A tailored course, built for your situation
Production-Grade AI Data Lineage Practices for Compliance Officers
Master audit-ready AI governance with implementation-grade data lineage 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.
Compliance teams face increasing scrutiny as AI systems grow in complexity and regulatory exposure
The situation this course is for
Auditors are now asking for granular data provenance in AI decisions, but most compliance functions lack the technical frameworks to provide it. Traditional documentation doesn’t meet the depth required for model accountability, creating friction during reviews and increasing the cost of assurance.
Who this is for
Compliance officers, risk analysts, and governance leads in organizations deploying or overseeing AI systems, particularly in regulated environments
Who this is not for
This course is not for data scientists focused solely on model accuracy, nor for IT administrators managing infrastructure without governance responsibilities
What you walk away with
- Implement end-to-end data lineage tracking in AI pipelines
- Design compliance-ready documentation that satisfies auditor requirements
- Map regulatory expectations to technical data governance controls
- Integrate lineage practices into model development lifecycles
- Reduce audit preparation time with pre-built traceability structures
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Introduce core concepts of data provenance, traceability, and their role in AI governance
12 chapters in this module
- Defining data lineage in AI contexts
- Distinguishing lineage from metadata
- Regulatory drivers shaping lineage needs
- The role of compliance in data tracking
- Lineage as a governance asset
- Common misconceptions about traceability
- Scope boundaries in AI systems
- Linking data to model inputs
- Stakeholder responsibilities
- Baseline assessment tools
- Maturity models for lineage practice
- Getting started: first steps
Module 2. Data Provenance in Practice
Apply provenance principles to real-world AI workflows
12 chapters in this module
- Tracking data from ingestion to output
- Versioning datasets effectively
- Capturing data transformations
- Mapping upstream dependencies
- Handling third-party data sources
- Documenting data quality checks
- Timestamping data events
- Linking data to training cycles
- Identifying data drift signals
- Maintaining chain of custody
- Automating provenance capture
- Validating data lineage records
Module 3. Compliance Framework Alignment
Align data lineage practices with major regulatory expectations
12 chapters in this module
- Mapping to GDPR requirements
- Meeting CCPA data tracking mandates
- Preparing for AI Act compliance
- Aligning with NIST AI RMF
- Integrating with ISO 38507
- Supporting SOC 2 Type II audits
- Demonstrating due diligence
- Building regulatory narratives
- Preparing for supervisory inquiries
- Documenting decision trails
- Handling data subject requests
- Proving non-discriminatory data use
Module 4. Technical Architecture for Lineage
Design systems that natively support traceability
12 chapters in this module
- Lineage-aware data pipelines
- Choosing metadata storage solutions
- Implementing data catalogs
- Integrating with MLOps tools
- Using open standards like OpenLineage
- Designing lineage APIs
- Tagging data at ingestion
- Automated lineage graph generation
- Handling streaming data
- Scalability considerations
- Security controls for lineage data
- Ensuring data lineage integrity
Module 5. Cross-Functional Collaboration
Bridge gaps between compliance, data science, and engineering
12 chapters in this module
- Defining shared ownership models
- Establishing data stewardship roles
- Facilitating team handoffs
- Creating common terminology
- Running lineage workshops
- Documenting inter-team agreements
- Managing conflicting priorities
- Building feedback loops
- Scaling collaboration across teams
- Measuring cross-functional effectiveness
- Resolving disputes over data ownership
- Maintaining alignment over time
Module 6. Audit Preparation and Response
Prepare for audits with confidence using lineage evidence
12 chapters in this module
- Anticipating auditor questions
- Organizing lineage documentation
- Creating audit packs
- Demonstrating data integrity
- Responding to findings
- Preparing for model revalidation
- Handling data deletion requests
- Proving data consistency
- Supporting impact assessments
- Streamlining evidence retrieval
- Reducing audit burden
- Building trust through transparency
Module 7. Automated Lineage Capture
Leverage tooling to reduce manual effort in tracking
12 chapters in this module
- Evaluating lineage tools
- Integrating with data platforms
- Automating metadata extraction
- Parsing model training logs
- Capturing pipeline configurations
- Monitoring lineage coverage
- Validating auto-captured data
- Handling exceptions
- Scaling automation
- Maintaining tool reliability
- Cost-benefit of automation
- Future-proofing tool choices
Module 8. Data Lineage Validation
Ensure lineage records are accurate and trustworthy
12 chapters in this module
- Designing validation checks
- Testing lineage completeness
- Verifying data links
- Auditing lineage infrastructure
- Sampling for accuracy
- Detecting gaps in tracking
- Correcting lineage errors
- Maintaining validation logs
- Involving third parties
- Benchmarking against peers
- Improving over time
- Reporting validation outcomes
Module 9. Scaling Across Organizations
Extend lineage practices beyond pilot projects
12 chapters in this module
- Developing enterprise standards
- Creating governance policies
- Training teams at scale
- Rolling out tools organization-wide
- Managing change resistance
- Aligning with enterprise architecture
- Setting KPIs for lineage quality
- Reporting to leadership
- Budgeting for scalability
- Integrating with legacy systems
- Phasing implementation
- Sustaining long-term adoption
Module 10. Ethical and Fairness Considerations
Use lineage to support ethical AI outcomes
12 chapters in this module
- Tracing data to fairness audits
- Identifying biased data sources
- Monitoring for discriminatory patterns
- Supporting explainability
- Ensuring consent compliance
- Protecting vulnerable groups
- Documenting ethical reviews
- Linking lineage to impact assessments
- Enabling redress pathways
- Promoting algorithmic accountability
- Balancing transparency and privacy
- Building public trust
Module 11. Incident Response and Recovery
Use lineage during outages and investigations
12 chapters in this module
- Diagnosing data-related failures
- Tracing errors to source
- Recovering corrupted data paths
- Supporting root cause analysis
- Documenting incident timelines
- Improving systems post-incident
- Preparing for regulatory scrutiny
- Communicating with stakeholders
- Reducing recurrence risk
- Integrating with security teams
- Building response playbooks
- Learning from near-misses
Module 12. Future-Proofing Lineage Practice
Stay ahead of evolving technical and regulatory landscapes
12 chapters in this module
- Anticipating regulatory changes
- Adapting to new AI paradigms
- Integrating with emerging standards
- Planning for model complexity
- Supporting decentralized data
- Preparing for edge AI
- Handling synthetic data
- Evolving with privacy laws
- Investing in team capability
- Measuring long-term value
- Sharing best practices
- Leading in governance innovation
How this maps to your situation
- When launching a new AI system
- During audit preparation cycles
- After regulatory changes
- During incident investigations
Before vs. after
Before
Compliance teams operate reactively, scrambling for evidence when auditors arrive, relying on incomplete documentation and tribal knowledge.
After
Compliance functions proactively build verifiable data trails into AI systems, reducing audit stress and demonstrating leadership in governance.
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 hours of self-paced learning, designed to fit around professional responsibilities.
If nothing changes
Without structured data lineage, organizations face longer audit cycles, higher compliance costs, and increased exposure to regulatory penalties as AI oversight intensifies.
How this compares to the alternatives
Unlike generic AI ethics courses or technical data engineering programs, this course is specifically tailored to compliance officers, combining regulatory insight with implementation-grade technical detail to bridge the gap between policy and practice.
Frequently asked
Who is this course designed for?
This course is for compliance officers, risk analysts, and governance professionals responsible for overseeing AI systems in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical background required?
No deep coding skills are needed, but familiarity with data concepts and regulatory frameworks is assumed.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed to fit around professional 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