Browsers, Extensions, Local models, Coding assistants, Seen secured, and governed by one policy, endpoint to cloud
Visibility into the AI your workforce actually uses, including what never crosses the network
Malicious AI components caught on the device, before they act
One AI policy from endpoint to cloud, instead of two frameworks to reconcile
No Device AI Goes Unseen
Endpoint AI Discovery finds the AI living on managed devices: local models, browser extensions, agent skills, and coding assistants. Device AI joins the same map as the rest of your AI estate, so there is one answer to what is running.
Endpoint AI Controls catch what traditional endpoint tools were never designed to model: malicious agent skills, model spoofing, and over-permissioned AI extensions. Caught on the device, before they act.
The AI policy you define in Zscaler applies on the device too. No second framework to write, reconcile, or audit. One policy, applied everywhere your people work.
Microsoft research finds that 65% of organizations report unsanctioned AI use inside their business
Your AI visibility likely starts at the network. That leaves a gap: local models that never generate traffic, browser extensions with sweeping permissions, agent skills from public marketplaces, and coding assistants working inside repositories. Employees adopt these tools in minutes. Security often learns about them in incident reviews. Endpoint platforms watch for malware and compromise, and they do that job well. But AI brings threats they were never designed to model: a spoofed model posing as a trusted one, an agent skill that turns malicious once installed. Device AI needs controls built for how AI actually behaves, enforced where the AI runs.
How it works
Built for How AI Behaves on Devices
Endpoint AI discovery
local models, AI browser extensions and plugins, agent skills, and IDE coding assistants found on managed devices
Endpoint AI controls
AI-native threats detected and policy enforced at the endpoint, including malicious agent skills and model spoofing
Applies the same AI policy
engine that governs cloud interactions, so allow, block, and caution stay consistent for a user everywhere
Feeds device AI telemetry
into AI Access Graph, so local AI joins the asset map instead of staying invisible
Covers developer AI
coding assistants and agentic tools working in repositories and terminals
Runs alongside your existing endpoint platform
adding AI-specific detection rather than replacing malware coverage
One report for audit
device AI and cloud AI answer to one policy, not two frameworks reconciled by hand
Use cases
Governed on the Device
A growing share of your workforce’s AI use happens in browsers, coding assistants, extensions, and local tools. Some of it never crosses the network. AI Endpoint Security brings device AI into view, stops AI-native threats where they start, and applies the same policy you enforce everywhere else.
An employee installs a local model or an unsanctioned desktop AI tool. It never touches the network, so nothing upstream sees it. With AI Endpoint Security it appears in your inventory the day it arrives, classified and governed by policy.
Coding assistants and agentic tools work directly in repositories and terminals, with broad access and little oversight. Device-level visibility and policy bring developer AI into the same governance as everything else, without slowing developers down.
Agent skills and AI extensions install from public marketplaces with a click. Most are useful. Some are not. AI-native detection identifies the ones that misbehave, on the device, before they reach your data.
When the review comes, device AI and cloud AI answer to one policy and one report, not two frameworks reconciled by hand.
Better together
Part of Zscaler AI Security
The device is one surface. The platform covers them all, under one policy.
AI Access Graph
device AI joins the complete asset map, with what it can reach mapped against what it should reach.
AI Guard for Users
access to AI tools decided per user, per app, per risk, on and off the device.
AI Guard for Apps
prompts and responses inspected in real time for the AI tools you sanction.
AI Gateway
agent actions authorized as they leave the device and move through your environment.
Dhawal Sharma zero trust AI
Q&A
No. Your endpoint platform covers malware and device compromise. AI Endpoint Security covers AI-specific threats and visibility those tools were not designed to model. They run side by side.
AI activity that stays on the device: local models, extensions, agent skills, and assistant tools that never generate network traffic that identifies them.
Yes. Coding assistants and IDE-based AI tools are part of device AI discovery and policy.
Policy is defined once in Zscaler and applied across device and cloud, so a user gets the same decision for the same AI interaction wherever it happens.
Enforcement happens on the device itself, so policy still applies on home networks, public Wi-Fi, or anywhere else the network isn't Zscaler's to see.