A tailored course, built for your situation
Direct sign-off authority on AI governance framework decisions
A 12-module program to establish unambiguous ownership of AI governance choices aligned with OECD AI Principles
$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.
Who this is for
Senior data practitioner in cloud platform environments advancing AI governance ownership
Who this is not for
Entry-level compliance staff, individual contributors without cross-functional influence, or practitioners focused only on infrastructure implementation
What you walk away with
- Own final decisions on AI system risk categorisation without review
- Control approval of monitoring and logging requirements for AI workloads
- Define what constitutes sufficient compliance evidence for audits
- Set thresholds for AI incident reporting and response workflows
- Finalise documentation templates used across AI governance engagements
The 12 modules (with all 144 chapters)
Module 1. Defining AI system risk tiers independently
Learn to classify AI workloads by impact level using OECD AI Principles. Make final determinations on high-risk status without escalation. Use real-world examples from financial services and healthcare to justify boundaries.
12 chapters in this module
- What is an AI system under OECD principles
- Mapping model function to risk category
- Determining high-risk triggers
- Boundary setting for inference scope
- Input autonomy vs decision impact
- Human oversight thresholds
- Legacy system inclusion rules
- Model update reclassification
- Jurisdictional variation handling
- Documentation standard setting
- Peer challenge response protocol
- Final determination authority
Module 2. Setting monitoring requirements for AI deployments
Establish performance, drift, and fairness thresholds your team must meet. Own the definition of what constitutes acceptable behaviour in production. No review needed for baseline monitoring rules.
12 chapters in this module
- Performance decay tolerance
- Drift detection frequency
- Bias metric selection
- Alerting threshold setting
- False positive trade-offs
- Logging depth per risk tier
- Access review intervals
- Model version tracking
- Feedback loop integration
- Remediation window definition
- Escalation path design
- Internal audit readiness check
Module 3. Approving compliance evidence packaging
Control what counts as proof for adherence to OECD AI Principles. Finalise templates used across teams. Own the format and depth of documentation shared with oversight groups.
12 chapters in this module
- Evidence type by risk level
- Model card completeness
- System documentation depth
- Third-party assessment scope
- Version control requirements
- Change approval tracking
- Stakeholder communication logs
- Internal review sign-off
- External auditor preparation
- Gap reporting method
- Compliance timeline mapping
- Final evidence package lock
Module 4. Owning AI incident classification and reporting
Decide what qualifies as an AI incident, how severe it is, and who gets notified. Set response timelines and documentation expectations. No override from risk or legal teams.
12 chapters in this module
- Defining incident scope
- Harm type classification
- Near-miss inclusion
- Severity band assignment
- Notification trigger setting
- Response team activation
- Post-incident review depth
- Remediation tracking
- Regulatory reporting threshold
- Public disclosure criteria
- Lessons-learned capture
- Process update ownership
Module 5. Finalising data provenance and lineage rules
Set standards for how training data is tracked and validated. Own the acceptable level of lineage detail per risk category. Define what counts as sufficient溯源.
12 chapters in this module
- Data source documentation
- Labeling process verification
- Synthetic data inclusion
- Bias audit requirements
- Third-party data validation
- Version tracking mechanism
- Access control alignment
- Retention policy setting
- Data refresh frequency
- Drift detection triggers
- Lineage gap handling
- Final record of truth
Module 6. Establishing human oversight protocols
Determine when and how humans intervene in AI-driven decisions. Define review frequency, escalation triggers, and override authority. Your design is binding across implementations.
12 chapters in this module
- Override mechanism design
- Review interval setting
- Escalation path definition
- Decision logging depth
- Audit trail retention
- Training requirement setting
- Role-based access control
- Failure mode analysis
- Handoff protocol design
- User feedback integration
- Performance review cycle
- Final authority confirmation
Module 7. Setting model update and retraining policies
Control when models are refreshed, retrained, or retired. Define performance decay thresholds that trigger action. Own the change management process for AI models.
12 chapters in this module
- Performance baseline setting
- Drift detection triggers
- Retraining frequency
- Data refresh requirements
- Version compatibility
- Rollback protocol
- Staging deployment rules
- User notification method
- Change approval threshold
- Documentation update cycle
- Incident link analysis
- Final update authority
Module 8. Defining fairness and non-discrimination metrics
Select which fairness indicators to track and at what tolerance levels. Own the methodology for bias testing. No need for external review before implementation.
12 chapters in this module
- Demographic parity check
- Equal opportunity rate
- Predictive parity
- Calibration testing
- Disparate impact threshold
- Bias audit frequency
- Mitigation technique selection
- Model constraint setting
- Trade-off documentation
- Stakeholder challenge handling
- Public reporting depth
- Final metric approval
Module 9. Controlling transparency and disclosure standards
Decide what information is shared with users and regulators. Set documentation depth and public communication norms. Your standard applies across teams.
12 chapters in this module
- User-facing explanation
- Model capability disclosure
- Limitation communication
- Third-party dependency
- Data use transparency
- Performance reporting
- Failure mode disclosure
- Assumption listing
- Change notification
- Glossary standardisation
- Language accessibility
- Final content authority
Module 10. Owning accountability and audit readiness process
Design how responsibility is assigned and verified across AI systems. Define what audits look for and how findings are resolved. Your framework is the reference.
12 chapters in this module
- Role definition clarity
- Decision trail logging
- Audit scope definition
- Evidence packaging
- Findings resolution
- Corrective action tracking
- Remediation timeline
- Stakeholder update
- Process improvement
- Review frequency
- Escalation handling
- Final accountability confirmation
Module 11. Setting security and robustness requirements
Determine resilience expectations for AI models. Own the definition of acceptable security posture. Your bar sets the minimum for deployment.
12 chapters in this module
- Adversarial attack resistance
- Model inversion protection
- Data poisoning defense
- Input validation rules
- System availability
- Fail-safe mechanisms
- Recovery protocol
- Penetration test frequency
- Threat model update
- Security patch cycle
- Incident response depth
- Final security standard
Module 12. Finalising governance committee inputs
Control what goes into oversight meetings. Define agenda items, reporting format, and escalation criteria. Your input shapes leadership understanding.
12 chapters in this module
- Meeting agenda setting
- Risk reporting format
- Escalation threshold
- Decision tracking
- Action item ownership
- Progress reporting
- External update
- Stakeholder alignment
- Resource request
- Strategic direction input
- Policy change proposal
- Final input authority
How this maps to your situation
- AI system onboarding
- Model incident response
- Audit preparation cycle
- Framework update rollout
Before vs. after
Before
Input on AI governance decisions required review and alignment across teams.
After
Final decision ownership on framework design, monitoring rules, and compliance evidence packaging.
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 existing workflow with immediate application.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses on specific decision ownership under OECD AI Principles, providing actionable frameworks rather than theoretical overviews.
Frequently asked
Is this course about Databricks or AI platforms?
No. The course focuses on governance decisions under OECD AI Principles, not specific platforms or tools.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
What makes this different from other AI governance training?
It focuses on concrete decision ownership , final sign-off on risk tiers, monitoring rules, incident response, and compliance packaging , using OECD AI Principles as the anchor.
$199 one-time. Approximately 3 hours per module, designed for integration into existing workflow with immediate 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