Security-first vector pipelines are moving from idea to practice. Enterprises encrypt embeddings before ingestion [1]. Access is gated with reBAC-protected contexts [2]. Governance-friendly ranking lets small teams tune results without a heavyweight ML setup [3].
In practice, the encryption-first angle is championed by Redpanda and CyborgDB, who advocate encrypting vector embeddings prior to ingestion [1].
For access control, open-source reBAC-protected RAG pairs with SQLite-vec to keep vector contexts behind policies [2].
On governance, CoderSwap offers a dirt-cheap custom ranking layer that does hybrid (vector + keyword) search with explainable scoring, plus governance features like versioning/rollback and a no-ML-team requirement. Free tier: 100MB, 10k calls/month [3].
These threads show a clear trend: encrypt by default, layer fine-grained access control, and add governance-friendly ranking that non-ML teams can actually operate.
References
Encrypting vector embeddings prior to data ingestion (Redpanda, Cyborg)
Discusses encrypting vector embeddings before data ingestion using Redpanda and CyborgDB for secure streaming in enterprise AI and privacy improvements
View sourceShow HN: Secure AI contexts with open source reBAC-protected RAG and SQLite-vec
Show HN about secure AI contexts using open source reBAC-protected RAG and SQLite-vec.
View sourceShow HN: Dirt-cheap custom ranking layer for your vector search
cheap ranking layer atop DB for vector search; hybrid scoring, plain English rules, governance, explainability, no ML team needed
View source