Data lineage has long been a concept in data engineering, tracing the origin and transformation of data as it moves through pipelines and systems. AI data lineage extends this to the model training and inference context, where the provenance of training data has significant legal, ethical, and operational implications.
For privacy and data governance, AI data lineage addresses a critical question: was the data used to train or inform this model collected with appropriate consent, under a valid legal basis, and for a compatible purpose? Under the GDPR's purpose limitation principle, data collected for one purpose cannot be repurposed for model training without a legal basis for the new use.
For risk management, AI data lineage enables organizations to assess whether sensitive, proprietary, or personal data has been embedded in model weights, creating the possibility of that information surfacing in model outputs. If a dataset is found to be biased, mislabeled, or improperly licensed, organizations need to know which models were trained on it and what outputs those models produced.