How does Glean keep sensitive data secure while still using AI?

# Getting started with Glean

# Assistant

This is one of the most important questions in any enterprise AI rollout.

April 24, 2026 · Last updated on May 5, 2026

Nikhar Gupta

Short version: Glean is designed so AI works inside your existing access controls, not around them. Search, Assistant, and Agents all enforce source-system permissions, so users only see what they are already allowed to access in the original app.

That is the first layer. If someone cannot see a document in the source system, Glean is not supposed to become a backdoor to it. And if permissions change in the source app, Glean reflects those changes rather than maintaining a separate, stale visibility model.

But in real enterprise environments, permissions alone are not always enough.

A lot of sensitive information is technically accessible because it has been overshared, mislabeled, or left visible longer than intended. That is where Glean Protect adds an active governance layer: it continuously scans connected data, detects overshared sensitive content, and can auto-hide it so it does not appear in Search, Assistant, or Agents.

A few controls matter most here:

Permissions enforcement at query time Users and agents only get access to content they could already access in the underlying system.

Sensitive content detection and auto-hide Glean can scan across 100+ applications for overshared sensitive information and prevent that content from surfacing in AI experiences.

Indexing controls Admins can decide what gets crawled and indexed, which adds another control point before content ever shows up in search or answers.

AI-specific protections Glean adds protections against prompt injection, jailbreak attempts, malicious code, and similar misuse patterns that become more important as agents take on more tasks.

Agent guardrails and admin oversight Agents follow user permissions, and admins can control who can create, edit, view, and share them, which helps keep automation scoped appropriately.

There is also the underlying infrastructure layer. Glean emphasizes enterprise SSO, encryption in transit and at rest, audit logging, and single-tenant deployment options, along with certifications such as SOC 2 Type II, ISO 27001, and ISO 42001.

One other point security teams usually care about: Glean’s AI governance includes zero data retention for LLMs, and the overall model is built so the LLM never gets access to data the user was not already permitted to see.

So the practical framing is not really “AI versus security.” It is closer to: permissions first, governance on top, and AI operating inside those boundaries.

For security and IT teams here: what part of the model usually needs the most explanation internally — permissions inheritance, overshared data governance, or AI-specific guardrails?

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