A tailored course, built for your situation
Enterprise-Class AI Data Lineage Practices for Compliance Officers
Master compliance-grade data lineage frameworks for AI systems in regulated environments
$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.
Deploying AI without clear data lineage creates invisible compliance exposure, even when models perform well.
The situation this course is for
As AI systems grow more complex, regulators expect transparent data provenance. Without structured lineage practices, compliance teams face increased scrutiny, audit delays, and operational rework. Traditional approaches don’t scale to modern data pipelines or satisfy evolving standards.
Who this is for
Mid-to-senior level compliance, risk, or governance professionals in regulated industries who influence or oversee AI deployment and data governance.
Who this is not for
This is not for data engineers focused solely on pipeline architecture, nor for executives seeking only high-level overviews. It’s for practitioners who must implement and defend lineage practices.
What you walk away with
- Apply enterprise-grade data lineage frameworks to AI and ML systems
- Align data provenance practices with compliance and audit requirements
- Implement automated metadata tracking aligned with regulatory expectations
- Document lineage trails that withstand regulatory scrutiny
- Lead cross-functional efforts to operationalize trustworthy AI governance
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Introduce core concepts of data lineage in AI, including scope, criticality, and compliance implications.
12 chapters in this module
- Defining data lineage in AI contexts
- Regulatory drivers shaping lineage requirements
- Lineage vs. provenance vs. traceability
- The role of compliance officers in lineage oversight
- Common misconceptions about AI lineage
- Scoping lineage efforts across data pipelines
- Data origin classification frameworks
- Lineage in batch vs. real-time systems
- Key stakeholders in lineage governance
- Internal audit expectations for lineage
- Baseline assessment tools for lineage maturity
- Integrating lineage into risk registers
Module 2. Regulatory and Compliance Frameworks
Explore current standards and expectations from SEC, GDPR, NIST, and other emerging guidelines.
12 chapters in this module
- GDPR and data provenance obligations
- SEC expectations for model data inputs
- NIST AI Risk Management Framework alignment
- FFIEC guidance on model risk data traceability
- ISO standards for data lifecycle governance
- Cross-border data flow implications
- Audit trail requirements for regulators
- Documentation standards for compliance
- Regulatory technology (RegTech) integration
- Preparing for inspection with lineage records
- Jurisdictional variations in data lineage rules
- Future-looking compliance scenarios
Module 3. Data Provenance and Model Lineage
Map data flow from source to model output, ensuring end-to-end traceability.
12 chapters in this module
- Tracking data from ingestion to inference
- Versioning data sets and model inputs
- Metadata tagging best practices
- Automated lineage capture tools
- Handling data transformations in lineage
- Provenance in federated learning environments
- Data drift and lineage documentation
- Model version to data set mapping
- Reproducibility requirements
- Lineage in A/B testing scenarios
- Third-party data sourcing and lineage
- Provenance in synthetic data use
Module 4. Automated Metadata Management
Implement systems that capture and maintain lineage metadata without manual effort.
12 chapters in this module
- Metadata schema design for compliance
- Instrumenting pipelines for auto-tagging
- Integrating with data catalogs
- OpenLineage and similar frameworks
- API-based metadata collection
- Handling unstructured data in metadata
- Data quality flags in metadata
- Temporal metadata and change tracking
- Ownership and stewardship metadata
- Access control and metadata visibility
- Metadata validation techniques
- Audit-ready metadata export formats
Module 5. Audit-Ready Documentation Patterns
Structure lineage documentation to meet auditor expectations and reduce friction.
12 chapters in this module
- Designing auditable lineage trails
- Standardizing documentation formats
- Narrative vs. technical documentation
- Lineage diagrams for non-technical reviewers
- Version-controlled documentation
- Cross-referencing with model risk records
- Preparing for internal and external audits
- Documentation for incident response
- Redaction and confidentiality handling
- Automated report generation
- Checklist-based validation
- Document retention and archiving
Module 6. Cross-Functional Governance Models
Align data, compliance, legal, and engineering teams around shared lineage practices.
12 chapters in this module
- Defining RACI for data lineage
- Compliance officer as governance hub
- Engaging engineering teams effectively
- Legal team involvement in data provenance
- Data governance committee integration
- Escalation paths for lineage gaps
- Change management for lineage adoption
- Training non-compliance stakeholders
- Metrics for cross-team accountability
- Conflict resolution in data ownership
- Vendor and third-party coordination
- Sustaining governance over time
Module 7. Implementation at Scale
Deploy lineage practices across enterprise AI portfolios, not just pilot projects.
12 chapters in this module
- Phased rollout strategies
- Prioritizing high-risk models first
- Resource allocation for lineage work
- Tooling integration across teams
- Standardizing across business units
- Handling legacy system constraints
- Cloud vs. on-premise lineage challenges
- Managing multi-cloud data flows
- Scaling metadata infrastructure
- Cost-benefit analysis of lineage effort
- Measuring implementation success
- Continuous improvement cycles
Module 8. Real-World Lineage Patterns
Study proven implementations in financial services, healthcare, and retail sectors.
12 chapters in this module
- Lineage in credit decisioning systems
- Healthcare AI and patient data tracking
- Retail demand forecasting provenance
- Fraud detection model lineage
- Customer segmentation data trails
- Supply chain AI and data sourcing
- Personalization engine transparency
- Geolocation data in lineage records
- Time-series data handling
- Multi-modal input tracking
- Edge AI and decentralized data
- Case comparison across industries
Module 9. Risk and Control Integration
Embed lineage into enterprise risk and internal control frameworks.
12 chapters in this module
- Mapping lineage to control objectives
- Integrating with SOX controls
- Data lineage in fraud risk programs
- Model risk management alignment
- Key risk indicators for lineage gaps
- Control testing with lineage data
- Exception reporting workflows
- Integrating with GRC platforms
- Third-party risk and data provenance
- Cybersecurity incident response linkage
- Insurance and liability considerations
- Board-level risk reporting
Module 10. Tools and Technology Stack
Evaluate and select platforms that support compliance-grade lineage.
12 chapters in this module
- Commercial vs. open-source lineage tools
- Integration with data warehouses
- ML metadata stores (e.g., MLflow)
- Data catalog platforms (e.g., Collibra)
- Custom vs. off-the-shelf solutions
- APIs for lineage interoperability
- Tool selection criteria for compliance
- Vendor due diligence for lineage tools
- Performance under scale
- Audit log extraction capabilities
- User access and role management
- Tool cost and licensing models
Module 11. Change and Incident Response
Use lineage to support incident investigations and model changes.
12 chapters in this module
- Root cause analysis with lineage
- Model rollback and data consistency
- Data breach impact assessment
- Handling unauthorized data use
- Model retraining and data versioning
- Change approval workflows
- Post-incident reporting with lineage
- Regulatory disclosure support
- Lessons learned documentation
- Strengthening controls after incidents
- Simulating incident scenarios
- Cross-team communication protocols
Module 12. Future-Proofing and Strategy
Anticipate evolving requirements and position your organization ahead of curve.
12 chapters in this module
- Emerging regulatory trends in AI
- Global data governance initiatives
- AI act and similar frameworks
- Preparing for algorithmic audits
- Sustainability and ESG data linkage
- AI ethics and provenance
- Consumer right-to-explanation
- Automated compliance monitoring
- AI assurance as a service
- Building internal expertise
- Succession planning for governance roles
- Long-term data stewardship vision
How this maps to your situation
- Implementing AI in a regulated environment
- Facing internal audit or regulatory scrutiny
- Scaling AI governance beyond pilots
- Responding to incident or control failure
Before vs. after
Before
Unclear ownership of data provenance, reactive compliance posture, and fragmented documentation that slows audits and increases risk.
After
Structured, automated, and auditable data lineage practices that support trustworthy AI deployment and reduce compliance friction.
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, 4 hours per module, designed for flexible pacing with immediate access to all materials.
If nothing changes
Organizations that delay implementation risk increased audit findings, regulatory penalties, and loss of stakeholder trust when AI systems come under scrutiny.
How this compares to the alternatives
Unlike generic data governance courses, this program focuses exclusively on AI lineage with compliance-grade precision. It goes beyond conceptual overviews to provide implementation templates and real-world patterns used in regulated sectors.
Frequently asked
Who is this course for?
Compliance, risk, and governance professionals in organizations deploying or overseeing AI systems, especially in regulated industries.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this technical or conceptual?
Implementation-grade: bridges technical depth with compliance requirements, designed for practitioners who must apply the concepts.
$199 one-time. Approximately 3, 4 hours per module, designed for flexible pacing with immediate access to all materials..
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