A tailored course, built for your situation
Direct Sign-Off Authority on AI Act Compliance Decisions
Own the final approval on AI governance controls under the EU AI Act without escalation
$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.
Waiting for approvals slows down your AI governance work
The situation this course is for
Even strong contributors find their judgment deferred when compliance sign-off requires senior review. That delay undermines momentum and weakens ownership on high-visibility AI projects.
Who this is for
Senior data engineer operating in regulated environments, implementing AI systems with compliance exposure
Who this is not for
Individuals not involved in AI system design, deployment, or compliance documentation under frameworks like the AI Act
What you walk away with
- Authority to approve AI system risk classifications without escalation
- Final say on inclusion of AI logging and monitoring controls in deployment packages
- Ownership of conformity assessment checklists for internal AI tools
- Ability to clear or block model releases based on AI Act compliance criteria
- Recognition as the internal decision owner for AI governance artefacts
The 12 modules (with all 144 chapters)
Module 1. AI Act Scope and High-Risk Classification
Learn how to identify which data pipelines and models fall under high-risk categories based on regulator guidance and technical criteria.
12 chapters in this module
- Defining AI systems under the AI Act
- Mapping data flows to AI system boundaries
- Identifying high-risk use cases
- Determining model autonomy levels
- Assessing real-world impact severity
- Evaluating legal effect triggers
- Reviewing EBA guidelines on scoring
- Classifying legacy systems
- Documenting classification rationale
- Handling borderline cases
- Updating classifications over time
- Versioning classification records
Module 2. Conformity Assessment Planning
Build a step-by-step plan for demonstrating compliance, tailored to your organization’s AI deployment model.
12 chapters in this module
- Choosing internal vs notified body route
- Defining assessment scope
- Creating evidence checklists
- Scheduling technical reviews
- Assigning documentation roles
- Integrating with SDLC
- Tracking open items
- Setting review cadences
- Preparing for audits
- Version control for artefacts
- Handling third-party components
- Maintaining assessment logs
Module 3. Technical Documentation Requirements
Generate complete and regulator-ready documentation for AI models and data pipelines.
12 chapters in this module
- Specifying system purpose and intent
- Recording data provenance
- Describing preprocessing logic
- Documenting feature engineering
- Logging model versions
- Capturing training parameters
- Outlining inference logic
- Detailing update mechanisms
- Noting limitations and assumptions
- Including human oversight steps
- Archiving documentation copies
- Securing documentation access
Module 4. Risk Management System Design
Implement a living risk management process that meets AI Act requirements and integrates with engineering workflows.
12 chapters in this module
- Defining risk identification triggers
- Setting risk scoring thresholds
- Assigning risk owners
- Integrating with incident tracking
- Documenting mitigation actions
- Scheduling risk reassessments
- Linking risks to controls
- Tracking residual risk
- Reporting risk status
- Updating risk models
- Handling new threat vectors
- Validating control effectiveness
Module 5. Data Governance for High-Risk AI
Ensure training and operational data meet AI Act standards for quality, bias, and provenance.
12 chapters in this module
- Defining data lineage scope
- Validating data collection methods
- Assessing representativeness
- Detecting selection bias
- Documenting data splits
- Logging data transformations
- Tracking drift detection
- Managing data retention
- Ensuring privacy compliance
- Auditing data access
- Handling synthetic data
- Reporting data issues
Module 6. Transparency and Logging Controls
Design audit-ready logging systems that support real-time monitoring and post-deployment review.
12 chapters in this module
- Defining log retention periods
- Capturing model inputs and outputs
- Recording user interactions
- Timestamping decisions
- Anonymizing sensitive data
- Securing log storage
- Automating log checks
- Alerting on anomalies
- Linking logs to models
- Supporting human review
- Generating summary reports
- Verifying log completeness
Module 7. Human Oversight Mechanisms
Implement effective human-in-the-loop processes that satisfy AI Act requirements.
12 chapters in this module
- Identifying oversight points
- Defining intervention rights
- Training human reviewers
- Setting escalation paths
- Documenting review outcomes
- Logging override actions
- Measuring oversight efficacy
- Reducing false positives
- Supporting remote review
- Integrating with workflows
- Updating oversight rules
- Auditing oversight logs
Module 8. Accuracy, Robustness, and Cybersecurity
Validate that AI systems perform reliably under normal and edge conditions.
12 chapters in this module
- Defining accuracy benchmarks
- Testing under stress conditions
- Measuring model drift
- Validating fail-safe modes
- Assessing adversarial robustness
- Reviewing cybersecurity controls
- Conducting penetration tests
- Updating threat models
- Monitoring system uptime
- Validating recovery procedures
- Logging security events
- Reporting vulnerabilities
Module 9. Third-Party AI Component Oversight
Manage compliance risk when using external models, APIs, or platforms.
12 chapters in this module
- Identifying third-party dependencies
- Reviewing vendor documentation
- Assessing compliance alignment
- Negotiating contractual terms
- Auditing external logs
- Validating model updates
- Tracking license obligations
- Managing API changes
- Handling service outages
- Documenting due diligence
- Updating risk assessments
- Reporting third-party issues
Module 10. Post-Market Monitoring
Establish ongoing surveillance to detect performance degradation or unintended consequences.
12 chapters in this module
- Setting monitoring frequency
- Tracking real-world outcomes
- Collecting user feedback
- Detecting bias drift
- Logging edge cases
- Updating models in production
- Managing version rollbacks
- Reporting incidents
- Updating documentation
- Alerting compliance teams
- Scheduling re-evaluations
- Archiving historical data
Module 11. Internal Audit and Readiness Checks
Run proactive audits to ensure continuous compliance and reduce external audit surprises.
12 chapters in this module
- Scheduling internal reviews
- Selecting audit scope
- Preparing evidence packs
- Interviewing team members
- Testing control execution
- Documenting findings
- Assigning action items
- Tracking closure
- Updating playbooks
- Simulating regulator questions
- Improving response time
- Reporting to leadership
Module 12. Regulator Engagement and Evidence Submission
Prepare for and respond to regulator inquiries with confidence and precision.
12 chapters in this module
- Identifying reporting obligations
- Preparing evidence packages
- Responding to information requests
- Structuring narrative responses
- Validating submission completeness
- Coordinating legal review
- Maintaining communication logs
- Handling follow-ups
- Updating internal records
- Learning from feedback
- Improving future submissions
- Archiving regulator correspondence
How this maps to your situation
- Preparing for first AI Act audit
- Leading compliance for a new AI product
- Responding to regulator questions
- Scaling governance across multiple models
Before vs. after
Before
Compliance decisions require approval from senior stakeholders, slowing down project velocity.
After
You own final approval on AI Act compliance artefacts and can release systems with confidence.
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 projects.
If nothing changes
Without clear ownership of compliance sign-off, your contributions remain dependent on others’ bandwidth and judgment, limiting visibility and growth.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses on enforceable AI Act obligations and the concrete decisions you can own as a practitioner.
Frequently asked
Who is this course for?
Senior data engineers and AI practitioners responsible for deploying systems under the EU AI Act.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover NIST AI RMF or ISO 42001?
The focus is the EU AI Act; those frameworks are referenced where aligned but not the core subject.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active projects..
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