A tailored course, built for your situation
Enterprise-Class AI Data Lineage Practices for Compliance Officers
Master implementation-grade data lineage frameworks that align with modern compliance demands and AI governance standards
$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.
Lack of clear, auditable data lineage undermines AI governance and exposes organizations to compliance risk during audits and regulatory reviews
The situation this course is for
Compliance officers are increasingly expected to validate the origins, transformations, and controls applied to data feeding AI systems. Without structured lineage practices, teams face challenges in demonstrating accountability, especially during regulatory scrutiny or internal audits. This gap can delay AI adoption and increase oversight friction.
Who this is for
Compliance, risk, and governance professionals in mid-to-large organizations implementing or overseeing AI systems and seeking to strengthen auditability and regulatory alignment
Who this is not for
Individuals seeking introductory AI awareness training or non-compliance roles such as data scientists without governance responsibilities
What you walk away with
- Apply enterprise-grade data lineage frameworks tailored to compliance requirements
- Map data flows across AI pipelines with audit-ready documentation
- Integrate lineage practices into existing governance and risk management processes
- Lead cross-functional alignment between legal, IT, data engineering, and compliance teams
- Reduce audit preparation time and increase confidence in regulatory reporting
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Establish core concepts, terminology, and compliance relevance of data lineage in AI systems
12 chapters in this module
- Introduction to data lineage in AI
- Compliance drivers for traceability
- Regulatory expectations across jurisdictions
- Key components of lineage architecture
- Lineage vs. metadata management
- The role of provenance in AI
- Common misconceptions clarified
- Stakeholder alignment basics
- Governance frameworks integration
- Audit readiness foundations
- Use case taxonomy
- Getting started: first steps
Module 2. Regulatory Landscape and Lineage
Examine current compliance standards requiring data traceability and how lineage satisfies them
12 chapters in this module
- GDPR and data provenance
- CCPA and consumer rights
- HIPAA considerations for health AI
- SOX implications for financial AI
- EU AI Act compliance mapping
- Industry-specific mandates
- Cross-border data flow rules
- Audit expectations by sector
- Documentation standards
- Risk-based approach to coverage
- Enforcement trends
- Future-proofing strategy
Module 3. Architecture for Auditable Lineage
Design systems that automatically capture and preserve lineage for compliance validation
12 chapters in this module
- Components of lineage-capable infrastructure
- Integration with ETL/ELT pipelines
- Metadata tagging standards
- Automated capture methods
- Storage and retention policies
- Versioning and change tracking
- Scalability considerations
- Cloud-native patterns
- Hybrid environment challenges
- Data catalog integration
- Schema evolution handling
- Tooling selection framework
Module 4. Implementing Traceability Protocols
Deploy standardized processes to ensure consistent, verifiable data tracking across AI workflows
12 chapters in this module
- Defining traceability scope
- Critical data elements identification
- Lineage depth requirements
- Transformation mapping techniques
- Ownership assignment models
- Validation checkpoints
- Data quality linkage
- Real-time vs. batch capture
- Exception handling
- Reconciliation procedures
- Cross-system consistency
- Operational maintenance
Module 5. Governance Framework Integration
Embed lineage practices into existing data governance, risk, and compliance programs
12 chapters in this module
- Aligning with data governance councils
- RACI model for lineage ownership
- Policy development templates
- Control integration points
- Risk assessment linkage
- Compliance monitoring integration
- Training and awareness planning
- Cross-functional workflows
- Escalation procedures
- Metrics and KPIs
- Audit trail alignment
- Continuous improvement cycle
Module 6. Audit-Ready Documentation
Create clear, defensible records that satisfy internal and external auditors
12 chapters in this module
- Audit expectations by regulator type
- Documentation structure standards
- Lineage visualization best practices
- Narrative explanation templates
- Evidence packaging methods
- Version control for records
- Retention scheduling
- Access control for audit logs
- Third-party verification readiness
- Response preparation workflow
- Common auditor questions
- Mock audit simulation
Module 7. Cross-Functional Leadership Alignment
Lead coordination between compliance, engineering, data science, and IT teams
12 chapters in this module
- Stakeholder mapping
- Communication protocols
- Shared definitions and glossaries
- Meeting cadence design
- Conflict resolution strategies
- Incentive alignment
- Executive reporting formats
- Escalation paths
- Feedback integration
- Collaboration tooling
- Decision rights clarification
- Change management tactics
Module 8. AI Model Lineage Specifics
Extend lineage practices to model development, training data, and inference pipelines
12 chapters in this module
- Model development tracking
- Training data provenance
- Feature lineage mapping
- Versioning for models and datasets
- Hyperparameter tracking
- Bias assessment linkage
- Model card integration
- Drift detection triggers
- Retraining traceability
- Inference data logging
- Explainability support
- End-to-end validation
Module 9. Automation and Tooling Strategies
Evaluate and implement tools that reduce manual effort and increase accuracy in lineage capture
12 chapters in this module
- Open-source vs. commercial tools
- API integration patterns
- Metadata extraction methods
- Data catalog synchronization
- Workflow automation platforms
- Custom scripting considerations
- Vendor evaluation checklist
- Interoperability standards
- Change detection automation
- Alerting and monitoring
- Scalability testing
- Total cost of ownership
Module 10. Risk-Based Lineage Prioritization
Focus resources on high-impact data flows and systems based on compliance and operational risk
12 chapters in this module
- Risk categorization framework
- Criticality scoring model
- Impact vs. likelihood matrix
- Regulatory exposure mapping
- Customer harm potential
- Financial materiality thresholds
- Tiered documentation approach
- Resource allocation planning
- Coverage gap analysis
- Progressive enhancement model
- Staged rollout plan
- Success measurement
Module 11. Third-Party and Supply Chain Lineage
Extend traceability to external vendors, APIs, and outsourced data processing
12 chapters in this module
- Vendor due diligence
- Contractual requirements
- Third-party audit rights
- API data provenance
- Subprocessor tracking
- Data sharing agreements
- Compliance verification methods
- SLA alignment
- Security controls linkage
- Incident response coordination
- Exit strategy considerations
- Ongoing monitoring
Module 12. Sustaining and Scaling Lineage Practices
Ensure long-term adoption, evolution, and organizational embedding of data lineage standards
12 chapters in this module
- Change management principles
- Training program design
- Role-based access design
- Metrics dashboard creation
- Continuous monitoring setup
- Feedback loop integration
- Process refinement cycle
- Scaling to new business units
- Technology refresh planning
- Knowledge transfer protocols
- Leadership reporting
- Future trends preparation
How this maps to your situation
- New AI initiatives needing compliance oversight
- Organizations preparing for AI regulation audits
- Compliance teams integrating with data engineering
- Leaders building trustworthy AI governance frameworks
Before vs. after
Before
Unclear ownership, inconsistent documentation, and reactive responses to audit requests
After
Proactive, standardized, and defensible data lineage practices embedded across AI systems
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 hours of self-paced learning, designed for professionals balancing active roles
If nothing changes
Without structured data lineage, organizations risk delayed AI adoption, increased audit friction, regulatory penalties, and erosion of stakeholder trust due to unverifiable data provenance
How this compares to the alternatives
Unlike generic data governance courses, this program focuses specifically on AI data lineage with implementation-grade detail, compliance-specific frameworks, and audit-ready tooling, unavailable in broad-scope or awareness-level training
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals responsible for overseeing AI systems and ensuring data traceability in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior technical experience required?
No, concepts are explained in accessible language, with optional deep dives for technical contributors.
$199 one-time. Approximately 45 hours of self-paced learning, designed for professionals balancing active roles.
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