Right to Erasure

Erasure is straightforward in a database and difficult in an AI stack, because personal data propagates into artefacts that are not obviously records: vector embeddings, cached answers, conversation histories, evaluation sets and logs. A deletion that removes the source document while leaving its embedding in the index has not honored the request — the content remains retrievable in semantic form. Meeting the obligation therefore requires knowing every derived location, which in turn requires that derivation be tracked rather than incidental. Organizations that cannot enumerate where a record's derivatives live cannot make a defensible erasure claim.

Because indexing is record-level and the index resides in the organization's environment, the derivatives of a given record are enumerable rather than dispersed. Removing the record removes its basis for retrieval, and the cached answers derived from it are invalidated against the same lifecycle rather than persisting independently.