AI governance is harder when teams cannot explain where important inputs came from or how they reached the system. A practical lineage record follows the data that materially influences an AI-enabled use case without trying to duplicate every technical detail of a data platform.
Start with material data sources
Record the datasets, business systems, documents, retrieval indexes, user inputs, external feeds, and reference sources that influence the use case. Identify owners and the purpose each source serves.
Capture meaningful transformations
Note filtering, aggregation, labeling, normalization, enrichment, redaction, retrieval, or other processing that can change how the data is interpreted by the AI system.
Track versions and change points
Record when an important source or transformation changes and connect that event to model or use-case review triggers. A new source can change behavior even when the underlying model is unchanged.
Link lineage to controls
Connect the record to access controls, data-quality checks, retention practices, monitoring, incident records, and review evidence where applicable.
Do not use lineage as a substitute for legal analysis
A lineage record can show where data came from, but it does not establish whether collection, use, retention, transfer, or automated decision-making is legally permitted or professionally appropriate.
Related knowledge
Use this with AI Use-Case Inventories, AI Model Change Reviews, AI Monitoring Thresholds, and AI Governance Control Owners.
References
This resource supports operational governance. Legal, regulatory, privacy, clinical, financial, employment, safety, accessibility, and other professional determinations require qualified review.