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24/06/2026 9:20 pm
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?
Explore further
25/06/2026 6:30 am
RAG turns authorization into a three-stage control problem. The article correctly shows that query approval, data retrieval, and output filtering are separate decisions, not one policy event. That matters because each stage can succeed or fail independently, and a control that exists in one layer does not automatically protect the others. Practitioners should stop treating GenAI access as a single gate and start governing each stage explicitly.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How do organisations know if GenAI authorization is actually working?
A: They should test whether unauthorized users can infer, retrieve, or see restricted data through the AI workflow. Good evidence includes policy logs for each checkpoint, denial rates for out-of-scope requests, and audit trails that show which document or field was filtered. If the system cannot explain those decisions, governance is incomplete.
👉 Read our full editorial: GenAI authorization gaps in RAG pipelines expose least-privilege failures
