verifying-training-data-erasure

ML data erasure verification checks raw datasets, embeddings, vector stores, model weights, inference caches, and backups for lingering subject data. It supports GDPR, CCPA, and HIPAA right-to-erasure requests by assembling structured DSR proof bundles for privacy review.

DSR erasure documentation requires stage-by-stage deletion evidence across raw datasets, embeddings, model weights, caches, and backups. The proof bundle includes commands run, outputs, sign-offs, residual-risk statements, and retention dates for privacy review.

Data erasure verification supports HIPAA, CCPA, and GDPR compliance workflows for right-to-erasure requests. It verifies deletion across raw datasets, embeddings, model weights, caches, backups, and deployed fine-tunes to produce audit-ready proof bundles.

Handling residual-risk when erasing training data from model weights requires explicit model-weight strategy selection and residual-risk acknowledgement. The verification process documents these statements in the DSR proof bundle alongside deletion evidence and retention dates.

ML data erasure requires checking inference caches and deployed fine-tunes because subject-derived information can persist in these stages after raw dataset deletion. Full verification ensures no lingering subject data remains across the entire ML pipeline.