Companies increasingly integrate RAG systems with internal knowledge bases to power AI assistants for policy management, technical documentation, and customer support. This process involves indexing corporate data, which often includes sensitive information, confidential documents, and personal data.
Standard vector storage solutions lack native access controls, returning information based solely on semantic similarity. Consequently, every indexed document risks becoming part of a model query, necessitating robust security measures beyond simple prompt filtering.
The implementation of RAG introduces four specific security threats: database poisoning, unauthorized access, data exfiltration, and model hallucination. These risks originate within the data and storage layers rather than the query text itself.
Effective security architecture for RAG requires monitoring at three critical points: the ingestion process into the index, the retrieval process from the index, and the final generation of the model response.
