A tailored course, built for your situation
Executive Visibility on Data Pipeline Governance Under AI Act Requirements
Prove alignment between engineering work and emerging AI regulation without stepping off your core roadmap
$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.
Technical work that meets compliance standards but stays invisible to leadership
The situation this course is for
Engineers deliver robust pipelines, but without structured traceability to regulatory frameworks, their work gets overlooked in AI governance reviews. The result: missed recognition, repeated requests for documentation, and external consultants brought in to 'translate' tech work for compliance teams.
Who this is for
Senior data engineer or IC at a data platform company, delivering ETL and governance workflows while navigating growing regulatory scrutiny
Who this is not for
Entry-level analysts, product marketers, or managers looking for high-level AI policy summaries
What you walk away with
- Surface data pipeline decisions to leadership with clear alignment to AI Act requirements
- Build traceable documentation that satisfies regulatory inquiries without rework
- Position yourself as the internal reference for AI governance compliance
- Reduce redundant documentation requests from compliance and audit teams
- Anchor technical work in executive-level AI oversight conversations
The 12 modules (with all 144 chapters)
Module 1. Mapping ETL workflows to AI Act high-risk criteria
Identify which data pipelines fall under AI Act scrutiny based on use case, data type, and downstream impact. Start framing your existing work within regulatory boundaries.
12 chapters in this module
- Understanding AI Act scope
- High-risk AI system definitions
- Data provenance thresholds
- ETL touchpoint analysis
- Risk classification matrix
- Use case tagging protocol
- Downstream impact mapping
- Data lineage thresholds
- System boundary definition
- Regulatory trigger checklist
- Pipeline segmentation strategy
- Initial scope validation
Module 2. Documenting data provenance for audit readiness
Turn SQL scripts and SSIS jobs into auditable artefacts with clear provenance. Build documentation that survives team changes and inspection cycles.
12 chapters in this module
- Provenance metadata standards
- Source system attribution
- Data transformation logging
- Version control integration
- Schema change tracking
- Ownership declaration format
- Data origin certification
- Update frequency declaration
- External data handling
- Third-party data tagging
- Internal lineage sign-off
- Audit trail preservation
Module 3. Aligning pipeline logic with transparency obligations
Structure ETL logic to meet AI Act transparency requirements. Show how data flows support explainability and human oversight.
12 chapters in this module
- Explainability thresholds
- Human oversight touchpoints
- Decision logic documentation
- Model input traceability
- Feature lineage mapping
- Bias mitigation evidence
- Data quality thresholds
- Monitoring trigger design
- Feedback loop integration
- Logging for reviewability
- Threshold exception handling
- Review cycle documentation
Module 4. Integrating Azure AWS services into compliant workflows
Ensure cloud infrastructure choices support AI Act compliance. Document platform decisions as part of governance posture.
12 chapters in this module
- Cloud service classification
- Processing location transparency
- Access control alignment
- Encryption standard mapping
- Vendor compliance checks
- Service-level attestation
- Cross-border data rules
- Disaster recovery alignment
- Incident response integration
- Monitoring configuration
- Change approval workflow
- Platform audit logging
Module 5. Building repeatable compliance documentation templates
Develop standard templates that turn pipeline delivery into automatic compliance artefacts. Eliminate ad-hoc requests.
12 chapters in this module
- Template design principles
- Automated metadata capture
- Version control sync
- Approval workflow integration
- Storage location standards
- Access control setup
- Update notification system
- Retirement protocol
- Cross-team distribution
- Feedback incorporation
- Compliance versioning
- Template audit trail
Module 6. Creating traceability from code to regulatory requirements
Link individual scripts and jobs to specific AI Act articles. Make technical work self-validating under scrutiny.
12 chapters in this module
- Requirement tagging system
- Code annotation standards
- Cross-reference indexing
- Automated validation rules
- Change impact analysis
- Regression testing scope
- Documentation sync protocol
- Review cycle alignment
- Exception handling process
- Audit readiness checklist
- Stakeholder access setup
- Version comparison tool
Module 7. Positioning technical work in executive oversight conversations
Frame engineering deliverables as governance enablers. Shift from contributor to trusted advisor in AI oversight discussions.
12 chapters in this module
- Executive summary format
- Risk exposure translation
- Technical debt quantification
- Compliance gap framing
- Remediation timeline design
- Resource requirement justification
- Strategic option presentation
- Escalation threshold definition
- Trade-off communication
- Success metric alignment
- Stakeholder expectation setting
- Follow-up action tracking
Module 8. Reducing rework from compliance and audit teams
Preempt documentation requests by building self-explanatory pipelines. Cut cycle time on external review responses.
12 chapters in this module
- Request pattern analysis
- Common gap identification
- Proactive documentation design
- Standard response templates
- Internal audit simulation
- Feedback loop integration
- Compliance team alignment
- Review cycle anticipation
- Exception process design
- Historical reference archive
- Version comparison system
- Lessons learned integration
Module 9. Establishing recognition as the go-to AI governance practitioner
Become the internal reference point for AI Act compliance. Let your technical work drive policy interpretation.
12 chapters in this module
- Internal knowledge sharing
- Cross-functional collaboration
- Mentorship opportunity identification
- Best practice documentation
- Lessons learned dissemination
- Compliance advisory role
- Policy interpretation guidance
- Stakeholder trust building
- Visibility opportunity targeting
- Reputation management
- Feedback incorporation process
- Thought leadership positioning
Module 10. Designing governance-aware development workflows
Embed compliance considerations into daily engineering practice. Make governance a default, not an afterthought.
12 chapters in this module
- Onboarding integration
- Code review checklist
- Pull request governance
- Testing requirement alignment
- Deployment gate criteria
- Monitoring alert mapping
- Incident response integration
- Change management sync
- Stakeholder notification
- Audit trail enrichment
- Post-mortem contribution
- Process improvement loop
Module 11. Supporting third-party audit and certification efforts
Prepare for external validation of AI systems. Provide evidence that meets auditor expectations.
12 chapters in this module
- Auditor expectation mapping
- Evidence package structure
- Documentation sufficiency test
- Gap remediation protocol
- Interview preparation materials
- Reference artefact indexing
- Compliance assertion drafting
- Response validation process
- Escalation path definition
- Timeline management
- Stakeholder coordination
- Post-audit follow-up
Module 12. Sustaining compliance through team and system changes
Ensure governance knowledge survives personnel changes and platform migrations. Build institutional memory.
12 chapters in this module
- Knowledge transfer protocol
- Onboarding integration
- Documentation ownership
- Review cycle scheduling
- Change impact assessment
- Historical reference system
- Lessons learned archive
- Process evolution tracking
- Stakeholder alignment
- Compliance drift detection
- Remediation trigger design
- Continuous improvement loop
How this maps to your situation
- When responding to AI governance audits
- When designing new ETL pipelines
- When updating legacy data workflows
- When collaborating with compliance teams
Before vs. after
Before
Technical work is completed but lacks formal alignment to AI Act requirements. Documentation is reactive, fragmented, and often requested after delivery.
After
Every pipeline includes built-in traceability to regulatory standards. Your work becomes the benchmark for compliance readiness and is proactively surfaced to leadership.
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 3 hours per week over 4 weeks, with self-paced access to all materials.
If nothing changes
Continue delivering strong technical work that remains invisible to governance teams. Risk being bypassed when compliance initiatives are staffed or external consultants are brought in to interpret your work.
How this compares to the alternatives
Generic AI governance courses focus on policy and theory. This course is built for practitioners who deliver ETL, SQL, and cloud data workflows and need to align with AI Act requirements without disrupting delivery.
Frequently asked
Do I need prior experience with AI Act compliance?
No. The course starts from engineering execution and shows how to align it with regulatory expectations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-AI Act frameworks?
Yes. The traceability and documentation methods work for ISO 42001, NIST AI RMF, and other governance standards.
$199 one-time. Approximately 3 hours per week over 4 weeks, with self-paced access to all 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