A tailored course, built for your situation
Deeper command of the AI Act compliance architecture
Master the structure, logic, and implementation lanes of the AI Act to lead internal guidance and shape cross-functional alignment
$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.
Struggling to translate AI regulations into actionable data workflows
Who this is for
Mid-level data analyst in a tech-forward organisation operating in or with EU markets, involved in model deployment, data pipeline governance, or compliance support.
Who this is not for
Executives seeking high-level overviews, legal counsel focused on liability interpretation, or engineers building foundation models, this is not for policy abstraction or low-level model tuning.
What you walk away with
- Map any AI system to the correct risk tier under the AI Act
- Build compliant technical documentation packs from scratch
- Lead internal alignment between data, legal, and product teams on deployment boundaries
- Anticipate auditor questions and prepare evidence proactively
- Design data governance lanes that satisfy transparency and record-keeping mandates
The 12 modules (with all 144 chapters)
Module 1. Understanding the AI Act’s scope and jurisdictional reach
Establish foundational clarity on what systems qualify as AI under the regulation, which uses trigger obligations, and how extraterritorial enforcement applies to non-EU companies.
12 chapters in this module
- What constitutes an AI system under Title III
- The four-tier risk classification model
- High-risk use cases in data analytics
- Exemptions and limited-risk exceptions
- Obligations for providers vs deployers
- Third-party model dependencies
- Geographic applicability for cloud platforms
- Interaction with national laws
- Enforcement bodies and reporting lines
- Timeline for conformity assessment
- Documentation thresholds by risk tier
- Mapping legacy systems to new rules
Module 2. Classifying AI systems by risk tier
Learn to categorise models based on intended use, impact potential, and sectoral context to determine compliance burden and documentation depth.
12 chapters in this module
- Step-by-step classification logic
- Use cases in hiring and credit scoring
- Biometric identification systems
- Real-time remote biometrics
- Emotion recognition in workplaces
- Education and vocational tracking
- Law enforcement exceptions
- Public sector AI deployments
- Open source model responsibilities
- Generative AI transparency rules
- Version control and updates
- Reclassification triggers
Module 3. Designing conformity assessments
Build repeatable processes to validate that AI systems meet regulatory standards before deployment, including evidence collection and internal review lanes.
12 chapters in this module
- Checklist for high-risk systems
- Data quality requirements
- Bias testing protocols
- Human oversight design
- Accuracy benchmarks
- Robustness and security tests
- Logging requirements
- Version rollback capability
- Incident reporting design
- Third-party audit readiness
- Internal sign-off workflow
- Post-deployment monitoring
Module 4. Building technical documentation packs
Create comprehensive, regulator-ready documentation that satisfies Article 13 requirements and survives scrutiny from legal and compliance reviewers.
12 chapters in this module
- Overview of required documentation
- System purpose and intended use
- Input data specifications
- Model architecture summary
- Training data provenance
- Preprocessing logic
- Performance metrics by group
- Risk mitigation measures
- Update and versioning policy
- User instructions and limitations
- Conformity assessment summary
- Declaration of compliance
Module 5. Implementing data governance for AI compliance
Align existing data pipelines with AI Act documentation needs, focusing on traceability, retention, and quality assurance.
12 chapters in this module
- Data lineage for compliance
- Provenance tracking methods
- Bias audit trails
- Data retention windows
- Access control logging
- Anonymisation standards
- Data quality monitoring
- Schema change governance
- Versioned datasets
- Metadata tagging for compliance
- Integration with Databricks UC
- Automated documentation triggers
Module 6. Managing generative AI transparency obligations
Apply disclosure requirements for systems that generate content, including deepfakes, synthetic media, and large language models.
12 chapters in this module
- Definition of generative AI
- Content labelling standards
- Training data disclosure
- Watermarking techniques
- Prohibited uses
- User notification design
- API consumer obligations
- Fine-tuning responsibility
- Model card requirements
- Copyright compliance
- Third-party content filters
- Monitoring for misuse
Module 7. Aligning legal, data, and product teams
Facilitate cross-functional coordination to ensure compliance decisions are technically sound, legally defensible, and product-aligned.
12 chapters in this module
- Stakeholder mapping
- Defining team responsibilities
- Escalation paths
- Decision logs
- Change approval process
- Risk appetite alignment
- Legal review templates
- Product roadmap integration
- Sprint planning with compliance gates
- Incident response roles
- Communication protocols
- Audit trail maintenance
Module 8. Preparing for audits and regulator inquiries
Anticipate scrutiny and build documentation that answers specific regulator questions before they’re asked.
12 chapters in this module
- Common auditor questions
- Evidence organisation
- Document access protocols
- Version control presentation
- Gap response strategy
- Third-party validation
- Self-reporting frameworks
- Corrective action plans
- Record retention policy
- Interview preparation
- Legal hold procedures
- Public disclosure alignment
Module 9. Establishing internal compliance playbooks
Create reusable templates, checklists, and workflows that institutionalise compliance and reduce rework across projects.
12 chapters in this module
- Playbook structure design
- Risk tier decision trees
- Classification workflows
- Documentation templates
- Automated linting rules
- Review cycle design
- Version control integration
- Training materials
- Onboarding new projects
- Updates for regulatory changes
- Lessons learned repository
- Metrics for compliance health
Module 10. Integrating AI Act rules with ISO 42001 and NIST AI RMF
Bridge multiple frameworks to avoid redundancy and create unified governance lanes across standards.
12 chapters in this module
- Mapping AI Act to ISO 42001
- Crosswalk with NIST AI RMF
- Shared control domains
- Documentation overlap
- Unified assessment lanes
- Common evidence pools
- Risk terminology alignment
- Audit efficiency gains
- Training harmonisation
- Governance committee design
- Framework update tracking
- Stakeholder communication
Module 11. Leading deployment boundary decisions
Gain authority to define where models can and cannot be used based on risk classification and compliance maturity.
12 chapters in this module
- Defining no-go zones
- Risk tolerance thresholds
- Pilot evaluation criteria
- Human-in-the-loop design
- Fallback mechanism planning
- Stakeholder consultation
- Ethics review integration
- Market-specific rules
- Sunset clauses
- Re-evaluation triggers
- Incident escalation path
- Public communication strategy
Module 12. Scaling compliance across teams and systems
Design governance that compounds across projects, reducing per-project overhead and increasing organisational maturity.
12 chapters in this module
- Centralised vs decentralised models
- Compliance as a service
- Internal consulting lanes
- Tooling standardisation
- Training rollout
- Metrics dashboard design
- Maturity assessment
- Benchmarking progress
- Feedback loops
- Continuous improvement cycle
- Leadership reporting
- External recognition pathways
How this maps to your situation
- When launching a new AI feature
- Before a third-party vendor audit
- During internal risk classification
- After a regulatory update
Before vs. after
Before
Reactive, fragmented approach to AI compliance; relying on others to define requirements and timelines
After
Proactive leadership in shaping compliant AI deployments; owning the framework and guiding teams with confidence
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 2.5 hours per module, designed to be completed alongside regular work over 4-6 weeks.
If nothing changes
Without structured knowledge of the AI Act, teams risk delayed deployments, failed audits, or non-compliant systems going live, exposing the organisation to regulatory scrutiny and reputational harm.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level legal summaries, this program delivers actionable, technical compliance capabilities tailored to data practitioners implementing the AI Act in real systems.
Frequently asked
Is this course only for legal or compliance professionals?
No, it's designed specifically for data analysts and engineers who are implementing compliant systems and need to understand the technical and procedural requirements of the AI Act.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover AI Act enforcement in non-EU countries?
The focus is on compliance for organisations subject to EU law, but the structural principles apply broadly to risk-based AI governance globally.
$199 one-time. Approximately 2.5 hours per module, designed to be completed alongside regular work over 4-6 weeks..
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