A tailored course, built for your situation
Becoming the go-to AI governance advisor via AI Act compliance
Position yourself as the internal authority on AI governance by mastering the AI Act's real-world implementation demands
$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
Mid-to-senior data or AI governance practitioner at a regulated tech firm, already involved in policy design or compliance alignment, seeking increased influence and visibility through subject matter authority.
Who this is not for
Entry-level compliance staff, external auditors, or consultants without internal platform context.
What you walk away with
- Lead internal AI Act readiness assessments with confidence and clarity
- Anticipate and shape platform-level controls before they become mandates
- Establish yourself as the first reference for cross-functional AI compliance queries
- Build reusable implementation playbooks that scale across teams
- Turn governance decisions into visible, career-compounding contributions
The 12 modules (with all 144 chapters)
Module 1. AI Act fundamentals in real-world context
Understand the core obligations of the AI Act as they apply to high-risk AI systems, with emphasis on data lineage, model documentation, and human oversight requirements.
12 chapters in this module
- Scope of regulated AI systems
- High-risk use case definitions
- Obligations for deployers vs providers
- Transparency and documentation mandates
- Role of technical documentation
- Human oversight requirements
- Conformity assessment pathways
- Registration and EU database rules
- Post-deployment monitoring duties
- Liability implications for developers
- Interaction with existing data laws
- Enforcement mechanisms by member state
Module 2. Mapping AI Act to internal governance frameworks
Align AI Act requirements with existing internal data governance and MLOps practices to avoid duplication and strengthen coherence.
12 chapters in this module
- Integrating with data governance programs
- Harmonizing with MLOps pipelines
- Leveraging model cards and data sheets
- Linking to risk classification tiers
- Adapting classification for EU context
- Incorporating audit readiness
- Embedding compliance into CI/CD
- Tracking changes across versions
- Documenting model intent and drift
- Standardizing metadata for audits
- Identifying system owners internally
- Cross-team ownership models
Module 3. Classifying AI systems under Title III
Apply practical decision frameworks to determine whether a system qualifies as high-risk under Annex III of the AI Act.
12 chapters in this module
- Analyzing use case by sector
- Evaluating biometric identification risks
- Assessing safety components in products
- Determining legal effects on individuals
- Screening for essential rights impact
- Reviewing remote biometric monitoring
- Evaluating emotion recognition use
- Judging AI in law enforcement context
- Handling migration from legacy models
- Documenting classification rationale
- Updating classifications over time
- Establishing internal review board
Module 4. Data and documentation requirements
Implement traceable data practices that satisfy the AI Act’s data governance expectations for high-risk systems.
12 chapters in this module
- Ensuring data quality standards
- Documenting training data sources
- Recording data preprocessing steps
- Handling synthetic data disclosure
- Tracking data lineage end to end
- Preserving data versioning
- Managing data bias assessments
- Logging data updates and refreshes
- Aligning with DORA data rules
- Meeting interoperability needs
- Securing documentation access
- Retention periods for audit
Module 5. Technical documentation for compliance
Build comprehensive technical files that meet Article 11 and Annex V requirements for high-risk AI systems.
12 chapters in this module
- Overview of required documentation
- System purpose and intended use
- Design specifications summary
- Model architecture description
- Training methodology details
- Validation and testing approach
- Performance metrics reported
- Known limitations disclosure
- Post-deployment monitoring plan
- User instructions and guidance
- Version control procedures
- Update and retraining policies
Module 6. Human oversight mechanisms
Design effective human-in-the-loop processes that fulfill the AI Act’s oversight obligations for high-risk AI.
12 chapters in this module
- Defining meaningful control points
- Timing of human intervention
- Role clarity for operators
- Training needs for reviewers
- Escalation paths for errors
- Logging oversight decisions
- Measuring intervention rates
- Designing feedback loops
- Avoiding automation bias
- Supporting explainability needs
- Balancing speed and safety
- Auditing oversight effectiveness
Module 7. Risk management system integration
Embed AI Act compliance into existing enterprise risk frameworks and operational workflows.
12 chapters in this module
- Mapping to NIST AI RMF
- Integrating with ISO 42001
- Adapting SOC 2 controls
- Leveraging COBIT principles
- Connecting to ERM platforms
- Tracking risk remediation
- Assigning risk ownership
- Setting risk thresholds
- Reporting on risk posture
- Auditing risk decisions
- Updating risk models
- Aligning with compliance calendars
Module 8. Transparency and user information
Ensure deployed AI systems meet transparency obligations for users and affected parties.
12 chapters in this module
- Providing meaningful explanations
- Disclosing AI use clearly
- Designing understandable interfaces
- Warning about limitations
- Informing about data use
- Supporting user rights
- Enabling opt-out mechanisms
- Publishing system availability
- Managing multilingual needs
- Updating disclosures post-deploy
- Logging access and changes
- Proving compliance to auditors
Module 9. Conformity assessment and certification
Navigate the process of demonstrating compliance through internal checks or notified body involvement.
12 chapters in this module
- Choosing conformity route
- Preparing for internal audits
- Engaging notified bodies
- Scheduling assessments
- Documenting due diligence
- Handling third-party reviews
- Responding to findings
- Maintaining certificate status
- Updating assessments post-change
- Managing renewal cycles
- Leveraging existing certifications
- Reducing assessment burden
Module 10. Post-deployment monitoring and updates
Establish ongoing monitoring practices that ensure continued compliance after AI systems go live.
12 chapters in this module
- Setting performance baselines
- Tracking model drift indicators
- Logging prediction patterns
- Detecting bias shifts
- Reviewing human oversight logs
- Updating models responsibly
- Managing version rollouts
- Communicating changes to users
- Auditing update decisions
- Documenting rollback plans
- Handling incident triggers
- Reporting major changes
Module 11. Cross-border compliance coordination
Manage AI Act compliance across jurisdictions with differing enforcement priorities and interpretations.
12 chapters in this module
- Understanding member state variation
- Working with local representatives
- Coordinating with EDPB
- Aligning with UK developments
- Tracking US state-level rules
- Harmonizing with global clients
- Managing multi-jurisdiction deployments
- Handling data transfer implications
- Responding to cross-border audits
- Building legal collaboration workflows
- Updating policies globally
- Escalating conflicts appropriately
Module 12. Building internal credibility as an AI Act authority
Position yourself as the go-to advisor by creating reusable assets, leading initiatives, and shaping internal standards.
12 chapters in this module
- Identifying early internal wins
- Creating internal playbooks
- Delivering training sessions
- Influencing roadmap decisions
- Publishing guidance notes
- Leading cross-functional workshops
- Shaping policy proposals
- Establishing review boards
- Documenting contributions
- Measuring influence growth
- Gaining executive visibility
- Becoming the default reference
How this maps to your situation
- When drafting internal AI policies
- Before launching new high-risk AI features
- During regulatory audit preparation
- When integrating third-party AI tools
Before vs. after
Before
AI governance feels like a reactive checklist, scattered across teams, with no clear ownership.
After
You lead the conversation , trusted to interpret the AI Act, shape internal standards, and guide deployments confidently.
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 2, 3 hours per module, designed to be completed incrementally alongside regular work.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses specifically on AI Act requirements and their implementation within technical organizations like yours , giving you practical, actionable authority rather than theoretical knowledge.
Frequently asked
How is this different from general AI ethics training?
It’s focused exclusively on the legal and operational requirements of the AI Act, with technical implementation pathways for real systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get ahead internally?
Yes , it’s designed to make you the internal reference for AI Act compliance, increasing your visibility and influence.
$199 one-time. Approximately 2, 3 hours per module, designed to be completed incrementally alongside regular 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