CyberGuard dictionary / AI Security
Retrieval Augmented Generation Risk
Retrieval augmented generation risk is the possibility that retrieved documents or embeddings mislead an AI answer, cross access boundaries, or expose sensitive data.
In simple terms
A retrieval system supplies outside material to a model before it answers. That material may be stale, malicious, or visible to the wrong user.
Technical explanation
RAG pipelines need document-level authorization, source provenance, ingestion controls, and separation between retrieved text and instructions. Vector similarity alone does not enforce access rights or factual accuracy.
Retrieval can import stale, malicious, or unauthorized material into a model's context. Source provenance, tenant filtering, and clear separation of retrieved text from instructions are essential.
Examples in practice
A user asks a company assistant about a policy and receives text from another tenant's private document because the retrieval filter used the wrong tenant ID.
A poisoned policy document tells the assistant to ignore its task and disclose private notes. The application treats the document as evidence to summarize, not as authority to change behavior.
Why it matters
Retrieval improves context only when the application checks which sources each user may access.
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Sources and further reading
Published 2026-10-15 · Last updated 2026-10-15 · CyberGuard editorial content. Source links provide context; they do not imply endorsement.