Deploying RAG in the enterprise means protecting sensitive documents, enforcing who can see what, and logging access for compliance. Retrieval must respect the same permissions as source systems—otherwise answers may leak confidential content.
Separate indexes, API keys, or namespaces per customer or business unit. Multi-tenant platforms should guarantee no cross-tenant retrieval. WeKnora’s multi-tenant design targets this pattern.
Attach ACL tags to chunks at index time. At query time, filter vector search by the user’s allowed labels so only permitted passages are candidates. This is essential for HR, legal, and financial content.
Log queries, retrieved document ids, and model versions. Auditors often need evidence of who asked what and which sources were used—especially alongside chat with documents features.
Some regions require data to stay in-country. Self-hosted LLMs and embedding models, or regional API endpoints, reduce compliance risk. Review subprocessors when using third-party model APIs.
Untrusted documents can contain instructions meant to manipulate the model. Combine retrieval filters, system prompts that prioritize trusted context, and monitoring for anomalous queries.
Harden APIs with authentication, rate limits, and TLS. Rotate keys and follow dependency updates for parsers and vector stores. See documentation for deployment hardening.
Related: AI knowledge base software, How to build a RAG application, WeKnora vs alternatives.