RAG pipelines and GenAI access control: are your policies keeping up?

TL;DR: RAG pipelines create new authorization exposure because LLMs can surface sensitive data through query, retrieval, and output paths when policy decisions are not applied consistently, according to PlainID. The underlying problem is that traditional access models were built for static applications, not AI-mediated access that can expand the blast radius of a single request.

NHIMG editorial — based on content published by PlainID: ALL NEW Agentic Identity Platform OWASP Top 10 for LLM and GenAI Security with PBAC

Questions worth separating out

Q: How should security teams control access in RAG-based GenAI systems?

A: Security teams should enforce authorization at three points: before the prompt is accepted, before data is retrieved, and before the answer is returned.

Q: Why do traditional RBAC models struggle with GenAI access control?

A: RBAC struggles because GenAI workflows are dynamic and context-dependent.

Q: What breaks when output filtering is missing in an LLM workflow?

A: Without output filtering, a model can surface confidential data even when the prompt and retrieval look legitimate.

Practitioner guidance

• Map the three authorization checkpoints in every RAG workflow Document where policy must be enforced before prompt submission, before retrieval, and before output display.
• Classify the data that the model can retrieve and reveal Tag documents, embeddings, and connected data sources by sensitivity so policy can distinguish between allowed questions and allowed answers.
• Centralize policy decisions across AI and non-AI systems Avoid separate authorization logic for APIs, retrieval layers, and downstream applications.

What's in the full article

PlainID's full blog covers the operational detail this post intentionally leaves for the source:

• The specific PBAC enforcement points used to control input, retrieval, and response handling in GenAI pipelines
• The vendor’s breakdown of how identity attributes, groups, and data sensitivity are combined in runtime policy decisions
• Implementation examples for centralizing policy across APIs, data layers, microservices, and AI access paths

👉 Read PlainID's analysis of OWASP Top 10 access control risks in GenAI →

RAG pipelines and GenAI access control: are your policies keeping up?

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