A tailored course, built for your situation
Mid-Market AI Data Lineage Practices for Compliance Officers
Implement compliant, auditable AI systems with precision and confidence
$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 lack clear, actionable frameworks to track AI data flows across evolving systems
The situation this course is for
As AI adoption grows, compliance officers are expected to validate data origins, transformations, and access controls, yet most operate without standardized lineage practices. This leads to inconsistent audits, delayed approvals, and reactive fixes. The gap isn’t policy, it’s implementation.
Who this is for
Compliance and risk professionals in mid-market firms implementing AI systems and needing auditable, repeatable data lineage practices
Who this is not for
Executives seeking high-level overviews or engineers focused solely on data pipeline code
What you walk away with
- Build end-to-end AI data lineage frameworks aligned with compliance standards
- Integrate lineage practices into existing governance workflows
- Prepare for audits with documented, verifiable data trails
- Collaborate effectively with data and AI teams using shared terminology and tools
- Reduce review cycles by enabling proactive compliance validation
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
- Defining data lineage in AI contexts
- Differences between metadata and lineage
- Regulatory drivers shaping lineage needs
- Scope of lineage across data ingestion
- Tracking transformations in preprocessing
- Model input traceability
- Output and decision tracking
- Linking lineage to accountability
- Common misconceptions and pitfalls
- Compliance officer’s role in lineage oversight
- Interfacing with data engineering teams
- Assessing current lineage maturity
Module 2. Regulatory Alignment and Standards
Map lineage practices to current compliance frameworks and expectations
12 chapters in this module
- GDPR and data provenance requirements
- CCPA and consumer data tracking
- HIPAA considerations for health AI
- SOX controls and data integrity
- ISO standards for information governance
- NIST AI Risk Management Framework
- EU AI Act implications
- Cross-border data flow compliance
- Industry-specific regulatory expectations
- Audit expectations for lineage documentation
- Preparing for regulator inquiries
- Building compliance-by-design into lineage
Module 3. Data Flow Mapping Techniques
Learn systematic approaches to diagramming and validating data movement
12 chapters in this module
- Identifying data sources and entry points
- Visualizing ingestion pipelines
- Documenting transformation logic
- Tracking data storage locations
- Mapping model training inputs
- Linking features to source systems
- Handling batch vs real-time flows
- Versioning data pipelines
- Using diagrams for stakeholder alignment
- Validating flow accuracy with teams
- Maintaining up-to-date maps
- Automating flow documentation
Module 4. Tooling and Integration Strategies
Evaluate and deploy lineage tools compatible with mid-market constraints
12 chapters in this module
- Open-source vs commercial tools
- Metadata extraction methods
- Integrating with data warehouses
- Connecting to ETL processes
- API-based lineage capture
- Model registry integration
- Handling unstructured data
- Scaling with data growth
- Vendor selection criteria
- Implementation timelines
- Team training requirements
- Pilot project design
Module 5. Cross-Functional Collaboration Models
Develop workflows that bridge compliance, data, and AI teams
12 chapters in this module
- Defining shared ownership
- Establishing RACI matrices
- Scheduling lineage reviews
- Documenting handoff points
- Aligning on terminology
- Resolving ownership conflicts
- Creating feedback loops
- Incorporating developer input
- Engaging legal and privacy teams
- Managing change across teams
- Building trust through transparency
- Sustaining collaboration long-term
Module 6. Audit Preparation and Evidence Packaging
Structure lineage documentation for internal and external review
12 chapters in this module
- Organizing lineage artifacts
- Creating audit-ready packages
- Version control for documentation
- Linking lineage to control assertions
- Demonstrating data integrity
- Showing model input consistency
- Documenting data quality checks
- Proving data access compliance
- Responding to auditor questions
- Maintaining evidence repositories
- Preparing for surprise audits
- Streamlining evidence updates
Module 7. Change Management and Lineage Maintenance
Ensure lineage stays current as systems evolve
12 chapters in this module
- Tracking schema changes
- Handling pipeline updates
- Model versioning and lineage
- Deprecating legacy systems
- Onboarding new data sources
- Managing team turnover
- Automated change detection
- Alerting on data flow breaks
- Scheduling regular reviews
- Updating documentation workflows
- Versioning lineage records
- Archiving outdated lineage
Module 8. Risk Assessment Using Lineage Data
Leverage lineage to identify, assess, and mitigate AI risks
12 chapters in this module
- Identifying high-risk data sources
- Mapping sensitive data flows
- Assessing bias propagation paths
- Evaluating data quality risks
- Detecting unauthorized access
- Monitoring for data drift
- Linking lineage to model risk
- Prioritizing remediation efforts
- Documenting risk decisions
- Reporting risk posture
- Integrating with GRC tools
- Updating risk models dynamically
Module 9. Policy Development and Enforcement
Create and operationalize data lineage policies
12 chapters in this module
- Defining lineage policy scope
- Setting data documentation standards
- Establishing accountability rules
- Enforcing policy adherence
- Auditing policy compliance
- Handling policy exceptions
- Updating policies with new regulations
- Training teams on policy requirements
- Measuring policy effectiveness
- Incorporating feedback loops
- Scaling policy across departments
- Documenting policy evolution
Module 10. Scaling Lineage Across AI Portfolios
Extend practices from single models to enterprise-wide systems
12 chapters in this module
- Assessing portfolio complexity
- Prioritizing high-impact models
- Standardizing lineage practices
- Creating reusable templates
- Training additional teams
- Centralizing documentation
- Building internal support
- Measuring adoption rates
- Optimizing for efficiency
- Managing multi-team coordination
- Integrating with AI governance
- Sustaining executive support
Module 11. Ethical and Reputational Considerations
Connect lineage to broader organizational trust and ethics
12 chapters in this module
- Demonstrating responsible AI use
- Building stakeholder trust
- Supporting ethical review boards
- Documenting fairness assessments
- Ensuring transparency commitments
- Responding to public inquiries
- Handling media scrutiny
- Aligning with ESG goals
- Reporting on ethical impact
- Preventing reputational risk
- Communicating lineage value
- Sustaining ethical accountability
Module 12. Future-Proofing and Continuous Improvement
Adapt lineage practices to emerging technologies and regulations
12 chapters in this module
- Monitoring regulatory changes
- Tracking new AI techniques
- Updating lineage for new tools
- Incorporating feedback
- Benchmarking against peers
- Investing in skill development
- Anticipating data complexity
- Planning for AI scale
- Adopting new standards
- Revising implementation playbook
- Sustaining leadership engagement
- Closing the improvement loop
How this maps to your situation
- Building foundational understanding
- Aligning with compliance demands
- Implementing technical practices
- Sustaining long-term adoption
Before vs. after
Before
Compliance efforts rely on fragmented documentation and reactive responses to audit requests
After
Compliance teams proactively manage AI data flows with clear, verifiable lineage and reduced review cycles
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 hours per week over 12 weeks to complete all modules and apply templates
If nothing changes
Organizations without structured data lineage face longer audit cycles, increased scrutiny, and growing operational friction as AI systems scale
How this compares to the alternatives
Unlike generic compliance webinars or engineering-focused data talks, this course is built specifically for mid-market compliance officers implementing AI systems, offering actionable, cross-functional guidance not available in off-the-shelf content.
Frequently asked
Who is this course designed for?
Compliance officers and risk professionals in mid-market organizations implementing AI systems who need practical, auditable data lineage frameworks.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is entirely text-based with downloadable templates and a hand-built implementation playbook.
$199 one-time. Approximately 3 hours per week over 12 weeks to complete all modules and apply templates.
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