A tailored course, built for your situation
Faster path from AI policy intent to working compliance artefact using AI Act
Turn regulatory requirements into deployed controls in days, not months
$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
Software Engineer working within a data and AI platform company, involved in or adjacent to AI governance implementation, seeking to reduce cycle time between regulatory input and system-level output
Who this is not for
Product managers, compliance officers, or legal generalists without hands-on implementation responsibility for AI systems
What you walk away with
- Map AI Act requirements directly to system controls in under a week
- Produce working compliance artefacts that stand up to internal and external review
- Reduce dependency on cross-functional coordination for standard compliance deliverables
- Ship first internal implementation of AI Act-aligned controls ahead of mandate
- Build a reusable template library for future regulatory responses
The 12 modules (with all 144 chapters)
Module 1. AI Act scope and applicability to engineered systems
Understand which provisions of the AI Act directly impact software architecture and deployment patterns in high-risk AI systems.
12 chapters in this module
- High-risk AI system classification
- General purpose AI obligations
- Provider vs deployer responsibilities
- Technical documentation requirements
- Conformity assessment pathways
- Market surveillance implications
- National competent authorities
- Extraterritorial effect for cloud providers
- Timeline for compliance enforcement
- Interaction with other regulations
- Sector-specific implementations
- Ongoing monitoring duties
Module 2. From legal text to technical control
Break down AI Act articles into testable, implementable software requirements using mapping heuristics.
12 chapters in this module
- Control decomposition framework
- Translating 'transparency' into logging
- Mapping fairness to model evaluation
- Accuracy requirements into testing
- Human oversight into UI design
- Data governance into pipeline checks
- Robustness into stress testing
- Cybersecurity integration points
- Versioning for auditability
- Change control thresholds
- Incident response triggers
- Monitoring for drift detection
Module 3. Designing for conformity assessment
Structure system documentation to satisfy Article 17 requirements and streamline third-party review.
12 chapters in this module
- Technical documentation outline
- System purpose specification
- Risk classification rationale
- Training data provenance
- Model architecture diagramming
- Performance metrics selection
- Bias testing methodology
- Robustness validation approach
- Security testing summary
- Use case limitations statement
- Post-deployment monitoring plan
- Update and change policy
Module 4. Building explainability into model pipelines
Implement Article 13 requirements using interpretable ML techniques and reporting structures.
12 chapters in this module
- Right to explanation scope
- Pre-decision transparency
- Post-hoc explanation methods
- Feature importance integration
- Counterfactual generation
- Local vs global explanations
- User-facing interfaces
- Confidence thresholding
- Drift-aware re-explanation
- Logging for auditability
- Versioned explanation outputs
- Accessibility requirements
Module 5. Data governance for high-risk systems
Meet data quality obligations under Article 10 with pipeline-enforced checks and documentation workflows.
12 chapters in this module
- Training data representativeness
- Bias mitigation in sourcing
- Annotation quality controls
- Data lineage capture
- Versioned dataset management
- Preprocessing audit trails
- Validation set independence
- Adversarial testing data
- Synthetic data compliance
- Data retention policies
- Anonymization standards
- Third-party data vetting
Module 6. Automated compliance testing framework
Create a repeatable suite of tests that validate AI Act adherence in CI/CD pipelines.
12 chapters in this module
- Test case derivation method
- Risk-based test prioritization
- Model card validation
- Dataset card validation
- Bias scan automation
- Robustness test integration
- Drift detection alarms
- Explainability output checks
- Security penetration tests
- Fail-safe logic verification
- Human-in-the-loop simulation
- Compliance test reporting
Module 7. Human oversight implementation
Design and deploy Article 14-compliant human review touchpoints in production systems.
12 chapters in this module
- High-risk decision types
- Review window requirements
- Operator training needs
- Override capability design
- Situational awareness tools
- Escalation pathways
- Review logging standards
- False positive tolerance
- Workload balancing
- Feedback loop integration
- Review effectiveness metrics
- Audit trail structure
Module 8. Risk management system integration
Embed ongoing risk assessment into system operations per Article 9 requirements.
12 chapters in this module
- Risk register structure
- Hazard identification process
- Risk estimation methodology
- Risk evaluation thresholds
- Mitigation effectiveness
- Residual risk documentation
- Incident tracking linkage
- Risk review frequency
- Cross-system dependencies
- Third-party risk aggregation
- Risk communication plan
- Escalation to senior management
Module 9. Cybersecurity for AI systems
Apply Article 15 security requirements to protect against AI-specific attack vectors.
12 chapters in this module
- Model poisoning prevention
- Adversarial example resistance
- Prompt injection defenses
- Model extraction protection
- Privilege access controls
- Secure model serving
- Model watermarking
- Backdoor detection
- Integrity verification
- Supply chain review
- Model repository security
- Incident response plan
Module 10. Versioning and change control
Implement Article 20 requirements for updates and transparency in deployed models.
12 chapters in this module
- Model versioning standards
- Change approval workflow
- Impact assessment process
- User notification requirements
- Deprecation policy
- Rollback capability
- Documentation update timing
- Performance regression testing
- Drift monitoring thresholds
- Security patching process
- Third-party model updates
- Patch impact communication
Module 11. Ongoing monitoring and incident reporting
Meet post-market surveillance obligations with automated monitoring and reporting protocols.
12 chapters in this module
- Performance deviation alerts
- Bias shift detection
- Usage pattern monitoring
- Incident classification
- Reporting thresholds
- Notification timelines
- Data subjects communication
- National authority reporting
- Corrective action tracking
- System suspension criteria
- Update release cadence
- Public transparency logging
Module 12. Cross-border data and system deployment
Navigate extraterritorial application of the AI Act for global system deployments.
12 chapters in this module
- Non-EU provider obligations
- Cloud hosting implications
- Data transfer mechanisms
- Subprocessor accountability
- Joint controller arrangements
- Global incident response
- Enforcement risk mapping
- National variation tracking
- Competent authority coordination
- Parallel compliance strategies
- Regulatory engagement protocol
- Future-proofing design
How this maps to your situation
- When leadership asks for first-mover status on AI Act
- Before the next internal audit cycle
- After a new high-risk use case is approved
- When onboarding third-party AI components
Before vs. after
Before
Waiting for compliance teams to interpret regulations before starting implementation
After
Turning AI Act requirements into working system controls in days
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.
How this compares to the alternatives
Unlike generic AI ethics courses, this program delivers actionable, legally-grounded implementation patterns specific to the AI Act, focused on accelerating delivery by engineering teams.
Frequently asked
Is this course suitable for engineers without legal training?
Yes. It’s designed for technical practitioners and translates legal requirements into system design choices without requiring prior legal knowledge.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover NIST AI RMF or ISO 42001 as well?
The core focus is AI Act implementation. However, mappings to NIST AI RMF and ISO 42001 are included in relevant modules.
$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