Tokenized Vector Data: Provenance, Permissions, and Deletion
Govern source text and derived embeddings with provenance, trusted authorization, versioned representations, deletion tests, and recovery controls.
VECTORTOKEN LAB / TAG
Governance articles
Keep permissions and provenance attached to evidence.
Governance follows the relationships between source documents, passages, embeddings, permissions, caches, and answers. This collection focuses on engineering controls that make those relationships inspectable. It does not equate numerical representation with anonymity or a database feature with complete application security.
Read the tokenized vector data guide for identity, revision, access, deletion, and recovery tests. Continue with the vector LLM architecture article when passages enter a generative workflow. Determine the eligible content before it reaches a user, a reranker, or a language model. Test with the actual application role and trusted request context rather than only an administrative connection.
Create synthetic lifecycle tests that retrieve a record, restrict it, replace it, delete it, and restore a collection. Check previews and cached answers as well as the active index. Keep logs and retained copies within their documented policies. A useful outcome is a traceable explanation of which source and authorization state supported a particular response.
THE READING PATH
02 articles to explore.
Vector LLM Architecture: Build RAG Around Evidence
Design retrieval-augmented generation around traceable evidence, controlled context, access boundaries, and separate retrieval and answer evaluations.