An AI governance framework built on policy documents governs nothing on its own. dope.security adds the enforcement layer that reads and controls the prompt on the device, before data leaves. Here is how the four layers fit.

Comparing Menlo Security competitors? Remote browser isolation only governs the browser tab. dope.security inspects every egress path on the device, catches AI prompts semantically, and skips the latency Menlo concedes.

Web content filtering built for the DNS layer or a data center misses the encrypted traffic where risk now lives. Here is how filtering works, the three architectures, and why the endpoint is the right place to do it, with dope.security.

Most insider risk tools score behavior and raise alerts after the data is already gone. Stopping insider data loss takes enforcement at the point of egress. Here is where detection ends and prevention begins, with dope.security.

Data classification tells you what you have. It does not stop it leaving. See why classification only prevents loss when it runs inline at the point of egress, and how dope.security classifies data at rest and in motion in one platform.

AI data governance fails the moment it lives in a policy document. Real control means seeing every AI app, pushing policy to the device, and inspecting the prompt before it leaves. Here is how to build a program that actually enforces, with dope.security.