A tailored course, built for your situation
Repeatable AI governance artefacts that compound across projects
Build a durable AI governance foundation with reusable frameworks aligned to the AI Act
$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.
Governance work gets reinvented every time
The situation this course is for
Teams restart from scratch on each AI project, duplicating effort on risk assessments, compliance checks, and documentation, wasting time and weakening consistency
Who this is for
Senior technical practitioner in high-trust AI or data infrastructure, shaping governance through code and architecture decisions
Who this is not for
Those looking for introductory AI ethics overviews or non-technical policy summaries
What you walk away with
- Produce AI Act-aligned conformity assessments that serve as templates for future use
- Generate version-controlled technical documentation repositories that evolve across deployments
- Design modular risk maps that transfer from one model stack to another
- Embed compliance checks directly into CI/CD pipelines using reusable components
- Build an internal IP library of governance artefacts that compound value across team boundaries
The 12 modules (with all 144 chapters)
Module 1. AI Act compliance at the engineering layer
Ground governance in the legal requirements of the AI Act, focusing on how they translate to concrete system design choices.
12 chapters in this module
- Scope of AI Act applicability
- High-risk AI system classification
- Obligations for deployers and developers
- Role of technical documentation
- Conformity assessment pathways
- Transparency requirements
- Record keeping expectations
- Interface design obligations
- Model monitoring mandates
- Data quality requirements
- Human oversight specifications
- Compliance timing under the AI Act
Module 2. Modular threat modeling for AI systems
Break down AI risk assessment into reusable components that adapt across model types and deployment contexts.
12 chapters in this module
- Taxonomy of AI-specific threats
- Threat modeling with STRIDE-LM
- Reusable dataflow diagrams
- Model card integration
- Prompt injection attack patterns
- Training data poisoning vectors
- Model inversion risks
- Membership inference scenarios
- Adversarial robustness checks
- Bias amplification pathways
- Output manipulation techniques
- Model stealing mitigations
Module 3. Living conformity assessments
Move beyond static compliance checklists to dynamic, versioned assessments tied to CI/CD and model lifecycle.
12 chapters in this module
- Structure of a conformity report
- Versioning compliance artefacts
- Automated evidence collection
- Mapping controls to AI Act Annex III
- Integrating with dev pipelines
- Evidence tagging strategies
- Audit readiness workflows
- Change impact analysis
- Rollback compliance checks
- Third-party model validation
- Internal escalation paths
- Certification preparation
Module 4. Reusable technical documentation templates
Design documentation structures that persist across projects, reducing friction in audits and onboarding.
12 chapters in this module
- Required elements under AI Act
- Model design specifications
- Data pipeline documentation
- Training data provenance
- Performance benchmarking
- Robustness testing logs
- Accuracy metrics tracking
- Version history logging
- Human oversight mechanisms
- Use limitation disclosures
- Post-deployment monitoring
- Incident reporting process
Module 5. Governance-aware CI/CD pipelines
Embed compliance checks directly into development workflows to catch issues early and reduce rework.
12 chapters in this module
- Pre-commit hooks for model cards
- Static analysis for bias checks
- Automated data lineage capture
- Model signature validation
- Policy-as-code integration
- Risk score calculation at merge
- Compliance gates before deploy
- Rollback compliance verification
- Audit trail generation
- Third-party dependency checks
- Model explainability requirements
- Documentation completeness checks
Module 6. Composable risk mapping
Design risk matrices that transfer across projects and adapt to new AI capabilities.
12 chapters in this module
- Common AI risk dimensions
- Scoring likelihood and impact
- Mapping to AI Act requirements
- Reusing control patterns
- Cross-project risk libraries
- Automated risk register updates
- Threshold-based escalation
- Mitigation effectiveness tracking
- Residual risk documentation
- Stakeholder communication plans
- Risk treatment workflows
- Periodic review automation
Module 7. Model registry with policy enforcement
Implement a centralized model governance layer that ensures compliance at every lifecycle stage.
12 chapters in this module
- Model registration workflow
- Metadata standardization
- Policy-based approval
- Version comparison tools
- Lineage tracking
- Stakeholder notification
- Decommissioning process
- Access control policies
- Audit logging
- Compliance snapshotting
- Cross-team visibility
- Integration with MLOps tools
Module 8. Human oversight mechanism design
Build meaningful human-in-the-loop systems that satisfy AI Act requirements and improve performance.
12 chapters in this module
- Defining meaningful control
- Threshold-based intervention
- Feedback loop integration
- Monitoring interface design
- Escalation protocols
- Training for oversight roles
- Response time benchmarks
- Override logging
- Incident review process
- Bias detection triggers
- Model drift alerts
- Compliance documentation
Module 9. Transparency outputs for external stakeholders
Generate clear, compliant disclosures for users, regulators, and partners.
12 chapters in this module
- User-facing documentation
- API documentation
- System capability disclosures
- Limitations communication
- Intended use definition
- Prohibited use policies
- Data handling disclosures
- Model update notifications
- Performance decline alerts
- Incident reporting channels
- Accessibility requirements
- Multilingual disclosure
Module 10. Post-deployment monitoring frameworks
Design observability systems that detect compliance drift and performance degradation.
12 chapters in this module
- Performance metric baselines
- Drift detection thresholds
- Bias monitoring
- Adversarial test suites
- Uptime tracking
- User feedback collection
- Error logging
- Anomaly detection
- Model retraining triggers
- Compliance check automation
- Incident response workflow
- Audit trail updates
Module 11. Cross-functional governance workflows
Align legal, compliance, engineering, and product teams around shared governance artefacts.
12 chapters in this module
- Common vocabulary adoption
- Shared documentation standards
- Review cycle design
- Approval workflows
- Change notification process
- Escalation paths
- Cross-team templates
- Feedback integration
- Conflict resolution
- Compliance ownership
- Audit coordination
- Training integration
Module 12. Sustaining compounding governance value
Ensure governance artefacts grow in value over time through reuse, refinement, and institutional memory.
12 chapters in this module
- Artefact deprecation policies
- Version retention rules
- Knowledge transfer
- Onboarding new members
- Process improvement
- Feedback loops
- Lessons learned tracking
- Benchmarking progress
- Value measurement
- Stakeholder reporting
- Continuous training
- Governance roadmap
How this maps to your situation
- During pre-deployment compliance review
- When scaling AI systems across business units
- Before regulatory inspection
- After a model incident or near miss
Before vs. after
Before
Governance artefacts are rebuilt from scratch per project, creating silos and rework.
After
A growing library of reusable, AI Act-aligned artefacts accelerates delivery and strengthens compliance posture across teams.
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 project timelines.
If nothing changes
Continuing to rebuild governance work erodes leverage, slows delivery, and keeps valuable contributions below the visibility line.
How this compares to the alternatives
Unlike generic AI ethics courses, this program delivers tangible, reusable artefacts aligned to the AI Act , built for engineers who ship systems, not policy papers.
Frequently asked
Is this course technical or policy-focused?
It's for technical practitioners building systems that must comply with the AI Act , with code-level implementation patterns and documentation templates.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover ISO 42001 or NIST AI RMF?
The core anchor is the AI Act, but principles align with broader frameworks like NIST AI RMF and ISO 42001 where applicable.
$199 one-time. Approximately 3 hours per module, designed for integration into real project timelines..
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