A tailored course, built for your situation
Influence in AI Governance Under the AI Act
Shape technical direction and vendor choices with authority grounded in the EU AI Act framework
$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 and analytics practitioner influencing governance, architecture, or policy in regulated or innovation-driven environments
Who this is not for
Entry-level practitioners, compliance officers without technical scope, or those not involved in cross-functional decision forums
What you walk away with
- Confidently lead AI governance discussions using AI Act structure and terminology
- Anchor technical trade-offs in verifiable regulatory intent
- Increase frequency of inclusion in strategic vendor and platform evaluations
- Produce reusable position papers that pre-frame debates
- Gain consistent traction for proposals in peer review settings
The 12 modules (with all 144 chapters)
Module 1. AI Act Structure and Strategic Intent
Break down the regulation's structure, tiered obligations, and intended impact on technical design. Focus on provisions affecting data pipelines, model transparency, and system classification.
12 chapters in this module
- Overview of AI Act scope
- Regulated vs non-regulated AI systems
- High-risk system criteria
- General purpose AI obligations
- Provider vs deployer duties
- Market surveillance roles
- Timeline for enforcement
- Alignment with existing data laws
- Interaction with NIS2
- Global extraterritorial effect
- Sector-specific annexes
- Key definitions verbatim
Module 2. Mapping AI Act to Data Architecture
Translate obligations into data pipeline requirements, including provenance, quality, bias monitoring, and versioning for compliance-ready systems.
12 chapters in this module
- Data logging for audit readiness
- Bias detection thresholds
- Training data provenance tracking
- Versioning for model lineage
- Retention policies by risk tier
- Access control mapping
- Documentation standards
- Human oversight integration
- Model performance thresholds
- Error feedback mechanisms
- Incident logging design
- Interoperability needs
Module 3. Risk Classification Workflows
Build repeatable processes to classify AI systems by risk level, incorporating stakeholder input and technical validation steps.
12 chapters in this module
- Risk tier definitions
- Checklist for high-risk triggers
- Cross-functional review process
- Evidence requirements
- Escalation paths
- Independent assessment need
- Third-party evaluation
- Documentation templates
- Internal audit alignment
- Change control process
- Vendor risk intake
- Self-declaration pitfalls
Module 4. Vendor Selection Under AI Act
Evaluate AI and data vendors through the lens of AI Act compliance, building scorecards and due diligence checklists.
12 chapters in this module
- Compliance as selection criterion
- Right to audit clauses
- Transparency obligations
- Subprocessor disclosure
- Data sovereignty alignment
- Model card requirements
- Performance benchmarking
- Incident reporting SLAs
- Termination rights
- Liability framing
- Insurance requirements
- Certification recognition
Module 5. Internal Policy Drafting Techniques
Write clear, actionable internal policies that reflect AI Act mandates while allowing technical flexibility.
12 chapters in this module
- Policy vs procedure distinction
- Tone for adoption
- Version control setup
- Approval workflows
- Training integration
- Enforcement mechanisms
- Alignment with SOC 2
- Mapping to ISO 42001
- Feedback loops
- Exception handling
- Audit trail requirements
- Cross-team rollout plan
Module 6. Peer Review Engagement Models
Structure contributions to architecture review boards and technical forums to increase influence and reduce friction.
12 chapters in this module
- Anticipating counterarguments
- Framing trade-offs clearly
- Using regulatory language
- Pre-submission alignment
- Evidence packet prep
- Stakeholder mapping
- Influence tactics
- Speaking to engineering values
- Balancing speed vs compliance
- Escalation thresholds
- Consensus building
- Follow-up process
Module 7. Model Documentation Standards
Create model cards, technical documentation, and transparency reports that meet AI Act expectations.
12 chapters in this module
- Model card components
- Intended use definition
- Performance metrics by group
- Bias mitigation results
- Training data summary
- System limitations
- Version history
- Human oversight process
- Change log format
- Third-party review access
- Update notification
- Archival requirements
Module 8. High-Risk System Controls
Implement technical and process controls for systems classified as high-risk under the regulation.
12 chapters in this module
- Accuracy benchmarks
- Robustness testing
- Security hardening
- Human-in-the-loop design
- Logging completeness
- Incident response triggers
- Fallback mechanisms
- User notification design
- Performance monitoring
- Bias retesting schedule
- Audit readiness
- Compliance sign-off
Module 9. Stakeholder Communication Frameworks
Tailor messaging for legal, engineering, and executive audiences when discussing AI Act implications.
12 chapters in this module
- Legal team alignment
- Engineering constraints
- Executive summary format
- Risk appetite framing
- Budget justification
- Project delay trade-offs
- Compliance debt
- Resource needs
- Milestone tracking
- Escalation paths
- Cross-department timeline
- Success metrics
Module 10. Audit Preparation and Response
Build documentation packages and rehearsal processes for internal and external AI Act audits.
12 chapters in this module
- Document checklist
- Chain of custody
- Interview prep
- Evidence assembly
- Finding response protocol
- Remediation tracking
- Third-party validator
- Report drafting
- Legal privilege
- Timeline management
- Cross-team coordination
- Post-audit review
Module 11. Change Management for AI Systems
Design approval workflows and impact assessments for updates to regulated AI systems.
12 chapters in this module
- Change classification
- Impact assessment template
- Stakeholder notification
- Testing requirements
- Rollback planning
- Documentation updates
- Audit trail maintenance
- User communication
- Regulatory reporting
- Version deprecation
- Knowledge transfer
- Lessons captured
Module 12. Future-Proofing and Global Alignment
Adapt AI Act implementation to evolving standards and overlapping regulations worldwide.
12 chapters in this module
- NIST AI RMF mapping
- ISO 42001 alignment
- GDPR interface
- US state laws
- UK regulatory stance
- Canada AI legislation
- Japan AI guidelines
- Singapore framework
- China regulations
- Global compliance strategy
- Standards convergence
- Long-term roadmap
How this maps to your situation
- When drafting a new internal AI policy
- Before joining an architecture review board
- During vendor evaluation for an AI tool
- After a regulatory change announcement
Before vs. after
Before
Ideas discussed but not adopted, influence limited to execution details
After
Proposals lead discussions, consistently shape technical direction and vendor choices
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 integration into real-world planning and review cycles.
If nothing changes
Continuing with ad-hoc responses risks reduced influence in key technical and strategic conversations, especially as AI governance becomes more institutionalized.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this course delivers specific, actionable framing tied directly to the AI Act’s text and implementation needs, so you can apply it immediately in peer reviews, architecture decisions, and policy shaping.
Frequently asked
Is this course technical enough for hands-on practitioners?
Yes. Every module includes implementation guidance, technical patterns, and concrete documentation examples tailored to data and analytics roles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this to influence vendor selection?
Yes. Module 4 provides scorecards and due diligence frameworks specifically for evaluating AI and data vendors under the AI Act.
$199 one-time. Approximately 3 hours per module, designed for integration into real-world planning and review cycles..
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