Resource
Secure RAG starts with boundaries.
Secure RAG is not just vector search behind a chatbot. For business data, retrieval must respect tenant boundaries, source permissions, audit requirements, and approval policies before it can become useful operational memory.
Evaluation checklist
- Tenant-isolated memory and embeddings
- Role-aware retrieval that mirrors source permissions
- Source citations and visible provenance
- Audit logs for retrieval and actions
- Human approval before outbound work
- Retention and deletion controls
Why it matters
Retrieval can accidentally widen access if it ignores the original system of record. A secure design treats every memory object as permissioned context, not just text in an index.
Achiral AI builds shared memory inside organization boundaries. It is designed around tenant-isolated memory, role-aware retrieval, audit logs, and documented controls rather than generic cross-customer context.
FAQs
- What makes RAG secure?
- Secure RAG preserves the access rules, tenant boundaries, audit trails, and approval policies of the systems it retrieves from.
- Is vector search enough for secure RAG?
- No. Vector search is only one retrieval method. Teams also need permission checks, source provenance, retention controls, and review before sensitive actions.
Related
Continue with Security and compliance, technical specs, and the agent security survey Securing the Agent.