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Faster path from AI Act compliance intent to working implementation

$199.00
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A tailored course, built for your situation

Faster path from AI Act compliance intent to working implementation

A 199 course for practitioners leading responsible AI delivery

$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.
Spending weeks translating compliance mandates into working systems only to face rework when regulators circle back.

The situation this course is for

Teams are stuck in loops, drafting policies that don’t map to implementation, building systems that miss control thresholds, or restarting because the first version didn’t survive review. The cost isn’t just time; it’s credibility when deadlines slip and scope balloons.

Who this is for

Senior practitioner in data or AI governance, embedded in a fast-moving tech environment, accountable for delivering compliant systems on tight timelines.

Who this is not for

Those seeking high-level overviews of AI ethics or introductory policy summaries, they won’t find theory here, only execution-grade tooling.

What you walk away with

  • Complete AI Act compliance mapping in half the review time
  • Repeatable artefacts that compound across projects
  • First version approval on internal documentation
  • Sources and specific examples on hand when auditors ask follow-ups
  • Sharp, narrative-ready outputs for cross-functional leads

The 12 modules (with all 144 chapters)

Module 1. AI Act scope determination
Define what systems fall under regulated AI based on annexed use cases and risk tiers.
12 chapters in this module
  1. Identify high-risk use cases
  2. Map to Article 6 criteria
  3. Classify models by impact level
  4. Apply derogations where valid
  5. Document scope decisions
  6. Flag borderline systems
  7. Trace classification to training data
  8. Assess third-party model risk
  9. Evaluate live system coverage
  10. Update scope with new guidance
  11. Integrate with product intake
  12. Archive rationale for audit
Module 2. Technical documentation baseline
Build the foundational record set required for all high-risk AI systems.
12 chapters in this module
  1. Define system purpose clearly
  2. List input-output schema
  3. Document data lineage
  4. Record training data sources
  5. Specify model version
  6. Note hyperparameters used
  7. Capture monitoring logic
  8. Log change thresholds
  9. Archive preprocessing rules
  10. List dependencies
  11. Verify reproducibility steps
  12. Attach validation metrics
Module 3. Risk management framework integration
Embed risk classification and mitigation into development workflows.
12 chapters in this module
  1. Link to NIST AI RMF tiers
  2. Assign risk severity scores
  3. Map controls to risk level
  4. Define escalation paths
  5. Set tolerance thresholds
  6. Document mitigation tactics
  7. Integrate with incident response
  8. Align with security policy
  9. Track risk over time
  10. Update with model changes
  11. Automate flagging rules
  12. Report summary to leads
Module 4. Data governance for training sets
Ensure compliance with data quality, bias, and provenance requirements.
12 chapters in this module
  1. Verify lawful basis for data
  2. Document data cleaning steps
  3. Assess representativeness
  4. Detect demographic skews
  5. Mitigate bias in sampling
  6. Log data augmentation rules
  7. Preserve metadata
  8. Validate labeling accuracy
  9. Ensure traceability
  10. Monitor drift over time
  11. Update datasets responsibly
  12. Certify data lineage
Module 5. Transparency deliverables
Generate user-facing and regulator-ready documentation.
12 chapters in this module
  1. Write system purpose summary
  2. List intended use cases
  3. Disclose known limitations
  4. Note environmental impact
  5. Clarify human oversight
  6. Define user rights
  7. Explain decision logic
  8. Provide contact path
  9. Publish model card
  10. Update with changes
  11. Archive past versions
  12. Standardize wording
Module 6. Human oversight design
Build meaningful human-in-the-loop mechanisms that satisfy Article 14.
12 chapters in this module
  1. Identify critical decision points
  2. Set intervention thresholds
  3. Design override capability
  4. Train oversight staff
  5. Log human actions
  6. Measure intervention rate
  7. Test fallback procedures
  8. Ensure timely response
  9. Clarify responsibility
  10. Document training process
  11. Audit oversight events
  12. Update based on feedback
Module 7. Accuracy and performance validation
Establish testing protocols that meet regulatory expectations.
12 chapters in this module
  1. Define performance metrics
  2. Test across subgroups
  3. Measure edge case behavior
  4. Validate under stress
  5. Benchmark against baselines
  6. Log failure modes
  7. Assess real-world fit
  8. Track model drift
  9. Set retraining triggers
  10. Report performance decay
  11. Compare across versions
  12. Certify test integrity
Module 8. Cybersecurity for AI systems
Apply robust protections to models, data, and endpoints.
12 chapters in this module
  1. Map attack surfaces
  2. Secure model weights
  3. Protect inference API
  4. Authenticate users
  5. Encrypt data in transit
  6. Log access attempts
  7. Test adversarial robustness
  8. Detect prompt injection
  9. Limit model scraping
  10. Patch dependencies
  11. Audit for vulnerabilities
  12. Update incident plan
Module 9. Compliance testing workflow
Run internal checks that mirror notified body assessments.
12 chapters in this module
  1. Schedule pre-audit cycles
  2. Assign test owners
  3. Run control validation
  4. Simulate inspection
  5. Check documentation
  6. Verify traceability
  7. Assess consistency
  8. Fix gaps pre-submission
  9. Log test results
  10. Update checklists
  11. Report readiness
  12. Certify compliance
Module 10. Implementation playbook sequencing
Assemble proven order of actions for fastest compliance delivery.
12 chapters in this module
  1. Start with scope
  2. Draft documentation early
  3. Integrate risk checks
  4. Run bias testing
  5. Build transparency pack
  6. Set up oversight
  7. Validate performance
  8. Secure endpoints
  9. Run internal audit
  10. Finalize playbook
  11. Deliver first version
  12. Update for next
Module 11. Cross-functional alignment tactics
Align engineering, legal, product, and compliance teams efficiently.
12 chapters in this module
  1. Map stakeholder needs
  2. Define shared goals
  3. Schedule sync points
  4. Use common templates
  5. Align on definitions
  6. Resolve conflicts
  7. Document agreements
  8. Track decisions
  9. Share updates
  10. Gather feedback
  11. Improve handoffs
  12. Automate coordination
Module 12. Living compliance system
Maintain compliance continuously, not just at launch.
12 chapters in this module
  1. Monitor for changes
  2. Reassess risk level
  3. Update documentation
  4. Retest key systems
  5. Log updates
  6. Notify stakeholders
  7. Archive old versions
  8. Audit change logs
  9. Improve playbook
  10. Share best practices
  11. Train new staff
  12. Scale to more models

How this maps to your situation

  • When starting a new AI project under the AI Act
  • When updating an existing AI system
  • When preparing for regulatory audit
  • When scaling compliance across teams

Before vs. after

Before
Waiting weeks to turn compliance guidance into working systems, with multiple review cycles and rework.
After
Delivering complete, auditable implementations in half the time with first-version approval.

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 in parallel with active projects.

If nothing changes
Continuing to rebuild compliance artefacts from scratch means longer cycles, repeated reviews, and missed deadlines, eroding trust and slowing innovation.

How this compares to the alternatives

Most AI governance courses focus on principles or high-level strategy. This is the only one that delivers execution-grade templates, checklists, and a proven sequence for turning AI Act obligations into working systems, fast.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for someone at a cloud data platform company?
Yes. The course focuses on AI Act implementation patterns that apply across environments, with no reference to specific vendor tools, only framework-backed execution steps.
Will I get templates?
Yes. Every module includes a downloadable template and a worked example you can adapt immediately.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with 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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