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AIG9021 Mastering AI Act Compliance for Senior AI Product Leaders

$199.00
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A tailored course, built for your situation

Mastering AI Act Compliance for Senior AI Product Leaders

Deliver defensible, auditable AI governance from first deployment

$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 justify AI system decisions under regulatory review

The situation this course is for

Teams are shipping AI faster than governance frameworks can catch up. Regulators now expect evidence of compliant design upfront, not retrofitted justification. Many practitioners still rely on ad-hoc documentation, leading to rework, weakened credibility, and exposure during audits.

Who this is for

Senior AI/ML product leaders in regulated environments who must balance innovation velocity with compliance rigor

Who this is not for

Junior compliance staff, generalist data scientists without governance exposure, or vendors selling turnkey 'AI compliance' tooling

What you walk away with

  • Produce complete AI Act conformity assessments in half the review cycles
  • Generate auditable system documentation that clears first-pass review
  • Map technical controls directly to Article-level requirements without gaps
  • Defend design choices using regulator-tested reasoning patterns
  • Reduce revision loops by shipping polished outputs from the first draft

The 12 modules (with all 144 chapters)

Module 1. AI Act Scope and Applicability Mapping
Determine which AI systems in your portfolio fall under high-risk classification and require full conformity assessments.
12 chapters in this module
  1. Identifying high-risk use cases under Annex III
  2. Assessing system autonomy level
  3. Evaluating real-world impact thresholds
  4. Vendor-provided AI vs in-house development
  5. Determining provider vs deployer status
  6. Handling open-source model integration
  7. Mapping data flows for transparency
  8. Conducting initial risk categorization
  9. Documenting system purpose and context
  10. Versioning AI system definitions
  11. Tracking changes across model iterations
  12. Establishing boundary criteria for updates
Module 2. Risk Management System Design
Build a living risk management framework aligned with Article 9 requirements for high-risk AI systems.
12 chapters in this module
  1. Threat modelling for AI inference
  2. Hazard identification methodology
  3. Severity and likelihood scoring
  4. Failure mode tracking system
  5. Risk treatment hierarchy
  6. Residual risk acceptance process
  7. Human oversight integration
  8. Adversarial testing planning
  9. Bias impact benchmarks
  10. Robustness validation protocol
  11. Data drift detection thresholds
  12. Incident escalation paths
Module 3. Data Governance for Training and Evaluation
Implement compliant data practices for training, validation, and monitoring datasets.
12 chapters in this module
  1. Data provenance tracking
  2. Bias audit trail creation
  3. Representativeness scoring
  4. Annotation quality controls
  5. Sensitive attribute handling
  6. Preprocessing documentation
  7. Synthetic data justification
  8. Data versioning standards
  9. Label correction protocols
  10. Evaluation set isolation
  11. Drift monitoring setup
  12. Data retention policies
Module 4. Technical Documentation Structure
Create comprehensive technical files that satisfy Article 11 and notified body expectations.
12 chapters in this module
  1. System overview drafting
  2. Intended purpose specification
  3. Architecture diagrams standardization
  4. Model selection rationale
  5. Development lifecycle description
  6. Version control integration
  7. Performance metrics selection
  8. Accuracy benchmarking method
  9. Uncertainty quantification
  10. Error analysis reporting
  11. Firmware compatibility notes
  12. Update impact assessment
Module 5. Transparency and User Information
Design effective disclosure mechanisms that meet user-facing obligations under Article 13.
12 chapters in this module
  1. User notification timing
  2. System capability description
  3. Interaction logging disclosure
  4. Human-in-the-loop expectations
  5. Performance limitation warnings
  6. Language clarity standards
  7. Multilingual support planning
  8. Accessibility compliance
  9. Consent mechanism design
  10. Change communication protocol
  11. Downtime notification process
  12. Feedback channel integration
Module 6. Human Oversight Mechanisms
Implement meaningful human oversight in line with Article 14 requirements.
12 chapters in this module
  1. Oversight point identification
  2. Intervention capability design
  3. Role assignment framework
  4. Training content development
  5. Monitoring interface specs
  6. Override logging system
  7. Effectiveness measurement
  8. Workload balancing rules
  9. Escalation threshold setting
  10. Fallback procedure documentation
  11. Decision audit trail creation
  12. Oversight performance review
Module 7. Accuracy, Robustness, and Cybersecurity
Meet performance and security expectations for high-risk AI systems under Article 15.
12 chapters in this module
  1. Adversarial attack testing
  2. Input perturbation tolerance
  3. Model inversion resistance
  4. Secure deployment configuration
  5. Access control enforcement
  6. Model integrity verification
  7. Inference time monitoring
  8. Logging and alerting setup
  9. Fail-safe behavior design
  10. Resource exhaustion protection
  11. Model denial-of-service mitigation
  12. Penetration testing coordination
Module 8. Conformity Assessment Pathways
Navigate self-certification vs notified body review options under Article 43.
12 chapters in this module
  1. Internal audit planning
  2. Gap analysis execution
  3. Evidence collection strategy
  4. Notified body selection
  5. Application submission process
  6. Technical file packaging
  7. Review cycle anticipation
  8. Non-compliance response
  9. Certification renewal timing
  10. Surveillance audit preparation
  11. Change notification protocol
  12. Voluntary withdrawal process
Module 9. Recordkeeping and Audit Trails
Establish durable, inspectable records that satisfy Article 60 requirements.
12 chapters in this module
  1. Log retention period setting
  2. Storage format standardization
  3. Tamper-proofing methods
  4. Access control logging
  5. System modification tracking
  6. Decision rationale documentation
  7. Version history maintenance
  8. Incident response recording
  9. Third-party access logging
  10. Chain-of-custody procedures
  11. Legal hold readiness
  12. Export and portability support
Module 10. Post-Market Monitoring Systems
Implement ongoing surveillance for AI systems after deployment.
12 chapters in this module
  1. Performance degradation tracking
  2. User feedback aggregation
  3. Incident detection rules
  4. Anomaly alert thresholds
  5. Model drift measurement
  6. Retraining triggers
  7. Version recall protocol
  8. Customer communication plan
  9. Field performance reporting
  10. Bug and vulnerability tracking
  11. Patch deployment coordination
  12. Decommissioning process
Module 11. Provider Obligations and Liability Frameworks
Understand legal responsibilities under Titles IV and VI of the AI Act.
12 chapters in this module
  1. Liability for system harm
  2. Fault-based claims process
  3. Burden of proof allocation
  4. Insurance requirements
  5. Contractual obligation mapping
  6. Indemnification clauses
  7. Warranty terms definition
  8. Jurisdiction selection
  9. Dispute resolution mechanism
  10. Cross-border compliance
  11. Enforcement authority interaction
  12. Penalty mitigation strategy
Module 12. Implementation Playbook Integration
Operationalize learning into team workflows and governance processes.
12 chapters in this module
  1. Team training rollout
  2. Internal audit adaptation
  3. Checklist integration
  4. Toolchain alignment
  5. Cross-functional handoff
  6. Stakeholder communication
  7. Feedback loop design
  8. Continuous improvement cycle
  9. Benchmarking performance
  10. Lessons learned documentation
  11. Scaling to other use cases
  12. Leadership reporting format

How this maps to your situation

  • Preparing for initial AI Act audit
  • Launching high-risk AI system in EU market
  • Responding to regulatory inquiry
  • Building internal AI governance framework

Before vs. after

Before
Governance documentation prepared reactively, often incomplete or requiring multiple revisions during audits.
After
Proactively generated, regulator-ready outputs that clear first-pass review with minimal feedback.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • 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 3-4 hours per week over 12 weeks, with modular access for on-demand learning.

If nothing changes
Without polished, defensible documentation, even well-designed AI systems face delays, increased scrutiny, and potential non-compliance findings during audits.

How this compares to the alternatives

Unlike generic AI ethics courses or broad compliance overviews, this program delivers actionable, article-specific implementation guidance used in actual AI Act assessments, designed for practitioners shipping regulated AI systems today.

Frequently asked

Is this course focused on EU regulation only?
The primary framework is the EU AI Act, but the documentation standards and control patterns apply globally to high-risk AI governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal audits as well?
Yes, many modules map directly to internal audit expectations, particularly around documentation, risk management, and oversight.
$199 one-time. Approximately 3-4 hours per week over 12 weeks, with modular access for on-demand learning..

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