Notifications
Clear all
Topic starter
24/06/2026 9:20 pm
TL;DR: Policy-based access control must sit around AI query input, retrieval, and response layers to prevent data leaks in RAG and LLM workflows, especially when agents act as NHIs and permissions must remain tied to user identity, according to PlainID. The governance problem is not AI alone but unchecked authorization paths that outgrow traditional Zero Trust assumptions.
NHIMG editorial — based on content published by PlainID: Secure Policy-Driven AI Data Access
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
Questions worth separating out
Q: How should security teams enforce access control in GenAI workflows?
A: Security teams should enforce access control at three points: before the query is accepted, before data is retrieved into context, and before the response is released.
Q: Why do AI agents complicate IAM and NHI governance?
A: AI agents complicate governance because they can behave like non-human identities while also reshaping what data gets retrieved and disclosed.
Q: What breaks when retrieval controls are too broad in RAG systems?
A: When retrieval controls are too broad, the model can ingest documents the user was never meant to access and then summarize or recombine them into a visible answer.
Practitioner guidance
- Map authorization to the full AI request path Define control points for prompt submission, retrieval, and response delivery so that each stage is policy checked against the user’s identity and entitlement.
- Limit retrieval to policy-approved data scopes Classify source documents and embeddings by sensitivity, then enforce retrieval filters that prevent the model from seeing content outside the user’s allowed scope.
- Inspect and mask generated outputs before release Add output-layer controls that detect secrets, regulated fields, and restricted content patterns before the response reaches the user or another system.
What's in the full article
PlainID's full blog post covers the operational detail this post intentionally leaves for the source:
- Policy design examples for enforcing authorization across AI query, retrieval, and output stages
- How PlainID frames PBAC for RAG and LLM environments in practice
- Product-oriented guidance on controlling AI-generated responses to reduce leakage risk
- The vendor's own walkthrough of dynamic authorization use cases for cybersecurity and data protection
👉 Read PlainID's analysis of policy-based access control for AI data security →
AI agents and GenAI data access: are your controls keeping up?
Explore further
25/06/2026 6:29 am
Policy-based authorization for AI is becoming a control-plane problem, not a model feature. The article is right to place access decisions around query, retrieval, and output handling rather than inside the model itself. That is the governance shift: the policy engine must constrain what the AI is allowed to see and say, not merely what the user is allowed to open in a traditional app. Practitioners should treat AI access as an identity workflow, not a chatbot setting.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface. That figure explains why AI governance cannot stop at authentication and why entitlement scope must be controlled throughout the workflow.
A question worth separating out:
Q: How do teams reduce the risk of sensitive data leaking from LLM outputs?
A: Teams reduce leakage by adding a response inspection layer that checks generated text for secrets, regulated data, and disallowed disclosures before delivery. They should pair that with logging and policy review so suppression events are visible to security and compliance teams. Without output controls, the model can become the last mile of accidental disclosure.
👉 Read our full editorial: Policy-based access control for AI agents and GenAI data risk
