A tailored course, built for your situation
Strategic AI Data Lineage Practices for Risk-Adverse Boards
Master governance-grade AI transparency with board-ready implementation frameworks
$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.
Even robust AI systems fail governance reviews when data provenance isn’t audit-ready or board-comprehensible.
The situation this course is for
AI initiatives in regulated sectors often stall during oversight reviews due to incomplete data lineage, inconsistent documentation, or misaligned reporting. Without a structured approach, teams face repeated requests for evidence, delayed approvals, and eroded board confidence, even when models perform well technically.
Who this is for
Mid-to-senior level professionals in data governance, compliance, risk, or technical leadership roles who are responsible for ensuring AI systems meet internal audit, regulatory, or board-level scrutiny.
Who this is not for
This is not for data scientists focused solely on model accuracy, nor for IT admins managing infrastructure. It’s not for those seeking high-level AI awareness content or general data management overviews.
What you walk away with
- Design end-to-end AI data lineage frameworks that satisfy internal audit and board expectations
- Translate technical data flows into governance-grade documentation
- Anticipate and respond to compliance inquiries with pre-built evidence structures
- Communicate AI system integrity clearly to non-technical leadership
- Implement repeatable processes for model onboarding and change review cycles
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Establish core principles of data provenance in AI systems, including traceability, ownership, and metadata standards.
12 chapters in this module
- Defining data lineage in AI contexts
- Key components of a lineage map
- Regulatory drivers shaping lineage needs
- Differences between ETL and AI lineage
- Role of metadata in audit readiness
- Data ownership models
- Versioning data and models
- Mapping upstream dependencies
- Downstream impact analysis
- Lineage in real-time vs batch systems
- Common gaps in lineage documentation
- Assessing organizational maturity
Module 2. Governance Frameworks for AI Systems
Explore compliance models and internal controls relevant to AI governance in risk-adverse environments.
12 chapters in this module
- Overview of AI governance standards
- Mapping controls to NIST AI RMF
- Integrating with SOC 2 and ISO frameworks
- Internal audit coordination
- Board reporting expectations
- Risk tiering for AI assets
- Documentation control processes
- Change management protocols
- Third-party model oversight
- Vendor data provenance
- Ethical review integration
- Audit trail retention policies
Module 3. Data Provenance Mapping
Learn systematic methods to map data origin, transformation, and flow across AI pipelines.
12 chapters in this module
- Identifying source data systems
- Tracking data ingestion points
- Transformation logging standards
- Schema evolution tracking
- Feature store lineage
- Label provenance in training sets
- Synthetic data documentation
- Data quality flagging
- Anomaly detection in data pipelines
- Cross-system data correlation
- Automated lineage capture tools
- Manual verification protocols
Module 4. Model Traceability and Versioning
Ensure every model iteration is documented, attributable, and auditable.
12 chapters in this module
- Model version control systems
- Training run metadata
- Hyperparameter tracking
- Dataset-model binding
- Model card creation
- Performance decay monitoring
- Drift detection protocols
- Model lineage across retraining
- Model deployment tracking
- Rollback readiness
- Model deprecation workflows
- Model inventory management
Module 5. Compliance Integration
Align AI data practices with existing regulatory and compliance requirements.
12 chapters in this module
- GDPR and data lineage
- CCPA implications for AI
- HIPAA considerations
- Financial services regulations
- Sector-specific audit requirements
- Cross-border data flows
- Consent tracking in AI
- Right to explanation frameworks
- Data minimization in practice
- Compliance automation
- Evidence packaging for regulators
- Response readiness for audits
Module 6. Board-Level Communication
Translate technical lineage into executive narratives for board engagement.
12 chapters in this module
- Understanding board priorities
- Risk communication frameworks
- Executive summary creation
- Visualizing lineage for leadership
- Scenario planning for oversight
- Anticipating board questions
- Reporting cadence design
- Crisis communication prep
- Linking lineage to business impact
- Building board confidence
- Non-technical storytelling
- Preparing Q&A briefs
Module 7. Audit Readiness Preparation
Structure documentation and processes to pass internal and external audits.
12 chapters in this module
- Audit scope definition
- Evidence collection workflows
- Document version control
- Access logging for audits
- Third-party audit coordination
- Pre-audit self-assessments
- Gap remediation planning
- Response timelines
- Audit trail completeness
- Corrective action tracking
- Post-audit review processes
- Continuous improvement loops
Module 8. Automated Lineage Capture
Implement tooling and pipelines to automatically generate and maintain data lineage.
12 chapters in this module
- Tool selection criteria
- Integration with data catalogs
- API-based lineage extraction
- Code instrumentation methods
- Metadata harvesting
- Event-driven lineage updates
- Accuracy validation
- Handling schema changes
- Scalability considerations
- Cloud-native lineage capture
- On-prem integration
- Hybrid environment support
Module 9. Change Impact Analysis
Assess downstream effects of data and model changes on AI system integrity.
12 chapters in this module
- Change request workflows
- Impact assessment frameworks
- Stakeholder notification protocols
- Testing requirements for changes
- Rollback planning
- Model revalidation triggers
- Documentation updates
- Version comparison tools
- Approval routing
- Post-change monitoring
- Incident linkage
- Change audit trails
Module 10. Third-Party and Vendor Oversight
Extend lineage practices to external data sources and AI vendors.
12 chapters in this module
- Vendor due diligence
- Contractual data requirements
- Third-party audit rights
- Data provenance from vendors
- Model transparency expectations
- Subprocessor tracking
- Vendor risk tiering
- Oversight reporting
- Incident response coordination
- Exit strategy documentation
- Compliance alignment
- Vendor offboarding
Module 11. Implementation Playbook Development
Build a customized, organization-specific playbook for AI data lineage.
12 chapters in this module
- Assessing current state
- Stakeholder alignment
- Roadmap creation
- Pilot program design
- Cross-functional team roles
- Tooling integration plan
- Policy drafting
- Training program development
- Success metrics definition
- Scaling strategy
- Continuous monitoring
- Feedback loop integration
Module 12. Sustaining Governance Over Time
Ensure long-term adherence and evolution of AI data lineage practices.
12 chapters in this module
- Ongoing training programs
- Periodic review cycles
- Policy update processes
- Lessons learned integration
- Benchmarking against peers
- Regulatory horizon scanning
- Internal audit collaboration
- Board reporting updates
- Technology refresh planning
- Team onboarding
- Knowledge retention
- Governance maturity assessment
How this maps to your situation
- AI systems facing board-level scrutiny
- Organizations preparing for AI audits
- Teams implementing new AI governance frameworks
- Enterprises scaling AI with compliance requirements
Before vs. after
Before
Unclear data provenance, reactive compliance responses, and misaligned communication between technical teams and executive leadership.
After
Structured, audit-ready AI data lineage frameworks with board-comprehensible reporting and repeatable governance processes.
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 4-6 hours per module, designed for flexible, self-paced learning over 8-12 weeks.
If nothing changes
Without structured data lineage, even well-designed AI systems face governance delays, repeated audit findings, and erosion of board trust, potentially stalling strategic initiatives.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade frameworks tailored to risk-adverse governance environments, with practical tools and board-focused communication strategies.
Frequently asked
Who is this course designed for?
Mid-to-senior professionals in data governance, compliance, risk, or technical leadership roles responsible for AI system oversight in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior AI governance experience required?
No, foundational concepts are covered, but the course is optimized for practitioners with some exposure to AI or data governance frameworks.
$199 one-time. Approximately 4-6 hours per module, designed for flexible, self-paced learning over 8-12 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