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Sharper AI Governance Outputs Under the AI Act

$199.00
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A tailored course, built for your situation

Sharper AI Governance Outputs Under the AI Act

Deliver more accurate, defensible, and polished governance artefacts the first time, aligned to the EU AI Act.

$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.
Avoid last-minute scrambles to fix gaps in AI compliance documentation before audits or vendor reviews.

The situation this course is for

Even skilled practitioners waste time revising AI governance outputs due to unclear thresholds, incomplete evidence trails, or misaligned risk categorisations under emerging rules like the AI Act. The cost isn’t just rework, it’s credibility when regulators or internal stakeholders push back.

Who this is for

Senior technical practitioner in data, AI, or infrastructure roles leading governance implementation without formal policy authority, focused on producing credible, auditable, and timely outputs under frameworks like the AI Act.

Who this is not for

People looking for high-level AI ethics overviews or non-technical policy summaries. This is for doers who ship real governance artefacts , risk matrices, conformity assessments, technical documentation , under regulatory scrutiny.

What you walk away with

  • Produce AI governance documentation that passes internal review with fewer revisions
  • Apply the AI Act’s risk classification criteria confidently and consistently
  • Build evidence trails that are complete and defensible from first submission
  • Structure technical documentation that meets conformity assessment requirements
  • Reduce time spent reconciling policy gaps during audit prep cycles

The 12 modules (with all 144 chapters)

Module 1. Foundations of the AI Act
Understand the AI Act’s structure, scope, and definitions , especially high-risk systems, prohibited practices, and obligations for deployers and developers.
12 chapters in this module
  1. Scope and applicability of the AI Act
  2. Definition of an AI system under EU law
  3. High-risk use case categories
  4. Prohibited AI practices
  5. Obligations by actor type
  6. Timeline for enforcement
  7. Alignment with existing digital regulations
  8. Role of notified bodies
  9. Market surveillance mechanisms
  10. Penalties and compliance thresholds
  11. Interaction with national laws
  12. Future amendments and expansion
Module 2. Risk Classification Workflows
Master consistent, justifiable risk categorisations using the AI Act’s annexed criteria and documented precedents.
12 chapters in this module
  1. Determining high-risk by function
  2. Use of safety component analysis
  3. Assessing real-time biometrics
  4. Evaluating remote identification
  5. Employment decision systems
  6. Education profiling tools
  7. Essential services access
  8. Law enforcement exceptions
  9. Documentation for borderline cases
  10. Internal appeals process design
  11. Cross-functional alignment on risk
  12. Versioning decisions over time
Module 3. Evidence Trail Design
Build comprehensive, audit-ready evidence packages aligned with AI Act requirements for transparency and accountability.
12 chapters in this module
  1. Training data provenance
  2. Data quality assurance steps
  3. Recordkeeping for logging
  4. Human oversight mechanisms
  5. Accuracy monitoring logs
  6. Bias mitigation reports
  7. Post-deployment performance metrics
  8. Change control documentation
  9. Version history tracking
  10. Incident response records
  11. Third-party component inventories
  12. Compliance self-audits
Module 4. Technical Documentation Standards
Structure technical files that meet Annex III requirements for high-risk AI systems.
12 chapters in this module
  1. System overview and purpose
  2. Intended use documentation
  3. Risk management system design
  4. Data pipeline descriptions
  5. Model architecture summary
  6. Performance metrics reported
  7. Robustness testing results
  8. Cybersecurity protections
  9. Human-in-the-loop design
  10. User information requirements
  11. Conformity assessment checklist
  12. Living documentation updates
Module 5. Conformity Assessment Prep
Align internal processes with formal conformity routes and notified body expectations.
12 chapters in this module
  1. Internal vs external conformity paths
  2. Choosing a notified body
  3. Application package contents
  4. Pre-audit gap analysis
  5. Corrective action logs
  6. Audit timeline coordination
  7. Representative sample selection
  8. Documentation formatting rules
  9. Compliance demonstration plans
  10. Post-assessment monitoring
  11. Handling non-conformities
  12. Recertification cycles
Module 6. Governance Workflow Integration
Embed AI Act compliance checks into existing development and deployment pipelines.
12 chapters in this module
  1. Pre-commit code reviews
  2. Model registry requirements
  3. CI/CD pipeline gates
  4. Monitoring integration points
  5. Incident escalation triggers
  6. Change approval workflows
  7. Access control aligning
  8. Model version tracking
  9. Retraining thresholds
  10. Deployment freeze rules
  11. Decommissioning logs
  12. Cross-team handover protocols
Module 7. Transparency Obligations
Meet mandatory disclosure and user-facing information standards under the AI Act.
12 chapters in this module
  1. User notification requirements
  2. Terms of use updates
  3. System capability disclosures
  4. Limitations documentation
  5. Human oversight information
  6. Contact point publication
  7. Log retention notices
  8. Marketing claims review process
  9. Public register entries
  10. API documentation rules
  11. Open source attribution
  12. License compatibility checks
Module 8. Vendor Oversight Strategy
Manage third-party AI components and services under AI Act due diligence rules.
12 chapters in this module
  1. Vendor risk assessment
  2. Contractual compliance clauses
  3. Substantial modification analysis
  4. Due diligence checklists
  5. Audit rights negotiation
  6. Liability allocation
  7. Component provenance tracking
  8. Open source compliance
  9. Cloud provider SLAs
  10. Incident notification terms
  11. Exit strategy planning
  12. Vendor offboarding
Module 9. Incident Response Protocols
Design response procedures for AI incidents that meet regulatory reporting timelines.
12 chapters in this module
  1. Incident definition and scope
  2. Detection and triage
  3. Escalation paths
  4. Root cause analysis
  5. Systemic failure logging
  6. User impact assessment
  7. Notification timelines
  8. Public authority reporting
  9. Corrective action timelines
  10. System updates and patches
  11. Stakeholder communication
  12. Post-mortem documentation
Module 10. Internal Audit Enablement
Equip internal audit teams with precise checklists and sampling methods tied to the AI Act.
12 chapters in this module
  1. Sampling for compliance validation
  2. Control testing frequency
  3. Evidence sufficiency standards
  4. Audit scope definition
  5. Finding severity tiers
  6. Remediation tracking
  7. Audit trail completeness
  8. Cross-functional feedback
  9. Reporting templates
  10. Remediation progress
  11. Executive summaries
  12. Follow-up testing
Module 11. Continuous Compliance Design
Build systems that maintain compliance as models evolve in production.
12 chapters in this module
  1. Model drift monitoring
  2. Accuracy threshold alerts
  3. Retraining triggers
  4. Human review cadence
  5. Performance degradation logs
  6. Version rollback criteria
  7. Feedback loop integration
  8. Bias recurrence checks
  9. Security patch response
  10. Log integrity maintenance
  11. Change approval tracking
  12. System decommissioning
Module 12. Defensible Positioning for Audits
Craft compelling narratives and supporting evidence that withstand regulator scrutiny.
12 chapters in this module
  1. Regulator communication style
  2. Timeline consistency
  3. Evidence organization
  4. Risk rationale documentation
  5. Precedent references
  6. Legal defensibility
  7. Stakeholder alignment
  8. Gap justification
  9. Mitigation plans
  10. Follow-up responsiveness
  11. Public statements
  12. Lessons learned reporting

How this maps to your situation

  • Preparing for EU market entry with a new AI product
  • Responding to internal audit findings on AI systems
  • Onboarding third-party AI models under compliance review
  • Updating governance documentation ahead of vendor assessment

Before vs. after

Before
Governance outputs require multiple review cycles, often missing key evidence or misclassifying risk under the AI Act.
After
Produce accurate, complete, and polished AI Act compliance artefacts the first time , ready for audit or escalation.

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 90 minutes per module , designed to be completed in parallel with ongoing work over 6, 8 weeks.

If nothing changes
Continuing to produce AI governance documentation without structured alignment to the AI Act increases the likelihood of delays in product launch, regulatory scrutiny, and reputational exposure due to non-compliant systems.

How this compares to the alternatives

Generic AI ethics courses offer broad principles but lack actionable structure. This course delivers precise, enforceable outputs aligned to the AI Act , with templates and examples built for technical practitioners implementing real governance.

Frequently asked

Is this course technical or policy-focused?
It’s designed for technical practitioners who lead implementation. You’ll gain hands-on clarity on how to structure documentation and evidence , not abstract theory.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover other frameworks like ISO 42001 or NIST AI RMF?
The focus is the AI Act. However, many concepts align and can be extended, but the course does not teach those frameworks directly.
$199 one-time. Approximately 90 minutes per module , designed to be completed in parallel with ongoing work over 6, 8 weeks..

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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