A tailored course, built for your situation
Mastering AI Act for Senior Data Governance Practitioners
Operationalize EU AI Act compliance with precision and strategic control
$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.
Who this is for
Senior data governance practitioners in cloud-native environments leading compliance integration for AI and data systems
Who this is not for
Junior compliance staff, auditors without technical implementation roles, or general legal advisors without hands-on system design experience
What you walk away with
- Map AI Act high-risk use cases directly to technical controls in data workflows
- Produce regulator-ready technical documentation for AI system conformity
- Lead cross-functional AI governance reviews with documented authority
- Differentiate your advisory capacity in high-stakes platform decisions
- Accelerate internal approvals by aligning AI Act requirements with existing data governance frameworks
The 12 modules (with all 144 chapters)
Module 1. AI Act Scope and High-Risk System Identification
Understand the EU AI Act’s scope, identify high-risk AI systems in data pipelines, and classify them according to annexed criteria with practical decision trees.
12 chapters in this module
- Defining AI under the AI Act
- Regulated vs unregulated AI use cases
- High-risk classification framework
- System boundary definition
- Integration with data lineage
- Dynamic reclassification triggers
- Exemptions and derogations
- Vendor-hosted model considerations
- Internal vs external deployment impact
- Threshold for real-time biometrics
- Automated scoring in HR and credit
- Critical infrastructure dependencies
Module 2. Governance Readiness Assessment
Conduct a compliance gap assessment tailored to data platform environments, identifying current capabilities and priority actions for AI Act alignment.
12 chapters in this module
- Baseline governance maturity
- Data provenance requirements
- Model documentation standards
- Human oversight thresholds
- Risk management system checks
- Transparency obligations
- Version control expectations
- Incident logging integration
- Third-party model oversight
- Internal audit readiness
- Cross-team alignment points
- Regulatory correspondence planning
Module 3. Technical Documentation Framework
Build comprehensive technical documentation for AI systems that satisfies Article 11 and integrates with engineering workflows.
12 chapters in this module
- Purpose and audience definition
- System architecture diagrams
- Data training provenance
- Preprocessing logic disclosure
- Model validation methods
- Performance metrics selection
- Bias and fairness testing
- Security robustness checks
- Versioning and updates
- Lifecycle management plan
- Conformity assessment sign-off
- Documentation maintenance rhythm
Module 4. Risk Management System Design
Implement a risk-based approach to AI system development and deployment that meets AI Act Articles 9 and 10 requirements.
12 chapters in this module
- Hazard identification process
- Risk estimation methodology
- Risk acceptability thresholds
- Mitigation control selection
- Residual risk evaluation
- Operational monitoring design
- Fail-safe mechanisms
- Human-in-the-loop requirements
- Adverse event documentation
- Risk register maintenance
- Audit trail integration
- Incident escalation protocol
Module 5. Data Governance for Training Sets
Ensure training, validation, and testing data meet AI Act data quality and bias mitigation standards.
12 chapters in this module
- Data suitability assessment
- Bias detection in training sets
- Representativeness checks
- Data collection documentation
- Data cleaning traceability
- Annotation quality controls
- Sensitive attribute handling
- Data refresh protocols
- Synthetic data governance
- Open-source data compliance
- Data version tracking
- Data split integrity
Module 6. Transparency and User Information
Design user-facing information and system documentation that comply with transparency obligations.
12 chapters in this module
- End-user notification design
- Purpose limitation disclosure
- Human oversight disclosure
- Interaction logging notice
- Biometric data alerts
- Automated decision explanation
- System capability documentation
- Limitation disclaimers
- Support contact pathways
- Updates to user information
- Multilingual requirements
- Accessibility standards
Module 7. Human Oversight Implementation
Integrate effective human oversight mechanisms into high-risk AI systems as required by Article 14.
12 chapters in this module
- Oversight role definition
- Training for human reviewers
- Intervention capability design
- Decision override mechanisms
- Monitoring workload balance
- Oversight logging
- Escalation paths
- Performance feedback loop
- Error detection triggers
- Review frequency planning
- Oversight effectiveness metrics
- Oversight documentation
Module 8. Robustness and Accuracy Testing
Validate AI system performance under normal and extreme conditions to ensure reliability.
12 chapters in this module
- Performance benchmark definition
- Stress testing design
- Adversarial attack resistance
- Edge case identification
- Drift detection protocols
- Model updating rules
- Fallback mechanism testing
- Accuracy across demographics
- Security penetration tests
- Resilience to data corruption
- Model revalidation triggers
- Testing environment fidelity
Module 9. Conformity Assessment Preparation
Prepare for internal or notified body conformity assessments using structured evidence packages.
12 chapters in this module
- Assessment route selection
- Internal audit checklist
- Evidence collection plan
- Documentation packaging
- Internal review cycle
- Notified body engagement
- Stage gate approvals
- Gap remediation tracking
- Audit communication protocol
- Findings resolution workflow
- Continuous monitoring plan
- Post-deployment verification
Module 10. Record-Keeping and Logging
Implement audit-compliant logging and record-keeping systems for ongoing AI system monitoring.
12 chapters in this module
- Log scope definition
- Event categorization
- Retention period alignment
- Access control rules
- Immutable logging design
- Incident logging format
- Audit trail integration
- Log validation methods
- Export and inspection readiness
- Chain of custody
- Log review frequency
- Security logging integration
Module 11. AI Governance Integration with Data Platforms
Align AI Act implementation with existing data governance and platform architecture.
12 chapters in this module
- Unity Catalog adjacent controls
- Data lineage for AI systems
- Model registry integration
- Access policy alignment
- Cross-platform visibility
- Metadata tagging strategy
- Policy enforcement points
- Automated compliance checks
- Platform-native documentation
- Governance workflow triggers
- Cross-team handoff design
- Change management integration
Module 12. Scaling AI Governance Across Teams
Operationalize governance practices across multiple development and data teams.
12 chapters in this module
- Governance enablement model
- Centralized vs embedded roles
- Training program design
- Template library creation
- Peer review framework
- Maturity assessment rollout
- Feedback loop integration
- Toolchain standardization
- Cross-domain alignment
- Reporting cadence
- Lessons learned capture
- Future amendment readiness
How this maps to your situation
- High-risk AI system identification in cloud data workflows
- Internal governance readiness for AI Act compliance
- Technical documentation for regulatory review
- Cross-functional AI governance leadership
Before vs. after
Before
Reactive engagement with AI governance, relying on ad hoc processes and fragmented documentation
After
Proactive leadership in AI Act implementation, delivering structured, regulator-ready artefacts on demand
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, with self-paced access and bookmarking across devices.
How this compares to the alternatives
Unlike broad AI ethics courses or high-level compliance summaries, this course delivers precise, implementable guidance aligned with the EU AI Act’s technical requirements , tailored for practitioners who must deliver auditable outcomes in data-intensive environments.
Frequently asked
Is this course focused on legal interpretation or technical implementation?
The course emphasizes technical implementation , turning legal requirements into actionable system designs, documentation, and governance workflows.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover other frameworks like NIST AI RMF or ISO 42001?
The primary focus is the EU AI Act, but adjacent concepts from NIST AI RMF and ISO 42001 are referenced where they support implementation clarity.
$199 one-time. Approximately 3 hours per module, with self-paced access and bookmarking across devices..
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