A tailored course, built for your situation
Implementation-Focused AI Data Lineage Practices for Compliance Officers
Master compliant, auditable AI systems through operational data traceability
$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.
AI systems are advancing faster than compliance frameworks can keep up, creating ambiguity in accountability and audit readiness.
The situation this course is for
Compliance officers are expected to oversee AI deployments without clear, actionable methods to verify data provenance or model decision trails. This leads to reactive audits, strained cross-functional relationships, and governance gaps that emerge only after deployment.
Who this is for
Business and technology professionals in compliance, risk, and governance roles who interface with data science and engineering teams and need to implement practical AI oversight frameworks.
Who this is not for
This course is not for data scientists focused solely on model development, nor for executives seeking high-level AI strategy summaries without implementation detail.
What you walk away with
- Apply a structured framework to map data lineage across AI pipelines
- Identify critical control points for compliance in data ingestion, transformation, and model inference
- Produce auditable documentation that satisfies regulatory scrutiny
- Collaborate effectively with engineering teams using shared lineage standards
- Implement automated lineage tracking that scales with AI deployment velocity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Establish core concepts and compliance motivations for data traceability in AI systems.
12 chapters in this module
- Defining data lineage in the context of AI
- Regulatory drivers shaping lineage expectations
- The role of compliance in AI governance
- Distinguishing lineage from metadata management
- Key stakeholders in lineage implementation
- Common misconceptions about data traceability
- Lineage as a foundation for audit readiness
- Linking lineage to model risk management
- Data provenance vs. data pedigree
- Scope definition for lineage initiatives
- Baseline maturity assessment
- Setting implementation objectives
Module 2. Data Flow Mapping for AI Systems
Learn to diagram end-to-end data journeys from source to insight.
12 chapters in this module
- Identifying data sources and ingestion points
- Mapping data transformations across pipelines
- Tracking data movement through staging layers
- Documenting feature engineering steps
- Visualizing model input dependencies
- Capturing data quality checks in flow
- Handling real-time vs batch data streams
- Integrating metadata from ETL tools
- Using process diagrams for clarity
- Versioning data flow documentation
- Cross-referencing with data dictionaries
- Validating flow accuracy with engineering
Module 3. Automated Lineage Capture Tools
Evaluate and integrate tooling that automatically tracks data relationships.
12 chapters in this module
- Overview of lineage tool categories
- Agent-based vs API-driven collection
- Integration with data warehouses
- Compatibility with cloud platforms
- Support for open metadata standards
- Evaluating tool scalability
- Assessing accuracy and completeness
- Vendor selection criteria
- Pilot deployment strategies
- Monitoring tool performance
- Handling schema changes in lineage
- Ensuring auditability of tool outputs
Module 4. Governance Policies for Data Lineage
Develop enforceable standards and ownership models.
12 chapters in this module
- Defining lineage ownership roles
- Establishing data stewardship frameworks
- Creating lineage documentation standards
- Setting retention and access rules
- Linking lineage to data classification
- Incorporating lineage into change control
- Audit preparation procedures
- Policy enforcement mechanisms
- Training requirements for teams
- Version control for policies
- Cross-functional policy alignment
- Measuring policy adherence
Module 5. Cross-Functional Coordination
Align compliance, engineering, and data science on lineage implementation.
12 chapters in this module
- Identifying shared objectives
- Building joint implementation teams
- Establishing communication protocols
- Negotiating priorities across functions
- Creating shared documentation standards
- Resolving ownership disputes
- Scheduling cross-team reviews
- Integrating lineage into SDLC
- Coordinating incident response
- Aligning on tooling choices
- Managing conflicting timelines
- Celebrating joint milestones
Module 6. Audit Preparation and Evidence
Generate defensible, organized records for internal and external review.
12 chapters in this module
- Anticipating auditor questions
- Organizing lineage artifacts
- Creating audit-ready narratives
- Documenting exception handling
- Preparing model validation packages
- Demonstrating data quality assurance
- Showing compliance with policies
- Responding to findings
- Maintaining versioned evidence
- Using lineage in root cause analysis
- Streamlining auditor access
- Reducing audit cycle time
Module 7. Scalability and Maintenance
Design systems that sustain lineage integrity as AI use grows.
12 chapters in this module
- Planning for increasing data volume
- Handling model retraining cycles
- Managing lineage for A/B testing
- Updating documentation at scale
- Automating validation checks
- Monitoring data drift impact
- Refreshing lineage for new regulations
- Integrating lineage into CI/CD
- Versioning lineage records
- Archiving legacy system data
- Optimizing storage costs
- Ensuring long-term accessibility
Module 8. Data Quality Integration
Embed data quality checks within lineage tracking.
12 chapters in this module
- Defining quality thresholds
- Linking quality to lineage events
- Tracking data cleansing steps
- Documenting imputation logic
- Monitoring for anomalies
- Alerting on quality breaches
- Validating transformation accuracy
- Reporting quality metrics
- Integrating with data observability
- Handling missing data documentation
- Quality assurance in real-time
- Auditing quality control processes
Module 9. Model Development Integration
Embed lineage practices into the model lifecycle.
12 chapters in this module
- Capturing training data provenance
- Documenting feature selection rationale
- Tracking hyperparameter choices
- Versioning model artifacts
- Linking models to business use cases
- Recording validation results
- Integrating with MLOps tools
- Handling model retraining triggers
- Auditing model performance decay
- Managing model deployment records
- Coordinating with data scientists
- Ensuring reproducibility
Module 10. Regulatory Alignment
Map lineage practices to key compliance frameworks.
12 chapters in this module
- GDPR data provenance requirements
- CCPA lineage expectations
- HIPAA data tracking rules
- SOX controls for AI systems
- Basel III implications
- SEC guidance on model governance
- Aligning with NIST AI standards
- Mapping to ISO 38507
- Preparing for future regulations
- Documenting compliance mappings
- Handling jurisdictional differences
- Updating for regulatory changes
Module 11. Incident Response and Remediation
Use lineage to accelerate investigation and correction.
12 chapters in this module
- Triggering incident workflows
- Tracing data corruption sources
- Identifying impacted models
- Documenting root cause analysis
- Coordinating remediation steps
- Validating fixes with lineage
- Reporting to oversight bodies
- Updating policies post-incident
- Conducting post-mortems
- Strengthening controls
- Communicating with stakeholders
- Preventing recurrence
Module 12. Future-Proofing Your Practice
Adapt to emerging technologies and evolving expectations.
12 chapters in this module
- Anticipating AI regulatory trends
- Preparing for autonomous systems
- Adapting to new data sources
- Integrating synthetic data tracking
- Handling federated learning
- Managing edge AI deployments
- Adopting blockchain for audit trails
- Leveraging zero-knowledge proofs
- Upskilling teams proactively
- Benchmarking against peers
- Investing in tool evolution
- Leading governance innovation
How this maps to your situation
- New AI governance mandate in place
- Recent audit raised data provenance concerns
- Scaling AI deployments across business units
- Preparing for regulatory examination
Before vs. after
Before
Uncertainty in tracing data origins, reactive compliance posture, fragmented coordination with technical teams.
After
Confident oversight of AI systems, proactive documentation, and structured collaboration with engineering and data science.
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 steady implementation alongside regular responsibilities.
If nothing changes
Without structured data lineage, organizations risk non-compliance findings, extended audit cycles, and erosion of trust in AI-driven decisions, challenges that grow harder to resolve as AI adoption accelerates.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance summaries, this program delivers implementation-grade practices specifically for data lineage, combining technical depth with governance pragmatism.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals who work alongside data science and engineering teams and need practical methods to ensure AI accountability.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical background required?
No deep coding skills needed, concepts are explained in accessible terms with implementation pathways for non-technical leaders.
$199 one-time. Approximately 3-4 hours per module, designed for steady implementation alongside regular responsibilities..
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