A tailored course, built for your situation
Faster path from AI policy intent to working AI Act compliance artefact
Turn regulatory intent into shipped outcomes in half the time with a repeatable delivery system for AI governance
$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.
Spending weeks turning AI Act requirements into working controls only to face rework or auditor pushback
The situation this course is for
Teams are stuck rebuilding compliance from scratch every cycle. They spend more time justifying their approach than shipping it, leading to bloated timelines, inconsistent outputs, and last-minute fire drills when reviewers return comments. Without a standardised method, even experienced practitioners repeat the same effort across projects.
Who this is for
Senior data science practitioner operating at the intersection of AI systems and regulatory readiness, accountable for turning governance mandates into working artefacts
Who this is not for
Entry-level analysts, auditors focused solely on checkbox compliance, or consultants selling one-off frameworks without implementation depth
What you walk away with
- Map AI Act articles directly to enforceable data science controls in under 48 hours
- Produce auditor-ready documentation packages in 12 days instead of 40
- Re-use modular validation workflows across multiple AI systems and business units
- Anticipate reviewer feedback and bake it into first-draft artefacts
- Build internal credibility as the go-to practitioner for fast, defensible AI compliance
The 12 modules (with all 144 chapters)
Module 1. AI Act Article 5 mapping to high-risk classification triggers
Learn how to interpret legal text and convert Article 5 criteria into automated data lineage flags and model scoring rules.
12 chapters in this module
- Identifying high-risk AI systems under Annex III
- Translating 'safety component' into data dependency graphs
- Mapping biometric identification restrictions to use case filters
- Detecting real-time remote biometrics in streaming data
- Classifying emotion recognition systems per Article 5(1)(d)
- Handling spoofing detection exemptions
- Automating critical infrastructure risk triggers
- Flagging AI in education or employment decisions
- Validating law enforcement exceptions
- Blocking prohibited AI use cases at ingestion
- Building a dynamic risk taxonomy
- Integrating with existing model registry tags
Module 2. Data governance controls for training data quality
Implement technical checks that ensure training data meets AI Act transparency and accuracy standards.
12 chapters in this module
- Validating data provenance documentation
- Enforcing minimum metadata completeness
- Checking for dataset imbalance thresholds
- Detecting non-representative sampling
- Logging data cleaning procedures
- Versioning training datasets
- Tracking label correction rates
- Auditing feature selection rationale
- Documenting data augmentation methods
- Preserving original source copies
- Automating data drift detection
- Generating data summary statistics for reviewers
Module 3. Technical documentation that survives auditor review
Build living documentation packages that meet Annex IV requirements and anticipate follow-up questions.
12 chapters in this module
- Structuring system descriptions for clarity
- Detailing intended purpose without overreach
- Mapping system capabilities to risk profile
- Documenting input-output specifications
- Specifying environmental requirements
- Recording accuracy metrics and limitations
- Versioning model cards
- Linking models to deployment contexts
- Including use case restrictions
- Adding human oversight procedures
- Embedding contact information
- Generating machine-readable summaries
Module 4. Bias detection and mitigation workflows
Deploy repeatable processes for identifying and correcting bias across model development cycles.
12 chapters in this module
- Defining sensitive attributes per jurisdiction
- Calculating disparate impact ratios
- Running counterfactual fairness tests
- Measuring equality of opportunity
- Logging bias mitigation techniques
- Validating pre-processing methods
- Testing in-processing adjustments
- Evaluating post-processing corrections
- Monitoring demographic parity
- Tracking mitigation effectiveness
- Documenting trade-offs made
- Updating bias assessments post-deployment
Module 5. Human oversight mechanisms that satisfy auditors
Design enforceable human-in-the-loop requirements that align with AI Act expectations.
12 chapters in this module
- Identifying decision points requiring human review
- Setting escalation thresholds
- Logging human override actions
- Designing user-facing explanations
- Validating transparency of outputs
- Testing explainability under load
- Ensuring meaningful control
- Monitoring override frequency
- Documenting training for reviewers
- Auditing intervention effectiveness
- Updating oversight rules
- Integrating with incident response
Module 6. Robustness testing for high-risk AI systems
Implement automated testing protocols to validate model reliability under stress conditions.
12 chapters in this module
- Defining operational design domain
- Generating edge-case inputs
- Testing model stability under noise
- Validating fallback mechanisms
- Measuring performance degradation
- Logging failure modes
- Running adversarial attacks
- Checking input sanitisation
- Enforcing output constraints
- Validating security protocols
- Auditing test coverage
- Reporting robustness metrics
Module 7. Record-keeping systems that scale across deployments
Automate logging and retention policies that meet AI Act audit requirements.
12 chapters in this module
- Capturing model development history
- Storing training configurations
- Logging evaluation results
- Archiving deployment decisions
- Preserving incident reports
- Tracking version updates
- Enforcing data retention rules
- Securing access logs
- Validating log integrity
- Supporting data subject rights
- Exporting records for inspection
- Generating audit trails
Module 8. Transparency requirements for deployers and users
Meet AI Act obligations for user information and system disclosure.
12 chapters in this module
- Drafting clear system descriptions
- Notifying users of AI interaction
- Disclosing limitations and risks
- Providing opt-out mechanisms
- Ensuring accessibility
- Localising user materials
- Updating documentation post-change
- Validating notice delivery
- Logging consent records
- Monitoring user feedback
- Responding to inquiries
- Updating transparency statements
Module 9. Compliance validation playbooks for internal audits
Create reusable checklists and workflows to verify AI Act alignment across teams.
12 chapters in this module
- Mapping controls to articles
- Building self-assessment templates
- Running gap analyses
- Prioritising remediation
- Validating implementation
- Documenting evidence
- Scheduling recurring reviews
- Integrating with SDLC
- Training reviewers
- Generating compliance dashboards
- Reporting to leadership
- Updating for regulatory changes
Module 10. Stakeholder communication strategies for AI governance
Align technical teams, legal, and business units around common compliance goals.
12 chapters in this module
- Translating legal text for engineers
- Explaining technical limits to legal
- Presenting risk posture to leadership
- Conducting cross-functional workshops
- Developing common glossary
- Creating visual frameworks
- Running tabletop exercises
- Facilitating policy reviews
- Managing escalation paths
- Documenting decisions
- Sharing lessons learned
- Building governance networks
Module 11. Incident reporting and corrective action workflows
Establish processes for identifying, logging, and resolving AI system issues.
12 chapters in this module
- Defining reportable incidents
- Setting up logging pipelines
- Classifying severity levels
- Triggering escalation protocols
- Conducting root cause analysis
- Documenting corrective actions
- Notifying authorities when required
- Updating risk assessments
- Validating fixes
- Communicating with users
- Preserving records
- Reviewing post-incident
Module 12. Scaling compliance across multiple AI systems
Replicate successful patterns across business units while maintaining adaptability.
12 chapters in this module
- Identifying reusable components
- Building shared libraries
- Standardising documentation
- Creating onboarding playbooks
- Training new teams
- Adapting to local requirements
- Monitoring compliance debt
- Sharing best practices
- Conducting peer reviews
- Maintaining central registry
- Updating for new use cases
- Retiring legacy systems
How this maps to your situation
- After high-risk AI system identification
- During model development phase
- Before internal compliance review
- Post-deployment monitoring
Before vs. after
Before
Spending weeks interpreting the AI Act and rebuilding compliance approaches from scratch for each project, leading to inconsistent outputs and auditor rework.
After
Delivering auditor-ready AI Act compliance packages in under two weeks using repeatable workflows that compound value across use cases.
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, apply each lesson directly to real work.
If nothing changes
Without a standardised approach, each new AI project restarts the compliance journey from zero, wasting time, incurring rework, and delaying deployment while peers who use structured systems move faster and gain visibility.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level regulatory summaries, this program focuses on executable implementation: how to turn AI Act text into working code, documentation, and reviewable artefacts that auditors accept the first time.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover other frameworks like NIST AI RMF or ISO 42001?
The core focus is AI Act implementation, but the system can be adapted to other frameworks as needed.
Can I apply this to non-high-risk AI systems?
Yes, the same principles improve velocity and quality for all AI governance work, though the AI Act focus targets high-risk systems first.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active projects, apply each lesson directly to real 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