A tailored course, built for your situation
Production-Grade AI Data Lineage Practices for Compliance Officers
Implement auditable, compliant AI systems with confidence and precision
$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.
Unclear data provenance undermines compliance confidence in AI systems
The situation this course is for
As AI models move into production, compliance officers face growing pressure to verify data origins, transformations, and access controls. Without clear, automated data lineage, audit outcomes become unpredictable and remediation costly. This gap is no longer technical, it’s strategic.
Who this is for
Compliance, risk, and governance professionals in technology-driven organizations overseeing AI deployment and regulatory adherence
Who this is not for
Individuals seeking theoretical overviews or high-level AI ethics discussions without implementation detail
What you walk away with
- Build and validate end-to-end data lineage for AI systems
- Implement audit-ready documentation practices
- Map lineage requirements to regulatory frameworks
- Evaluate tooling for automated lineage capture
- Lead cross-functional teams on compliance-by-design for AI
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Establish core concepts, compliance drivers, and system context
12 chapters in this module
- Defining data lineage in AI contexts
- Regulatory expectations across jurisdictions
- Lineage vs. provenance vs. traceability
- Role of metadata in compliance
- Common misconceptions in practice
- Lifecycle stages of data in AI
- Compliance officer responsibilities
- Integration with data governance
- Key stakeholders and ownership
- Documentation standards overview
- Case example: Model rollout failure
- Self-audit checklist
Module 2. Regulatory Landscape and Compliance Drivers
Map lineage requirements to active frameworks and expectations
12 chapters in this module
- GDPR and data provenance
- CCPA and consumer data rights
- EU AI Act compliance tiers
- Sector-specific mandates
- Audit frequency and scope
- Cross-border data flows
- Enforcement trends
- Documentation as evidence
- Risk-based approach thresholds
- Compliance reporting cycles
- Third-party assessment prep
- Future-looking standards
Module 3. Data Provenance in Machine Learning Pipelines
Trace data from source to model input with precision
12 chapters in this module
- Ingestion tracking methods
- Schema versioning
- Data quality checkpoints
- Feature store integration
- Label provenance
- Data augmentation tracking
- Bias detection triggers
- Model-data dependency maps
- Pipeline metadata capture
- Automated lineage tagging
- Tool interoperability
- Validation workflows
Module 4. Model Lineage and Version Control
Track model development, training, and deployment history
12 chapters in this module
- Model card essentials
- Training run metadata
- Hyperparameter tracking
- Artifact storage standards
- Version control for models
- Environment configuration
- Reproducibility requirements
- Model registry integration
- Audit trail structure
- Change approval workflows
- Rollback preparedness
- Stakeholder access controls
Module 5. Automated Lineage Capture Tools
Evaluate and deploy tooling for scalable lineage
12 chapters in this module
- Open-source vs. commercial tools
- Metadata extraction methods
- API-based integration patterns
- Graph database backends
- Real-time vs. batch capture
- Tool accuracy benchmarks
- Vendor evaluation criteria
- Cost of ownership analysis
- Interoperability with data stack
- Custom parser development
- Alerting on lineage gaps
- Toolchain documentation
Module 6. End-to-End Lineage Framework Design
Architect a unified lineage system across data and AI
12 chapters in this module
- System boundary definition
- Cross-layer mapping
- Metadata consistency rules
- Ownership assignment models
- Data contract integration
- Change propagation rules
- Validation at scale
- Failure mode analysis
- Recovery procedures
- Cross-functional workflows
- Stakeholder communication plan
- Framework maturity model
Module 7. Documentation Standards for Audits
Prepare clear, defensible records for inspection
12 chapters in this module
- Audit-ready package structure
- Lineage diagram conventions
- Metadata completeness checks
- Timestamp accuracy
- Access logs and permissions
- Data retention alignment
- Anonymization impact
- Third-party data handling
- Versioned document control
- Reviewer feedback loop
- Pre-audit rehearsal
- Response protocol
Module 8. Cross-Functional Collaboration Models
Lead alignment between compliance, data, and engineering
12 chapters in this module
- Shared ownership frameworks
- Compliance embedded in sprints
- Engineering handoff protocols
- SLA definitions for lineage
- Conflict resolution pathways
- Training for technical teams
- Feedback loop design
- Incentive alignment
- Escalation procedures
- Joint documentation practices
- Tool access delegation
- Performance metrics
Module 9. Validation and Testing of Lineage Systems
Ensure accuracy, completeness, and reliability
12 chapters in this module
- Test case design
- Synthetic data injection
- Gap detection methods
- Accuracy measurement
- False positive handling
- Automated validation scripts
- Manual verification protocols
- Third-party validation
- Error correction workflows
- Performance under load
- Edge case handling
- Reporting mechanisms
Module 10. Incident Response and Remediation
Respond to lineage gaps and compliance issues
12 chapters in this module
- Incident classification
- Root cause analysis
- Temporary mitigation paths
- Stakeholder notification
- Regulatory disclosure triggers
- Remediation planning
- Post-mortem process
- Process improvement
- Timeline reconstruction
- Audit coordination
- Legal counsel engagement
- Public statement prep
Module 11. Scaling Lineage Across the Organization
Expand practices from pilot to enterprise level
12 chapters in this module
- Phased rollout strategy
- Center of excellence model
- Training program design
- Internal certification
- Budget justification
- Executive reporting
- Change management
- Tool standardization
- Vendor management
- Policy harmonization
- Global team coordination
- Success metrics
Module 12. Future-Proofing and Continuous Improvement
Adapt to evolving regulations and technologies
12 chapters in this module
- Regulatory horizon scanning
- Technology watch process
- Compliance innovation pipeline
- Feedback loop integration
- Version upgrade planning
- Stakeholder expectation shifts
- AI model complexity trends
- Emerging data rights
- Global alignment efforts
- Internal audit evolution
- Benchmarking against peers
- Course recap and next steps
How this maps to your situation
- Preparing for regulatory audit
- Scaling AI initiatives responsibly
- Improving cross-team collaboration
- Implementing new data governance standards
Before vs. after
Before
Uncertain about how to verify data origins in AI systems, relying on ad-hoc documentation and fragmented tooling
After
Confident in designing, validating, and defending end-to-end data lineage frameworks that meet compliance demands
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 45, 60 minutes per week over 12 weeks, designed for working professionals
If nothing changes
Organizations without robust data lineage risk delayed AI adoption, failed audits, and reputational harm due to lack of transparency
How this compares to the alternatives
Unlike general AI ethics courses or high-level overviews, this program delivers implementation-grade practices, tool-specific guidance, and audit-ready documentation frameworks tailored to compliance officers in regulated environments.
Frequently asked
Who is this course designed for?
Compliance, risk, and governance professionals overseeing AI deployment in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is issued after finishing all modules and assessments.
$199 one-time. Approximately 45, 60 minutes per week over 12 weeks, designed for working professionals.
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