A tailored course, built for your situation
Reference of choice on cross-functional AI governance calls
Become the internal authority on AI Act alignment for engineering and policy teams
$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 software engineer or technical IC working at a data and AI platform company, contributing to or shaping AI governance frameworks in response to emerging regulation.
Who this is not for
Individuals seeking entry-level compliance training or non-technical overviews of AI policy.
What you walk away with
- Recognized source of truth for AI Act interpretation within engineering and cross-functional teams
- Documented decision patterns for AI risk categorization and mitigation aligned to AI Act requirements
- Templates and playbooks that accelerate internal reviews and external audits
- Credibility to lead AI governance working sessions with product, legal, and risk stakeholders
- Proven ability to translate high-level AI Act mandates into working system controls
The 12 modules (with all 144 chapters)
Module 1. AI Act scope and high-risk system classification
Understand which AI use cases fall under the AI Act’s high-risk category and how to map them to engineering boundaries.
12 chapters in this module
- Purpose of the AI Act
- Definition of an AI system under EU law
- High-risk AI use cases listed in Annex III
- General-purpose AI provisions
- Role of upstream model providers
- Obligations for deployers vs developers
- Geographic reach of the regulation
- Interaction with national laws
- Registration requirements for high-risk systems
- Timeline for compliance deadlines
- Exemptions for research and non-production use
- How enforcement actions are initiated
Module 2. Data governance for training and validation
Implement data practices that satisfy AI Act transparency and quality mandates for high-risk systems.
12 chapters in this module
- Data provenance requirements
- Bias assessment in training data
- Representativeness of data sets
- Documentation of data preprocessing
- Versioning of training data
- Human oversight in data labeling
- Data retention policies
- Right to access training data
- Audit trail for data changes
- Third-party data sourcing risks
- Geolocation data handling
- Synthetic data disclosure rules
Module 3. Technical documentation and logging
Build compliant system records that meet Article 13 requirements for design, monitoring, and traceability.
12 chapters in this module
- Minimum contents of technical documentation
- System architecture diagrams for audit
- Version control integration
- Logging of model inputs and outputs
- Model performance thresholds
- Change management for updates
- Human-in-the-loop logging
- Incident reporting workflows
- Model card integration
- System interface documentation
- Security logging for inference APIs
- Retention period for system logs
Module 4. Risk management system design
Apply a tiered approach to AI risk identification, mitigation, and escalation aligned with the AI Act.
12 chapters in this module
- Risk identification framework
- Hazard classification methodology
- Risk estimation vs risk evaluation
- Continuous risk monitoring
- Fallback plans for system failure
- User notification protocols
- Escalation paths for unresolved risks
- Third-party risk assessment
- Security threat modeling
- Bias mitigation across lifecycle
- Post-deployment risk tracking
- Risk documentation for audits
Module 5. Human oversight mechanisms
Design meaningful human review processes that fulfill Article 14 requirements for high-risk AI.
12 chapters in this module
- Definition of human oversight
- Roles for human reviewers
- Timing of intervention points
- Training for human operators
- Overrides and escalation paths
- Auditability of human decisions
- Feedback loop integration
- Oversight in automated decision chains
- Interfaces for human control
- Limits of human-in-the-loop
- Scalability of oversight design
- Oversight documentation requirements
Module 6. Accuracy, robustness, and cybersecurity
Ensure AI systems meet required levels of performance and resilience under normal and adversarial conditions.
12 chapters in this module
- Performance metrics selection
- Stress testing design
- Adversarial attack resistance
- Model drift detection
- Failure mode analysis
- Redundancy planning
- Input validation rules
- Model explainability integration
- Secure deployment pipelines
- Model integrity verification
- Monitoring under distribution shift
- Incident response for model compromise
Module 7. Transparency and user information
Meet disclosure obligations for deployers and developers under Article 13 and Article 14.
12 chapters in this module
- User notification requirements
- Nature of AI decision explanation
- Machine-readable disclosures
- Instructions for use content
- Public register submission process
- Labeling of AI-generated content
- Right to know when interacting with AI
- Clarity vs legal compliance balance
- Multilingual disclosure needs
- Accessibility standards
- Dynamic updates to disclosures
- Third-party content liability
Module 8. Conformity assessment procedures
Navigate the process of demonstrating compliance with the AI Act, whether self-assessed or notified body-reviewed.
12 chapters in this module
- Internal conformity process
- Role of quality management systems
- Testing against specifications
- Audit trail preparation
- Use of harmonized standards
- Notified body involvement
- Declaration of conformity
- Technical file assembly
- Ongoing compliance monitoring
- Post-market surveillance
- Substantial modification assessment
- Record retention obligations
Module 9. Third-party provider relationships
Manage compliance responsibilities when using or providing foundation models or AI components.
12 chapters in this module
- Obligations for model providers
- Downstream risk communication
- API documentation standards
- Model card requirements
- Terms of use for AI services
- Liability allocation in contracts
- Compliance warranties
- Model version support lifecycle
- Security update obligations
- Reseller compliance tracking
- Vendor audit rights
- Open source model compliance
Module 10. Internal governance and audit readiness
Establish cross-functional review boards and workflows to maintain ongoing AI Act compliance.
12 chapters in this module
- AI governance committee structure
- Cross-team escalation paths
- Policy ownership definitions
- Compliance monitoring cadence
- Internal audit protocols
- External auditor preparation
- Regulator engagement strategy
- Incident reporting workflows
- Training for new hires
- Lessons learned integration
- Compliance dashboard design
- Regulatory change tracking
Module 11. Integration with existing compliance frameworks
Align AI Act requirements with SOC 2, ISO 27001, GDPR, and other enterprise compliance systems.
12 chapters in this module
- Mapping AI Act to SOC 2 controls
- Overlap with ISO 27001 domains
- AI and data protection impact assessments
- GDPR and AI interaction
- NIST AI RMF integration
- CIS Controls adaptation
- Privacy by design principles
- Model risk management alignment
- Enterprise risk framework integration
- Control automation strategies
- Audit trail consolidation
- Unified compliance reporting
Module 12. Future-proofing for global AI regulation
Anticipate and adapt to emerging AI laws beyond the EU AI Act, including US, UK, and Asia-Pacific regimes.
12 chapters in this module
- UK AI regulation roadmap
- US federal AI executive order alignment
- Canadian AIDA comparison
- Japan’s JSIA guidelines
- South Korea AI Act
- China’s algorithm registration
- Singapore’s Model AI Governance Framework
- OECD AI Principles adoption
- Cross-border compliance conflicts
- Industry-specific rules in healthcare and finance
- Standards body developments
- Scenario planning for regulatory change
How this maps to your situation
- When launching a new AI product
- During internal audit prep cycles
- Before external regulator engagement
- When onboarding third-party AI vendors
Before vs. after
Before
AI Act requirements are interpreted reactively, with fragmented documentation and inconsistent cross-team alignment.
After
You lead consistent, credible responses to AI governance questions, with complete, audit-ready systems that reflect your technical authority.
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 for asynchronous learning around active engineering work.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level policy summaries, this course delivers actionable, code-adjacent compliance patterns tailored to senior engineers shaping AI systems in regulated environments.
Frequently asked
Who is this course for?
Senior engineers, technical leads, and ICs shaping AI systems in firms facing EU market exposure or global AI compliance requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover implementation for non-EU companies?
Yes. The AI Act is setting the global benchmark, and this course includes adaptation strategies for US, UK, and Asia-Pacific contexts.
$199 one-time. Approximately 3 hours per module, designed for asynchronous learning around active engineering work..
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