Privacy Tradeoffs of Local RAG

Local LLM tools with built-in RAG protect prompt privacy but can leak sensitive data through insecure document parsing and vector stores.

WHY IT MATTERS

Document ingestion pipelines often rely on external API services for OCR parsing or remote vector embedding generation. Verifying local RAG architecture prevents accidental document leakage to cloud endpoints.

GO DEEPER

• Local embedding models: Ensure vector embeddings are generated using local models like bge-large-en.
• Document parser telemetry: Verify PDF parsing libraries do not send document content to external servers.
• Vector store security: Encrypt local vector database stores (Chroma, LanceDB) stored on disk.
• Chunk caching leaks: Plaintext document chunks saved in temporary cache folders expose confidential text.
• Multi-document permissions: Built-in local RAG tools often lack granular document-level access control.
• Memory footprint expansion: Document ingestion increases system RAM and VRAM requirements during inference.

THE BOTTOM LINE

Use strictly local embedding models and encrypted vector stores to keep RAG document pipelines completely private.