A tailored course, built for your situation
Mastering ISO 42001 for Data Engineering Practitioners
Turn AI governance into a documented, defensible engineering function with a complete control framework
$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 feels scattered, owned by no one, enforced by checklists, and exposed during audits.
The situation this course is for
Teams ship models fast, but when compliance calls, there's no single source of truth. Controls are re-invented per project. Audits expose gaps. Engineering time gets pulled into reactive fixes instead of proactive design.
Who this is for
Senior data engineer in a global systems integrator navigating rising AI governance demands with no clear framework or ownership model
Who this is not for
Entry-level analysts, pure-play data scientists without compliance exposure, or practitioners outside regulated deployment cycles
What you walk away with
- Documented control ownership for AI systems under ISO 42001 that maps to your current role
- Repeatable evidence flows from data pipeline to compliance report
- Clear scope expansion into AI governance without organizational change
- First internal reference implementation of ISO 42001 in engineering backlog
- Structured playbook that survives team turnover or leadership changes
The 12 modules (with all 144 chapters)
Module 1. Why ISO 42001 is becoming the baseline for AI governance in engineering teams
Explore how ISO 42001 shifts AI governance from abstract principle to auditable engineering responsibility. Learn why data engineers are best positioned to own it now, and how to claim that space without waiting for permission.
12 chapters in this module
- How ISO 42001 differs from earlier AI ethics guidelines
- The three concrete artefacts that prove compliance
- Why data engineers are first in line for ownership
- Mapping control requirements to existing ETL jobs
- How consultancy delivery cycles create natural entry points
- Recognizing when governance becomes engineering scope
- Case example: First compliant data pipeline at a Tier 1 bank
- Common missteps when bridging data and compliance teams
- Ownership signals that auditors actually look for
- How to position ISO 42001 as a delivery accelerator
- Integrating control checks into CI/CD pipelines
- When to escalate vs. when to own outright
Module 2. Structuring your AI governance mandate within current role boundaries
Define a scope of ownership that expands your influence without requiring a promotion. Use existing deliverables as anchor points for formalizing control authority.
12 chapters in this module
- Identifying five existing assets you already control
- How to embed governance into sprint planning
- Positioning documentation as a force multiplier
- Where your current role meets compliance handoff points
- Creating traceability from code to control clause
- Using data lineage to justify broader oversight
- Avoiding overreach while claiming authority
- Linking data quality metrics to control outcomes
- Documenting decisions that others rely on
- Establishing default ownership by doing first
- Proving consistency across project teams
- Building credibility through repeatable outputs
Module 3. Building the audit-ready data lineage map for ISO 42001
Transform raw lineage into a compliance-grade artefact. Learn what auditors accept, and what they dismiss, as proof of control.
12 chapters in this module
- Difference between operational and compliance lineage
- Minimum viable lineage for control validation
- Tagging data flows to specific ISO 42001 clauses
- Automating metadata capture for audit trails
- Validating lineage against data retention policies
- Handling edge cases in cross-border data movement
- Documenting exceptions without weakening controls
- Using lineage to reduce audit preparation time
- Integrating with existing data catalog tools
- Proving completeness under time pressure
- Versioning lineage maps per deployment cycle
- Sharing lineage with compliance teams securely
Module 4. Designing repeatable evidence workflows from engineered components
Stop rebuilding evidence per audit. Create systems that generate compliant artefacts as a byproduct of normal work.
12 chapters in this module
- Embedding evidence generation in pipeline jobs
- Automating control logs with structured outputs
- Standardizing naming conventions for auditors
- Creating evidence templates used across teams
- Validating evidence against ISO 42001 clause 8.4
- Scheduling automatic retention and archiving
- Linking code commits to control implementation
- Using pipeline metrics as proxy for control health
- Generating summary reports for non-technical reviewers
- Handling reprocessing and corrections transparently
- Integrating with ticketing systems for traceability
- Reducing manual collection effort by 70%
Module 5. Aligning data engineering with compliance teams using ISO 42001 language
Speak the same language as compliance. Translate technical outputs into governance-ready inputs.
12 chapters in this module
- Mapping pipeline stages to ISO 42001 control domains
- Translating data drift alerts into risk statements
- Documenting model inputs as controlled data assets
- Creating governance summaries from pipeline logs
- Using control objectives to prioritize tech debt
- Responding to compliance queries with evidence
- Avoiding jargon mismatches in cross-functional meetings
- Pre-empting audit findings with proactive disclosure
- Building trust through consistent terminology
- Clarifying ownership boundaries with legal teams
- Escalating issues using formal control language
- Maintaining independence while collaborating
Module 6. Implementing ISO 42001 control clauses in data pipeline design
Hardwire compliance into architecture. Make controls unavoidable, not optional.
12 chapters in this module
- Clause 6.3: Identifying AI system boundaries in code
- Clause 7.2: Training records embedded in CI/CD
- Clause 8.1: Documenting data processing purposes
- Clause 8.4: Managing third-party data dependencies
- Clause 9.1: Automating performance monitoring
- Clause 9.2: Building internal audit capability
- Clause 10.1: Implementing corrective actions in code
- Clause 10.2: Capturing lessons learned automatically
- Validating controls during pull request reviews
- Enforcing controls at deployment gates
- Testing control failure modes in staging
- Reconciling controls across microservices
Module 7. Creating the Statement of Applicability for AI systems
Produce the foundational document that defines what applies, and why, based on your actual data architecture.
12 chapters in this module
- Starting from data topology, not control list
- Justifying exclusions based on engineering design
- Linking controls to specific pipeline components
- Using threat modelling to prioritize coverage
- Documenting rationale for each inclusion decision
- Versioning SoA with codebase releases
- Reviewing SoA with compliance teams effectively
- Updating SoA during incident response
- Aligning SoA with client-specific requirements
- Using SoA to deflect out-of-scope requests
- Proving consistency across engagements
- Archiving historical SoA versions
Module 8. Documenting AI system boundaries and processing purposes
Clarify what's in and out of scope for governance. Use precise definitions to reduce ambiguity and audit risk.
12 chapters in this module
- Defining AI system scope in engineering terms
- Mapping models to data sources with precision
- Documenting processing purposes per jurisdiction
- Handling edge cases: batch vs real-time pipelines
- Versioning system boundaries with deployments
- Notifying stakeholders of boundary changes
- Using diagrams that auditors accept
- Proving closure of deprecated systems
- Handling shadow AI models in test environments
- Linking system inventory to asset management
- Automating boundary documentation updates
- Validating scope completeness annually
Module 9. Managing third-party and open-source components under ISO 42001
Govern what you don't build. Extend control to external dependencies.
12 chapters in this module
- Inventoring all third-party data processors
- Validating open-source license compliance
- Assessing AI model cards for completeness
- Documenting API usage against control clauses
- Requiring SOC 2 reports from vendors
- Creating fallback paths for deprecated libraries
- Tracking version lifecycles across dependencies
- Enforcing update policies in CI/CD
- Monitoring for security vulnerabilities
- Handling emergencies without bypassing controls
- Auditing vendor compliance independently
- Documenting due diligence for regulator queries
Module 10. Building internal audit capability for AI governance
Shift from reactive to proactive. Equip your team to self-assess and improve.
12 chapters in this module
- Designing lightweight internal review cycles
- Creating audit checklists from control clauses
- Training team members on compliance expectations
- Running mock audits before external review
- Capturing findings in trackable systems
- Prioritizing remediation based on risk
- Using audit results to improve pipeline design
- Reporting upward without alarmism
- Integrating audit outcomes into sprint planning
- Measuring improvement over time
- Recognizing team contributions publicly
- Sustaining audit readiness between cycles
Module 11. Incorporating lessons learned into engineering feedback loops
Make failures productive. Turn incidents into improvements.
12 chapters in this module
- Documenting root causes without blame
- Linking incident reports to control gaps
- Updating training materials based on events
- Adjusting pipeline design to prevent recurrence
- Sharing insights across project teams
- Using retrospectives to strengthen controls
- Updating SoA after real-world events
- Validating fixes with automated tests
- Reporting improvements to compliance teams
- Archiving case studies for future reference
- Recognizing proactive improvements
- Creating feedback loops that last
Module 12. Scaling AI governance ownership across projects and teams
Extend your model. Make compliance repeatable across the organization.
12 chapters in this module
- Creating onboarding materials for new engineers
- Standardizing templates across delivery units
- Documenting best practices from early wins
- Mentoring peers on control implementation
- Using code reviews to spread knowledge
- Building internal communities of practice
- Sharing playbooks with other chapters
- Measuring adoption across teams
- Celebrating compliance successes
- Adjusting approach based on feedback
- Reducing time-to-compliance for new projects
- Establishing yourself as the default starting point
How this maps to your situation
- Data engineers now lead AI governance in consultancies
- ISO 42001 creates formal scope for technical owners
- Compliance evidence must come from engineered systems
- Ownership expands without title changes
Before vs. after
Before
AI governance feels like an add-on, something extra that lands outside normal deliverables.
After
You lead AI governance through engineered systems, with documented ownership and repeatable compliance outputs.
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 six weeks, with self-paced access forever
If nothing changes
Without structured ownership, AI governance remains reactive, exposing teams during audits and limiting career growth despite strong technical work.
How this compares to the alternatives
Unlike generic AI ethics courses, this focuses on documented control implementation that expands your mandate. Unlike auditor-led trainings, it's built for engineers who ship code.
Frequently asked
Is this about AI ethics or technical implementation?
Technical implementation. We focus on building engineered systems that satisfy ISO 42001 controls, using code, pipelines, and metadata as evidence.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead governance without a formal title?
Yes. The course shows how to use existing deliverables to claim de facto ownership, documented in a way that scales and survives review.
$199 one-time. 90 minutes per week over six weeks, with self-paced access forever.
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