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Influence across more business lines with AI Act compliance design

$199.00
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A tailored course, built for your situation

Influence across more business lines with AI Act compliance design

Turn regulatory precision into cross-functional impact

$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.
Regulatory work stuck in silos limits visibility and impact

The situation this course is for

Even technically sound implementations lose traction when they can't speak across functions. Practitioners often default to reactive documentation instead of shaping upstream design.

Who this is for

Data Engineer scaling AI governance impact across teams

Who this is not for

Those looking for high-level AI policy commentary or non-technical AI ethics discussion

What you walk away with

  • Map AI Act obligations to specific data pipeline controls
  • Generate jurisdiction-aware compliance artefacts in hours not weeks
  • Lead design discussions across data, legal, and risk teams
  • Own the boundary definition between model development and regulatory evidence
  • Ship reusable templates for audit readiness across use cases

The 12 modules (with all 144 chapters)

Module 1. AI Act scope boundaries for data engineering
Identify where the AI Act applies to data infrastructure and where it does not. Learn to distinguish high-risk pipelines from general-purpose data flows using EBA guidance and real case examples.
12 chapters in this module
  1. Define high-risk AI under EU text
  2. Map data lineage to risk thresholds
  3. Classify pipeline components
  4. Document training data provenance
  5. Trace model dependencies
  6. Identify monitoring triggers
  7. Assess update frequency thresholds
  8. Determine human oversight points
  9. Apply derogations correctly
  10. Use conformity assessment templates
  11. Align with NIST AI RMF
  12. Reference certified baseline controls
Module 2. Data pipeline transparency design
Engineer auditable data flows that meet AI Act transparency obligations. Build logging, metadata tagging, and access patterns that support regulatory inspection without sacrificing performance.
12 chapters in this module
  1. Log decision-making data points
  2. Tag training data sources
  3. Version control for datasets
  4. Access audit trails
  5. Data drift monitoring
  6. Bias detection integration
  7. Performance threshold alerts
  8. Model-data lineage maps
  9. Retention policy alignment
  10. Subject access workflows
  11. Data anonymization logs
  12. Third-party data tracking
Module 3. Jurisdictional control mapping
Adapt AI Act requirements across regions where data is processed or models are deployed. Recognize overlaps and divergences with CCPA, PIPEDA, and NIS2.
12 chapters in this module
  1. Map EU AI Act to global laws
  2. Flag data transfer risks
  3. Identify dual-reporting pipelines
  4. Document GDPR crossover points
  5. Track NIS2 enforcement timelines
  6. Apply CCPA opt-out logic
  7. Assess PIPEDA equivalency
  8. Harmonize audit calendars
  9. Localize model documentation
  10. Adapt consent mechanisms
  11. Build regional playbooks
  12. Use multijurisdiction templates
Module 4. Cross-functional stakeholder alignment
Facilitate alignment between data, legal, and compliance teams using shared frameworks and precise control language.
12 chapters in this module
  1. Host AI Act requirement workshops
  2. Translate legal text to engineers
  3. Explain risk tiers clearly
  4. Define escalation paths
  5. Build cross-team RACI
  6. Align documentation standards
  7. Lead control validation sessions
  8. Facilitate evidence sharing
  9. Negotiate implementation scope
  10. Clarify ownership boundaries
  11. Document decision rationale
  12. Close feedback loops
Module 5. Automated compliance evidence generation
Design pipelines that auto-generate audit-ready outputs including data provenance reports, bias assessments, and model performance logs.
12 chapters in this module
  1. Code compliance assertions
  2. Trigger evidence on merge
  3. Generate conformity reports
  4. Embed metadata in pipelines
  5. Auto-tag high-risk outputs
  6. Build audit dashboards
  7. Schedule recurring checks
  8. Integrate with ticketing
  9. Push logs to vault
  10. Version control artefacts
  11. Sign outputs cryptographically
  12. Verify chain of custody
Module 6. High-risk system boundary definition
Precisely define where high-risk AI begins and ends in multi-component systems to avoid over-scope and under-protection.
12 chapters in this module
  1. Map data flow entry points
  2. Identify inference triggers
  3. Define model handoff points
  4. Classify output sensitivity
  5. Determine autonomy level
  6. Assess error impact potential
  7. Set confidence thresholds
  8. Evaluate fallback logic
  9. Audit override mechanisms
  10. Log boundary decisions
  11. Update control maps
  12. Validate with legal
Module 7. Human oversight integration
Design meaningful human intervention points in fully automated pipelines to meet AI Act requirements without creating bottlenecks.
12 chapters in this module
  1. Identify intervention triggers
  2. Design alert thresholds
  3. Assign oversight roles
  4. Build review interfaces
  5. Log intervention timing
  6. Measure override frequency
  7. Track escalation paths
  8. Validate training adequacy
  9. Audit decision records
  10. Balance speed and control
  11. Document rationale flow
  12. Update playbooks quarterly
Module 8. Model risk documentation at scale
Produce consistent, regulator-ready documentation for multiple models using reusable templates and automation.
12 chapters in this module
  1. Structure technical documentation
  2. Template model specs
  3. Define performance metrics
  4. Document training process
  5. List expected use cases
  6. Flag known limitations
  7. Record bias testing
  8. Archive validation results
  9. Version documentation sets
  10. Link to data sources
  11. Publish update history
  12. Support external audits
Module 9. Third-party model governance
Apply AI Act principles to externally sourced models and APIs, ensuring compliance without direct access to source code.
12 chapters in this module
  1. Assess vendor transparency
  2. Review third-party assurances
  3. Audit black-box outputs
  4. Monitor performance drift
  5. Define fallback triggers
  6. Validate input sanitization
  7. Track usage compliance
  8. Document oversight process
  9. Require conformity evidence
  10. Negotiate audit rights
  11. Enforce update protocols
  12. Maintain accountability
Module 10. Incident response for AI systems
Design detection and response workflows for AI-specific incidents including bias spikes, data poisoning, and unauthorized model access.
12 chapters in this module
  1. Define incident thresholds
  2. Log anomalous outputs
  3. Detect data drift
  4. Flag unauthorized access
  5. Activate fallback modes
  6. Escalate to oversight
  7. Document root cause
  8. Notify stakeholders
  9. Update controls
  10. Preserve evidence
  11. Report to authorities
  12. Resume operations
Module 11. Compliance testing automation
Build integrated testing suites that validate AI Act compliance during CI/CD cycles.
12 chapters in this module
  1. Write compliance unit tests
  2. Integrate with CI pipeline
  3. Check data provenance
  4. Verify logging coverage
  5. Test oversight workflows
  6. Scan for bias signals
  7. Validate input controls
  8. Audit model outputs
  9. Run boundary checks
  10. Enforce policy gates
  11. Fail unsafe merges
  12. Generate test reports
Module 12. Sustaining compliance over time
Maintain AI Act alignment as models evolve and new regulations emerge.
12 chapters in this module
  1. Track regulation changes
  2. Update control baselines
  3. Revalidate model use
  4. Refresh documentation
  5. Retrain bias detectors
  6. Audit oversight logs
  7. Update training materials
  8. Adjust human review
  9. Revise risk thresholds
  10. Notify affected parties
  11. Archive old versions
  12. Report compliance status

How this maps to your situation

  • New AI system implementation
  • Regulator audit preparation
  • Cross-border data pipeline design
  • Model risk committee reporting

Before vs. after

Before
Compliance work feels isolated, reactive, and prone to rework when shared across teams.
After
You lead the design of AI Act compliant systems that are adopted across data, legal, and risk functions with minimal friction.

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 module, designed to be completed alongside active projects.

If nothing changes
Without structured compliance design, engineering teams remain reactive, audit cycles lengthen, and cross-functional trust erodes.

How this compares to the alternatives

Public webinars give surface-level overviews. Certification prep focuses on memorization. This course delivers implementable design patterns used by teams shipping AI Act compliant systems today.

Frequently asked

Is this course technical or policy-focused?
It’s technical implementation focused , for engineers who must build compliant systems, not just interpret the law.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me influence non-engineering teams?
Yes , by grounding your work in verifiable AI Act control patterns, you gain credibility and clarity in cross-functional discussions.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active projects..

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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