A tailored course, built for your situation
Mid-Market AI Data Lineage Practices for Compliance Officers
Implement audit-ready data traceability for AI systems in regulated mid-market environments
$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.
Manual, fragmented data tracking undermines trust in AI outputs and slows audits
The situation this course is for
Compliance teams are expected to validate AI-driven decisions but often lack clear visibility into data origins, transformations, and dependencies. Without structured lineage, audits take longer, remediation is reactive, and cross-functional alignment stalls.
Who this is for
Compliance, risk, and governance professionals in mid-market firms implementing or overseeing AI systems with regulatory exposure
Who this is not for
Enterprise architects at Fortune 500 firms with dedicated AI governance teams or practitioners focused only on non-regulated AI use cases
What you walk away with
- Design and deploy a compliant AI data lineage framework aligned with regulatory expectations
- Map data flows from source to AI output with audit-ready documentation
- Integrate lightweight tooling that works within mid-market resource constraints
- Collaborate effectively with data engineering and IT teams using standardized lineage protocols
- Reduce audit preparation time by 50% with proactive lineage documentation
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage in Regulated Environments
Establish core concepts, regulatory drivers, and scope boundaries for mid-market compliance teams.
12 chapters in this module
- Defining data lineage in AI systems
- Key regulators and guidance shaping lineage expectations
- Differences between enterprise and mid-market lineage needs
- Common AI use cases requiring lineage tracking
- Regulatory touchpoints: privacy, fairness, model risk
- Lineage as a trust enabler, not just compliance
- Scope definition: what to include and exclude
- Stakeholder mapping: legal, IT, data, compliance
- Baseline assessment of current lineage maturity
- Setting realistic implementation goals
- Common misconceptions about lineage complexity
- Preparing for cross-functional collaboration
Module 2. Regulatory Expectations and Compliance Mapping
Translate GDPR, CCPA, model risk management, and industry-specific rules into lineage requirements.
12 chapters in this module
- GDPR and the right to explanation
- CCPA and data transparency obligations
- Model Risk Management (MRM) and SR 11-7 alignment
- Sector-specific rules: finance, healthcare, insurance
- Mapping regulations to data tracking requirements
- Documentation standards for auditors
- Proactive vs reactive compliance postures
- Handling cross-border data flows
- Demonstrating continuous compliance
- Audit trail expectations for AI decisions
- Engaging legal counsel on lineage scope
- Building a compliance playbook appendix
Module 3. Data Provenance and Source Tracking
Capture and verify origin points for training and inference data across systems.
12 chapters in this module
- Identifying primary and secondary data sources
- Metadata tagging standards for provenance
- Automated source logging techniques
- Handling third-party and vendor data
- Validating data ownership and licensing
- Time-series data and versioning
- Immutable logging for source integrity
- Data ingestion audit points
- Schema change detection
- Source-to-ingestion lineage mapping
- Documenting data collection methods
- Handling legacy system inputs
Module 4. Transformation Logic and Pipeline Visibility
Track data modifications across ETL, feature engineering, and preprocessing steps.
12 chapters in this module
- Mapping data transformation stages
- Logging feature engineering decisions
- Version control for transformation code
- Dependency tracking between pipeline stages
- Handling real-time vs batch processing
- Documenting data quality checks
- Annotating business logic in transformations
- Capturing threshold and rule changes
- Linking transformations to model inputs
- Audit-ready transformation logs
- Handling open-source library dependencies
- Pipeline ownership and access controls
Module 5. Model Input-Output Linking and Traceability
Connect specific model outputs to input data, parameters, and training versions.
12 chapters in this module
- Tagging model inputs with lineage IDs
- Storing input snapshots for reproducibility
- Model versioning and registry integration
- Linking decisions to training data subsets
- Tracking hyperparameter configurations
- Logging inference request metadata
- Time-stamped output records
- Handling batch vs real-time inference
- Data drift detection and response
- Output validation against input rules
- Audit trails for model updates
- Retirement and deprecation tracking
Module 6. Toolchain Integration for Mid-Market Realities
Leverage existing tools and lightweight solutions for cost-effective lineage capture.
12 chapters in this module
- Assessing current tech stack for lineage capability
- Integrating with data warehouses and lakes
- Using metadata managers and catalog tools
- Open-source lineage tools: strengths and gaps
- Low-code/no-code automation options
- APIs for cross-system data tracking
- Logging strategies without full MLOps
- Spreadsheets and databases as interim tools
- Cloud provider-native lineage features
- Vendor selection criteria for mid-market
- Building internal lineage dashboards
- Maintaining tooling with limited IT bandwidth
Module 7. Documentation Standards and Audit Readiness
Create clear, consistent, and defensible lineage records for internal and external review.
12 chapters in this module
- Standardizing documentation formats
- Creating lineage diagrams for non-technical reviewers
- Version-controlled documentation repositories
- Automated report generation
- Preparing for internal audits
- Responding to regulator inquiries
- Redacting sensitive data in reports
- Maintaining documentation over time
- Cross-referencing policies and procedures
- Using templates for consistency
- Stakeholder review cycles
- Archiving and retention policies
Module 8. Cross-Functional Collaboration Frameworks
Align compliance, data, engineering, and business teams on lineage responsibilities.
12 chapters in this module
- Defining RACI matrices for lineage work
- Establishing data stewardship roles
- Creating shared definitions and glossaries
- Scheduling cross-team syncs
- Handling conflicting priorities
- Translating compliance needs to technical teams
- Documenting decisions and rationale
- Managing change across departments
- Onboarding new team members
- Escalation paths for gaps
- Building trust through transparency
- Measuring collaboration effectiveness
Module 9. Change Management and Ongoing Maintenance
Sustain lineage accuracy as data, models, and systems evolve.
12 chapters in this module
- Change detection workflows
- Versioning data and model updates
- Automated alerts for schema changes
- Handling system upgrades and migrations
- Deprecation of legacy data sources
- Model retraining and lineage updates
- User access and permission changes
- Incident response and lineage gaps
- Quarterly lineage health checks
- Updating documentation after changes
- Tracking technical debt in lineage
- Planning for scalability
Module 10. Risk Assessment and Gap Analysis
Identify and prioritize lineage vulnerabilities before audits or incidents.
12 chapters in this module
- Conducting lineage risk workshops
- Mapping high-risk AI use cases
- Assessing data criticality and sensitivity
- Evaluating automation coverage
- Identifying manual process dependencies
- Third-party risk and vendor lineage
- Gap scoring and prioritization
- Remediation planning
- Benchmarking against industry peers
- Reporting risks to leadership
- Integrating findings into risk registers
- Tracking remediation progress
Module 11. Scaling Lineage Across Use Cases
Extend initial efforts to cover multiple AI models and business functions.
12 chapters in this module
- Prioritizing use cases for rollout
- Creating reusable lineage templates
- Standardizing tooling across teams
- Onboarding new models efficiently
- Centralizing lineage oversight
- Decentralized execution with consistency
- Managing multiple timelines and owners
- Sharing best practices across units
- Handling department-specific requirements
- Scaling documentation processes
- Budgeting for expansion
- Measuring program maturity
Module 12. Future-Proofing and Emerging Trends
Anticipate evolving requirements and prepare for next-generation AI governance.
12 chapters in this module
- Monitoring regulatory developments
- Preparing for AI-specific legislation
- Adopting emerging standards (e.g., ISO, NIST)
- Integrating with broader ESG reporting
- Handling generative AI lineage
- Federated learning and distributed data
- Blockchain for immutable logs
- AI audit certifications
- Building internal training programs
- Engaging with industry consortia
- Succession planning for governance roles
- Positioning compliance as a strategic function
How this maps to your situation
- Implementing first formal AI data tracking process
- Preparing for external audit or regulatory review
- Scaling AI use cases across departments
- Reducing manual work in compliance reporting
Before vs. after
Before
Manual tracking, inconsistent documentation, and reactive responses to audit requests
After
Structured, automated, and audit-ready AI data lineage that builds trust and reduces compliance overhead
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 completion within 12 weeks with weekly pacing.
If nothing changes
Without a formal approach, teams remain dependent on tribal knowledge, increasing audit risk, slowing AI adoption, and creating avoidable rework during regulatory reviews.
How this compares to the alternatives
Unlike generic data governance courses, this program focuses exclusively on AI data lineage in mid-market contexts, offering specific templates, tool integration guidance, and compliance mapping not found in broader or enterprise-focused programs.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals in mid-market firms implementing AI systems under regulatory scrutiny.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for non-technical compliance professionals?
Yes. The course is written for compliance leaders who need to understand, oversee, and validate technical processes without requiring coding skills.
$199 one-time. Approximately 3-4 hours per module, designed for completion within 12 weeks with weekly pacing..
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