Technical cluster
RAG security design for enterprise retrieval workflows.
RAG security design starts before retrieval and continues through prompt construction, provider routing, output handling, and audit. PromptWall connects secure LLM gateway controls with prompt firewall and AI DLP policy.
Traffic
Gateway aligned
Apply controls before prompts reach external model providers.
Data
DLP aware
Detect sensitive prompts, regulated data, and document leakage risk.
Evidence
Audit ready
Keep explainable records for security, risk, and compliance reviews.
Secure RAG is a chain of controls, not one vector database setting.
A secure RAG workflow needs identity-aware retrieval, protected corpus boundaries, prompt injection inspection on retrieved text, sensitive data masking, model-route policy, and response audit. Weakness in any layer can turn trusted internal knowledge into ungoverned model context.
DLP
Document leak detection
Detect when protected material appears in prompts or generated context.
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Firewall
Prompt injection prevention
Treat retrieved content as untrusted input that can carry malicious instructions.
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Gateway
LLM gateway architecture
Route and audit RAG traffic through a controlled provider layer.
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What enterprise buyers should ask before production.
Teams should ask which documents can be retrieved, which users can retrieve them, whether retrieved content is inspected before prompt assembly, how sensitive fragments are masked, and how the final request is audited. PromptWall provides the control vocabulary for those buying questions.
Review your RAG security design
Map retrieval, prompt assembly, model routing, and audit evidence into a PromptWall control plan.
Frequently asked questions
Why is RAG security different from normal app security?+
RAG adds retrieved content to prompts, which means documents and knowledge base entries can become model instructions or leak sensitive context unless inspected.
Where should RAG controls run?+
Controls should run at retrieval, prompt assembly, gateway dispatch, and audit layers so teams can enforce policy before and after model use.