A tailored course, built for your situation
Mastering AI Act for LLM Governance Practitioners
Build defensible, auditable AI governance frameworks with structured reasoning and source-backed decisions
$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.
Making governance decisions that get challenged in cross-functional meetings
The situation this course is for
LLM governance is moving from informal oversight to formal review. Practitioners who relied on tribal knowledge are now asked to justify choices, often without a structured framework or documented rationale. This creates friction when escalating incidents or proposing controls.
Who this is for
IC-level engineer or technical specialist managing LLM operations in a regulated or scaling environment, focused on governance, reliability, and audit readiness
Who this is not for
Executives seeking high-level AI strategy, entry-level engineers without governance exposure, or practitioners outside AI/ML infrastructure
What you walk away with
- Map AI Act requirements directly to LLM control points in deployment, monitoring, and review
- Construct defensible rationale for model access, versioning, and rollback decisions
- Document control implementations with source-backed justification for auditors and peers
- Anticipate challenges to LLM policies using precedent from EU AI Office guidance
- Deliver repeatable governance artefacts that survive team changes and leadership shifts
The 12 modules (with all 144 chapters)
Module 1. AI Act Foundation and LLM Classification
Understand how the AI Act defines high-risk AI and where LLMs fall within its scope. Identify binding obligations based on deployment context and functionality.
12 chapters in this module
- Scope of AI Act
- Definition of AI system
- LLM as general-purpose AI
- High-risk determination
- Provider vs deployer roles
- Extraterritorial effect
- Sector-specific implications
- Compliance deadlines
- Member state enforcement
- Voluntary codes of practice
- Risk tier mapping
- Exemptions for research
Module 2. Governance Structure Under Title III
Establish internal roles and responsibilities aligned with AI Act requirements, including human oversight and control delegation.
12 chapters in this module
- Obligations of providers
- Duties of deployers
- Third-party integration risks
- Human-in-the-loop definition
- Role assignment framework
- Accountability mapping
- Compliance officer mandate
- Training requirements
- Change logging
- Incident reporting chain
- Internal audit readiness
- Documentation standards
Module 3. Risk Assessment and Management
Apply AI Act’s risk categorization to LLM use cases, focusing on systemic risk, context drift, and unintended outputs.
12 chapters in this module
- Hazard identification
- Severity classification
- Likelihood estimation
- Risk register design
- Mitigation hierarchy
- Model card integration
- Output filtering
- Drift detection
- Feedback loop design
- Escalation thresholds
- Remediation protocols
- Third-party validation
Module 4. Data Governance and Provenance
Ensure training and operation data meet transparency and quality standards required by Article 10, tailored to LLM pipelines.
12 chapters in this module
- Data lineage tracking
- Bias assessment methods
- Synthetic data use
- Personal data handling
- Data set documentation
- Versioning policies
- Sourcing transparency
- Annotation standards
- Data refresh cycles
- Data drift monitoring
- Retention rules
- Audit trail design
Module 5. Technical Documentation Requirements
Build and maintain technical files that satisfy AI Act Article 11 and support regulator inquiries.
12 chapters in this module
- System overview
- Intended use definition
- Architecture diagrams
- Performance metrics
- Uncertainty estimates
- Input output specs
- Version history
- Update process
- Security measures
- Testing results
- Failure modes
- Maintenance logs
Module 6. Model Transparency and Disclosure
Implement notice and disclosure requirements for LLM outputs, especially in public-facing deployments.
12 chapters in this module
- User notification
- Deepfake labeling
- Output watermarking
- Discrimination warnings
- Language clarity
- Accessibility needs
- Multilingual support
- Consent mechanisms
- Logging user interactions
- Feedback capture
- Transparency reports
- Public register submission
Module 7. Human Oversight Controls
Design effective human review processes that meet AI Act requirements without creating operational bottlenecks.
12 chapters in this module
- Oversight timing
- Reviewer qualifications
- Escalation paths
- Intervention rights
- Logging review actions
- Automated flagging
- Risk-based sampling
- Training programs
- Performance tracking
- Bias detection
- Appeal process
- Audit readiness
Module 8. Accuracy, Robustness, Cybersecurity
Meet Article 15 requirements with measurable controls for LLM reliability and threat resistance.
12 chapters in this module
- Threat modeling
- Adversarial testing
- Model retraining
- Prompt injection defense
- Output validation
- Latency monitoring
- Fallback mechanisms
- Security patching
- Penetration testing
- Monitoring alerts
- Incident response
- Recovery procedures
Module 9. Conformity Assessment Path
Navigate the AI Act conformity process, including internal checks and third-party involvement if required.
12 chapters in this module
- Self-assessment steps
- Notified body selection
- Technical file submission
- Certification timing
- Post-market surveillance
- Change impact review
- Incident documentation
- Complaint handling
- Audit preparation
- Non-compliance response
- Withdrawal procedures
- International alignment
Module 10. Record Keeping and Audit Readiness
Generate and maintain records that satisfy AI Act Article 12 and prepare for regulator inspections.
12 chapters in this module
- Log retention duration
- Access control
- Data integrity
- Timestamping
- Immutable storage
- Audit trail design
- Query support
- Incident documentation
- Version linkage
- Automated reporting
- Retention policies
- Cross-border transfer
Module 11. Implementation Playbook Integration
Adapt the AI Act framework to real-world LLM operations with templates, examples, and checklists.
12 chapters in this module
- Policy drafting
- Control mapping
- Team onboarding
- Stakeholder review
- Version control
- Change request process
- Training delivery
- Compliance monitoring
- KPIs and metrics
- Remediation tracking
- Lessons learned
- Continuous improvement
Module 12. Defensible Governance in Practice
Use real scenarios to test defensibility of decisions, anticipate challenges, and refine reasoning.
12 chapters in this module
- Peer review simulation
- Regulator Q&A
- Board-level explanation
- Incident post-mortem
- Cross-functional debate
- External audit prep
- Public scrutiny handling
- Legal defense prep
- Lessons from enforcement
- Precedent database
- Argument structuring
- Source citation
How this maps to your situation
- Classifying own LLMs under AI Act
- Responding to internal compliance review
- Preparing for external audit
- Advocating for control changes in technical meetings
Before vs. after
Before
Making LLM governance decisions without a formal framework, relying on informal consensus or technical intuition
After
Leading with structured, source-backed reasoning that stands up in cross-functional review and audit settings
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 alongside active work commitments over 4-6 weeks.
If nothing changes
Continuing to operate without a defensible framework increases exposure to rework, escalation delays, and loss of influence when policies are challenged.
How this compares to the alternatives
Unlike generic AI ethics courses or vendor-specific tool training, this course delivers structured, jurisdictionally relevant reasoning patterns tied directly to AI Act obligations , giving you the depth to justify decisions under scrutiny.
Frequently asked
Is this course specific to EU operations?
The AI Act applies to any organization deploying or providing AI in the EU. Its risk-based approach is becoming a benchmark globally, even for non-EU teams managing exportable models.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this while working under different AI governance frameworks?
Yes. The AI Act’s structure complements NIST AI RMF and ISO 42001. You’ll learn how to map across frameworks using shared principles.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active work commitments over 4-6 weeks..
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