How do you design clustering systems with strict data lineage and auditability requirements?

Data lineage is enforced by tracking dataset versions, feature transformations, and model training provenance.

Auditability requires tracking every step in the clustering pipeline—from raw data ingestion to feature engineering to model training. Each dataset version is immutable, and transformations are logged. Metadata systems store lineage graphs that link input data, feature pipelines, and model outputs. This is critical in regulated industries like finance and healthcare.