintaining ownership records for every AI module
  8. Versioning your asset register alongside code
  9. Exporting the register for auditor consumption
  10. Connecting assets to control objectives in ISO 42001
  11. Handling deprecation and retirement of AI models
  12. Auditable timestamps for asset changes
Module 3. AI Risk Assessment: Practical Templates for Developers
Apply lightweight, repeatable risk assessments to AI features without slowing product velocity.
12 chapters in this module
  1. Scoping AI risk at feature level, not enterprise level
  2. Identifying high-risk AI patterns in frontend and backend systems
  3. Template for 30-minute risk scoring per integration
  4. Classifying AI impact on privacy, safety, and fairness
  5. Developer-friendly risk language that auditors accept
  6. When to call in domain experts versus proceed independently
  7. Documenting risk treatment decisions in pull request comments
  8. Linking risk scores to control implementation
  9. Updating assessments after model retraining
  10. Avoiding over-engineering for low-impact AI features
  11. Cross-checking with legal and compliance thresholds
  12. Archiving assessment records for audit
Module 4. Data Governance for AI: From Bias to Lineage
Implement traceable data practices that satisfy ISO 42001 requirements for transparency and fairness.
12 chapters in this module
  1. Proving data provenance for training and inference
  2. Documenting data preprocessing steps for audit
  3. Tracking data splits and their rationale
  4. Checking for bias in training sets with lightweight tooling
  5. Versioning training data alongside models
  6. Handling synthetic data in governance workflows
  7. Data retention policies for AI components
  8. Annotating sensitive data usage in AI pipelines
  9. Logging data drift detection results
  10. Connecting data decisions to fairness controls
  11. Automating data documentation from notebook runs
  12. Exporting data lineage for external reviewers
Module 5. Model Documentation That Survives Review Cycles
Build technical documentation that satisfies both engineering standards and compliance reviewers.
12 chapters in this module
  1. Required elements of model cards under ISO 42001
  2. Writing model descriptions that developers and auditors understand
  3. Versioning model documentation with code
  4. Automatically generating model cards from training logs
  5. Linking model decisions to risk assessments
  6. Capturing model performance metrics over time
  7. Documenting intended and unintended use cases
  8. Recording assumptions and limitations in plain language
  9. Including testing procedures and results
  10. Handling open-source model attribution
  11. Archiving model documentation for decommissioned systems
  12. Maintaining documentation across team turnover
Module 6. Transparency Controls for AI Systems
Implement practical transparency features that align with ISO 42001 without compromising IP or performance.
12 chapters in this module
  1. Deciding what to disclose and what to protect
  2. User-facing explanations that don’t slow UI
  3. Logging model decisions for audit without overhead
  4. Creating accessible documentation for non-technical users
  5. Handling model uncertainty in user interactions
  6. Providing meaningful recourse paths
  7. Versioning transparency artifacts with models
  8. Automating transparency reports from inference logs
  9. Balancing explainability with performance needs
  10. Documenting transparency implementation in SoA
  11. Handling third-party model transparency gaps
  12. Updating transparency materials after retraining
Module 7. Human Oversight Mechanisms in Automated Systems
Design developer-level controls that ensure humans remain in the loop where required.
12 chapters in this module
  1. Identifying which AI decisions need human review
  2. Implementing lightweight approval workflows
  3. Logging human intervention points
  4. Setting thresholds for automatic escalation
  5. Documenting oversight design in architecture diagrams
  6. Testing override functionality regularly
  7. Handling edge cases where oversight fails
  8. Versioning oversight rules with code
  9. Auditing human-in-the-loop effectiveness
  10. Reducing false positives in review triggers
  11. Balancing speed and control in high-frequency systems
  12. Archiving oversight logs for compliance
Module 8. Bias and Fairness Testing in Development Workflows
Integrate fairness checks into your daily development cycle without slowing delivery.
12 chapters in this module
  1. Identifying fairness-critical AI features
  2. Selecting appropriate metrics for different use cases
  3. Running bias tests during CI/CD pipeline execution
  4. Documenting fairness assessment methods
  5. Handling edge cases in demographic data
  6. Updating tests after model retraining
  7. Logging results for audit purposes
  8. Integrating open-source fairness tools
  9. Setting thresholds for intervention
  10. Avoiding over-testing low-impact features
  11. Cross-checking with legal and policy requirements
  12. Archiving fairness reports across versions
Module 9. Robustness and Safety Testing for AI Components
Apply developer-level testing practices that satisfy ISO 42001 safety requirements.
12 chapters in this module
  1. Defining robustness expectations for different AI types
  2. Testing model performance under edge conditions
  3. Monitoring for adversarial inputs
  4. Implementing fallback mechanisms
  5. Logging safety test results
  6. Versioning test configurations
  7. Automating regression testing for retrained models
  8. Documenting testing procedures in SoA
  9. Handling third-party model safety gaps
  10. Updating tests after environment changes
  11. Balancing thoroughness with deployment speed
  12. Archiving test results for audit
Module 10. Accountability and Ownership in AI Systems
Clarify developer-level ownership to ensure clear accountability in AI implementations.
12 chapters in this module
  1. Assigning ownership at the component level
  2. Documenting ownership transitions
  3. Handling team changes without governance gaps
  4. Linking code ownership to ISO 42001 controls
  5. Maintaining up-to-date contact records
  6. Escalation paths for ownership questions
  7. Versioning ownership records with code
  8. Automating ownership verification
  9. Handling open-source and third-party components
  10. Documenting ownership in audit packages
  11. Cross-checking with HR and org structure
  12. Archiving ownership data for decommissioned systems
Module 11. Automating ISO 42001 Compliance Evidence
Build self-documenting systems that generate audit-ready evidence without manual rework.
12 chapters in this module
  1. Identifying evidence requirements for each control
  2. Automating logs for AI decision tracking
  3. Generating compliance reports from code
  4. Integrating evidence collection into CI/CD
  5. Versioning evidence alongside artifacts
  6. Exporting structured data for auditor review
  7. Handling evidence retention policies
  8. Validating automated evidence quality
  9. Reducing manual documentation burden
  10. Cross-checking against ISO 42001 requirements
  11. Documenting automation in control mappings
  12. Maintaining evidence systems during tech changes
Module 12. Maintaining AI Governance Through Iteration Cycles
Keep governance alive through continuous development without creating bottlenecks.
12 chapters in this module
  1. Updating governance artifacts after retraining
  2. Handling model versioning and rollback
  3. Communicating changes to stakeholders
  4. Reviewing control effectiveness regularly
  5. Adapting to new threats and vulnerabilities
  6. Handling dependency updates in AI components
  7. Updating documentation for incremental changes
  8. Maintaining audit trails through iterations
  9. Reducing governance drag on velocity
  10. Automating governance checks in sprints
  11. Documenting changes for audit
  12. Archiving governance records for decommissioned features

How this maps to your situation

  • Initial AI integration planning
  • Ongoing development and iteration
  • Pre-audit preparation
  • Post-deployment governance maintenance

Before vs. after

Before
Spending extra time reconstructing documentation for AI features after development, responding to last-minute compliance requests, and facing uncertainty about audit readiness.
After
Shipping AI-integrated features with built-in governance evidence, reducing rework, and confidently owning key compliance decisions in your stack.

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 of focused learning, plus 30 minutes per module for implementation planning.

If nothing changes
Without structured AI governance practices, developers face growing audit friction, last-minute rework, and increased personal accountability for compliance gaps, especially as AI scrutiny intensifies in high-growth tech.

How this compares to the alternatives

Unlike generic AI ethics courses or executive-level compliance trainings, this course focuses exclusively on developer-level actions, decisions, and documentation that directly satisfy ISO 42001 requirements without slowing delivery.

Frequently asked

Is this course only for developers using Shopify’s platform?
No. While the examples are tailored to developers in Shopify’s ecosystem, the practices apply to any web developer integrating AI features in high-growth environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an ISO 42001 audit?
Yes. The course teaches you how to build and document AI systems that meet ISO 42001 requirements from a developer's perspective, making audit evidence ready at time of deployment.
$199 one-time. 90 minutes of focused learning, plus 30 minutes per module for implementation planning..

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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