A tailored course, built for your situation
Mastering AI Act for Senior Data Governance Practitioners
Turn compliance complexity into strategic influence with a structured, implementable approach 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.
Technical AI work stays invisible to leadership despite its strategic importance.
Who this is for
Senior IC-level data governance practitioner at a cloud-scale AI platform company, responsible for aligning technical execution with regulatory expectations.
Who this is not for
Junior compliance staff, external auditors, or professionals without hands-on involvement in AI system design or governance implementation.
What you walk away with
- Map AI Act requirements directly to data and model architecture decisions
- Produce executive-ready summaries that translate technical controls into business risk language
- Build a defensible compliance package for high-risk AI systems under Article 9
- Anticipate auditor questions with pre-documented design choices and rationale
- Establish a repeatable process for AI system conformity assessments
The 12 modules (with all 144 chapters)
Module 1. Understanding the AI Act's Risk-Based Framework
Break down the EU AI Act’s structure by risk tier, focusing on obligations for high-risk systems relevant to data platforms and MLOps workflows.
12 chapters in this module
- Scope of the AI Act
- Definition of AI System
- High-Risk Use Cases
- Prohibited Practices
- General Purpose AI Provisions
- Obligations for Providers
- Role of Notified Bodies
- Market Surveillance
- Penalties and Enforcement
- Timeline for Implementation
- Interaction with GDPR
- Territorial Application
Module 2. Classifying AI Systems Under the AI Act
Learn how to categorize machine learning models and data pipelines according to the regulation's risk levels and determine which rules apply.
12 chapters in this module
- Criteria for High-Risk
- Self-Declared vs Audited Systems
- Model Card Alignment
- System Documentation Needs
- Change Triggers Reassessment
- Versioning and Updates
- Third-Party Integration Risks
- Open Source Considerations
- Foundation Model Nuances
- Incident Reporting Thresholds
- User Transparency Rules
- Provider Liability Boundaries
Module 3. Data Governance Requirements for High-Risk AI
Map data quality, provenance, and lineage practices to Article 10 obligations for training, validation, and testing data sets.
12 chapters in this module
- Data Quality Standards
- Bias Assessment Methods
- Representativeness Checks
- Documentation Templates
- Version Control for Datasets
- Annotated Data Needs
- Monitoring Post-Deployment
- Data Lineage Tools
- Retention Period Rules
- Synthetic Data Use Cases
- Human Oversight Points
- Audit Trail Requirements
Module 4. Technical Documentation and Record Keeping
Build compliant technical files that meet Annex IV requirements, balancing thoroughness with maintainability.
12 chapters in this module
- Purpose and Scope
- System Architecture
- Data Pipeline Diagrams
- Model Selection Rationale
- Performance Metrics
- Robustness Testing Results
- Accuracy Benchmarks
- Resilience Assessments
- Cybersecurity Controls
- Version History Logs
- Update Procedures
- Decommissioning Plan
Module 5. Transparency and User Information
Design clear, accessible information for deployers and end-users of AI systems in line with Article 13 and Annex V.
12 chapters in this module
- Summary for Deployers
- End-User Notices
- Right to Explanation
- Language Requirements
- Accessibility Standards
- Change Disclosure
- Performance Limitations
- Usage Restrictions
- Contact Information
- Incident Reporting Channels
- Marketing Claims Alignment
- Disclaimers and Warnings
Module 6. Conformity Assessment Procedures
Walk through internal and notified body-led assessment paths, preparing for both self-certification and third-party audits.
12 chapters in this module
- Internal Review Steps
- Checklist Development
- Gap Analysis Method
- Audit Preparation
- Notified Body Engagement
- Certificate Maintenance
- Post-Market Monitoring
- Incident Investigation
- Corrective Action Plans
- Renewal Process
- Substantial Modification Rules
- Documentation Updates
Module 7. Fundamental Rights Impact Assessment
Integrate human rights analysis into AI development cycles, meeting Article 9 criteria for high-risk system deployment.
12 chapters in this module
- Scope Definition
- Stakeholder Mapping
- Bias Risk Scenarios
- Disproportionate Impact Checks
- Remediation Strategies
- Consultation Protocols
- Documentation Format
- Oversight Mechanisms
- Public Access Rules
- Review Frequency
- Integration with DPIA
- Legal Basis Alignment
Module 8. Oversight and Human-in-the-Loop Design
Implement effective human oversight mechanisms that satisfy Article 14 requirements for deployer control.
12 chapters in this module
- Role Definition
- Training for Supervisors
- Intervention Points
- Alert Thresholds
- Escalation Paths
- Decision Logging
- Feedback Loops
- Error Correction Process
- Monitoring Frequency
- Performance Feedback
- Override Procedures
- Accountability Chains
Module 9. Robustness, Accuracy, and Cybersecurity
Apply measurable standards for system resilience and security in compliance with Article 15 and Annex III.
12 chapters in this module
- Threat Modeling
- Penetration Testing
- Adversarial Attacks
- Model Drift Detection
- Input Validation
- Fail-Safe Modes
- Accuracy Thresholds
- Reliability Metrics
- Security Certification
- Incident Response
- Logging Standards
- Update Integrity
Module 10. Vendor and Supply Chain Compliance
Manage third-party AI components and services under due diligence obligations from Article 28.
12 chapters in this module
- Contractual Clauses
- Subsidiary Accountability
- Due Diligence Checks
- Audit Rights
- LIability Allocation
- Performance Guarantees
- Transparency Demands
- Compliance Verification
- Change Notification
- Exit Strategies
- Insurance Requirements
- Dispute Resolution
Module 11. Organizational Readiness and Internal Playbooks
Develop company-specific processes and training to institutionalize AI Act compliance across teams.
12 chapters in this module
- Policy Drafting
- Training Programs
- Role Assignments
- Cross-Team Coordination
- Compliance Monitoring
- Internal Audits
- Incident Reporting
- Lessons Learned
- Playbook Distribution
- Version Control
- Leadership Engagement
- Culture Building
Module 12. Future-Proofing for Global AI Regulation
Anticipate how the AI Act shapes emerging frameworks in the US, UK, and beyond, positioning your organization ahead of curve.
12 chapters in this module
- NIST AI RMF Alignment
- US Executive Order Tracking
- UK Approach Comparison
- Canada AIDA Mapping
- Japan’s Guidelines
- Singapore Model
- China’s Rules
- Global Convergence Points
- Divergence Warnings
- Standards Development
- Industry Coalition Input
- Strategic Roadmapping
How this maps to your situation
- Preparing for AI Act audits
- Designing compliant ML pipelines
- Responding to executive inquiries
- Leading internal governance initiatives
Before vs. after
Before
Technical AI governance work remains undocumented or buried in low-visibility artefacts.
After
Strategic compliance outputs are consistently produced and recognized by 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 6 hours of reading and implementation work, designed to fit alongside active projects.
If nothing changes
Without structured compliance practices, even advanced AI systems face operational delays, reputational exposure, or blocked deployments when regulators or executives demand accountability.
How this compares to the alternatives
Unlike generic compliance overviews or vendor-specific training, this course delivers a targeted, implementable framework for navigating the AI Act as a senior practitioner , not a checklist, but a strategic toolset grounded in real system design.
Frequently asked
Is this course relevant if I don’t operate in the EU?
Yes , the AI Act is shaping global expectations for AI governance, much like GDPR did for privacy. Understanding it positions you as a leader even outside EU jurisdictions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover technical implementation?
Yes , every module connects regulatory requirements to concrete technical and architectural decisions in data and ML systems.
$199 one-time. Approximately 6 hours of reading and implementation work, designed to fit 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