A tailored course, built for your situation
Direct Sign Off Authority on AI Act Compliance Controls
Own the final decision on which controls apply, how they’re implemented, and when they’re closed, no escalations needed.
$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.
Stuck in approval loops on AI compliance decisions
The situation this course is for
High-performing practitioners are expected to lead AI Act compliance, but still need permission to close issues or adjust control boundaries, creating friction, delays, and diluted ownership.
Who this is for
Senior compliance or governance practitioner contributing to AI risk or policy, with technical fluency and cross-functional influence
Who this is not for
Entry-level analysts, pure engineering contributors without policy scope, or executives seeking board-level summaries
What you walk away with
- Authority to classify AI systems under Title III of the AI Act without legal or compliance review
- Ownership of the technical documentation checklist and enforcement timeline
- Final determination on whether a model update triggers a new conformity assessment
- Control over exception requests and remediation deadlines for dev teams
- Autonomous closure of audit findings tied to transparency and data provenance requirements
The 12 modules (with all 144 chapters)
Module 1. Mapping AI Act Titles to Operational Ownership
Learn how each section of the AI Act translates into discrete decision rights for practitioners. Focus on Title III for high-risk AI systems and who owns classification calls.
12 chapters in this module
- Overview of AI Act structure
- High-risk vs general-purpose AI
- Classification decision criteria
- Internal appeals process
- Mapping to Databricks use cases
- Timeline for reassessment
- Cross-border implications
- Interaction with NIST AI RMF
- Documentation burden bands
- Determining update triggers
- Vendor-included models
- Internal challenge protocols
Module 2. Defining the Technical Documentation Package
Build authority over the required components of technical documentation, including what can be deferred and what constitutes completeness.
12 chapters in this module
- Mandatory elements summary
- System purpose description
- Architecture diagrams required
- Training data provenance
- Pre-deployment testing logs
- Performance benchmarks
- Risk management documentation
- Human oversight measures
- Post-deployment monitoring
- Version control details
- Compliance sign-off checklist
- Exemptions for research use
Module 3. Control Ownership for High-Risk Models
Establish your role as the final approver for high-risk model deployment, including boundary-setting and exception rules.
12 chapters in this module
- Identifying high-risk functions
- Model registry tagging rules
- Third-party dependency checks
- Data lineage requirements
- Bias mitigation documentation
- Accuracy thresholds
- Robustness testing protocol
- Fallback plans
- User information standards
- API exposure levels
- Change impact assessment
- Decommission criteria
Module 4. Deciding on Conformity Assessment Triggers
Gain confidence in determining when a model change requires a new conformity assessment.
12 chapters in this module
- Scope of substantive change
- Thresholds for retesting
- Minor update classification
- Training data shift rules
- Architecture modifications
- Use case expansion
- Performance drift limits
- Feedback loop changes
- Monitoring adjustments
- Versioning policy
- Rollback obligations
- Documentation updates
Module 5. Managing Model Exceptions and Waivers
Lead the process for granting temporary exceptions to compliance requirements with clear accountability.
12 chapters in this module
- Exception request workflow
- Time-bound approvals
- Escalation criteria
- Risk acceptance thresholds
- Internal audit notification
- Stakeholder alignment
- Remediation deadlines
- Progress tracking
- Reapproval requirements
- Waiver denial protocol
- Legal exposure limits
- Leadership notification
Module 6. Oversight of Post-Deployment Monitoring
Own the design and enforcement of monitoring plans for live high-risk AI systems.
12 chapters in this module
- Monitoring frequency bands
- Performance degradation alerts
- User feedback integration
- Incident logging
- Drift detection methods
- Model retraining triggers
- Human-in-the-loop thresholds
- Anomaly response protocol
- Reporting cadence
- Dashboard standardization
- Cross-team access rules
- Audit trail retention
Module 7. Handling AI Incident Reporting
Make binding decisions on whether incidents meet AI Act reporting thresholds and coordinate disclosure.
12 chapters in this module
- Defining reportable incident
- Harm assessment criteria
- Near-miss classification
- Stakeholder notification list
- Regulator reporting window
- Internal investigation protocol
- Root cause documentation
- Remediation tracking
- Legal privilege boundaries
- Public communication rules
- Lessons learned archive
- Process update mandate
Module 8. Building Cross-Functional Accountability
Institutionalize your authority over AI Act controls across engineering, legal, and product teams.
12 chapters in this module
- RACI for compliance tasks
- Engineering handoff checklist
- Legal consultation boundaries
- Product roadmap integration
- Change advisory board role
- SLO alignment
- Documentation ownership
- Audit participation
- Training requirements
- On-call responsibilities
- Compliance champion network
- Escalation playbook
Module 9. Leading Internal AI Audits
Own the audit process for AI Act compliance, including scope, findings, and closure.
12 chapters in this module
- Audit frequency rules
- Scope definition
- Evidence collection
- Interview protocols
- Finding severity levels
- Remediation tracking
- Closure criteria
- Executive reporting
- External auditor prep
- Follow-up cadence
- Audit tool integration
- Compliance scorecard
Module 10. Vendor and Third-Party AI Oversight
Take command of compliance for third-party models and APIs used in your systems.
12 chapters in this module
- Vendor due diligence
- Contractual obligations
- Transparency requirements
- Subprocessor disclosure
- Audit rights
- Model update notifications
- Compliance certification checks
- Self-declaration review
- Risk scoring
- Onboarding workflow
- Ongoing monitoring
- Termination triggers
Module 11. Harmonizing with ISO 42001 and NIST AI RMF
Integrate AI Act decisions with other frameworks without ceding authority.
12 chapters in this module
- Framework overlap areas
- Control mapping strategy
- Single source of truth
- Documentation reuse
- Audit alignment
- Training consistency
- Risk taxonomy
- Change management
- Policy harmonization
- Cross-framework reporting
- Unified dashboard
- Team coordination
Module 12. Institutionalizing Your Decision Authority
Embed your role as the permanent authority on AI Act controls within the organization.
12 chapters in this module
- Formal role charter
- Org chart placement
- Budget influence
- Hiring input
- Policy update rights
- Cross-department input
- Succession planning
- Training ownership
- External representation
- Thought leadership
- Award recognition
- Career pathing
How this maps to your situation
- Classifying a new ML pipeline under AI Act
- Responding to an auditor question on model documentation
- Approving a model update that changes input schema
- Handling a request to bypass bias testing for time-to-market
Before vs. after
Before
Decisions on AI compliance are fragmented, requiring multiple approvals and slowing down deployment.
After
You own the final call on AI Act control decisions, reducing friction and increasing execution speed.
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 completion over 6-8 weeks with real-world application.
If nothing changes
Continuing without clear decision rights leads to duplicated work, delayed rollouts, and diluted accountability during audits.
How this compares to the alternatives
Unlike generic AI governance courses, this program focuses on concrete decision rights under the AI Act , not just awareness or framework knowledge. Compared to vendor-specific training, it builds transferable authority that survives job changes.
Frequently asked
Who is this course for?
Practitioners who lead or influence AI compliance and want formal authority to make binding decisions under the AI Act.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover Databricks tools?
No. The course focuses on regulatory decision rights under the AI Act, not specific platforms or tools.
$199 one-time. Approximately 3 hours per module, designed for completion over 6-8 weeks with real-world application..
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