Is Ent or Zscaler better for AI endpoint control?

Neither is scored on this site. Ent describes 2 of four product control types and Zscaler 1 on the pages we read, and they differ on 9 recorded facts. Which fits depends on the AI tools people use, your devices, where the work happens and whether you block or guide; the finder checks those.

Which of the two decides on a tool call before it runs?

Ent: Partly. Zscaler: Not in our sources. Warn or ask: Ent Matches, Zscaler Not in our sources.

Do Ent or Zscaler publish prices?

Ent: Not published (demo request). Zscaler: Not published (demo request).

HEAD-TO-HEAD · FACTS ONLY

Ent vs Zscaler: AI endpoint control facts side by side

Short answer

Ent describes 2 of the four product control types on the pages we read and Zscaler describes 1; they differ on on-device control, browser extension and network or SSE proxy. On decisions, Ent describes some control over tool calls and Zscaler does not describe tool-call decisions on the pages we read. We do not score either; the right choice depends on the AI tools people use, your devices, your current stack and whether you block or guide.

By The Device Desk · Endpoint AI Finder · Published 2026-10-11

Desk research from public vendor pages read on 27 September and 11 October 2026. Profiles follow our published method; we did not run the products.

Vendor pages read September and October 2026 · updated 2026-10-13

Where do their controls sit?

Filled or hatched marker: Ent
Dashed teal ring: Zscaler, described

DEV Filled marker: described on the product pages we read
DEV Hatched marker: partly described (another component, a narrower scope, or method not detailed)
DEV Dashed marker: not in our sources (it may exist; we did not find it)

The map shows where each product's own pages describe a control, or where your answers point. It is not a measure of how well a product works.

What are the facts, side by side?

Ent and Zscaler Generative AI Security, from each product's own pages. Differs is computed from recorded statuses, not wording.
Fact	Ent	Zscaler
Owner	None	None
Positioning	An intent-aware AI endpoint: one programmable policy for people and AI agents, with interventions from a soft nudge to a hard block, an on-device small language model that classifies sensitive data, and investigation timelines written in plain language. Use cases: insider risk, last-mile threats such as ClickFix, AI usage control and data security.	Zscaler's controls for employee use of generative AI: prompt-level visibility, allow or block by app, DLP blocking across GenAI apps, browser isolation that disables cut, paste and download, and data security for Microsoft Copilot.
How it reaches devices	A single lightweight agent across Windows, macOS, Linux and modern browsers, run as SaaS or self-hosted in your own cloud; rollout through MDM is not stated. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	Delivered with Zscaler's cloud security service in the network path; endpoint agents are not described on the page we read. Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026
On-device controlDiffers	DescribedA single lightweight agent across Windows, macOS and Linux with one programmable policy for people and AI agents. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	Not in our sourcesNot described on the pages we read.
Browser extensionDiffers	DescribedBrowser-extension deployments generally available at launch. Source: Ent press: $100M seed, 16 June 2026 · Read 13 Oct 2026	Not in our sourcesNot described on the pages we read.
Network or SSE proxyDiffers	Not in our sourcesNot described on the pages we read.	DescribedSee all input prompts users send to apps, allow or block apps, block sensitive data with DLP across GenAI, and isolate the browser to disable cut, paste and download. Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026
AI or MCP gateway	Not in our sourcesNot described on the pages we read.	Not in our sourcesNot described on the pages we read.
Names the coding agents it coversDiffers	MatchesNames OpenAI Codex, Claude Code, Claude Cowork, GitHub Copilot and Cursor. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	Not in our sources
Decides on a tool call before it runsDiffers	PartlyBlocks risky agent behavior where needed; per-tool-call mechanics are not described. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	Not in our sources
Controls what goes into browser AIDiffers	PartlyClassifies sensitive data at the point of use on the device and in browsers; prompt-level controls are not detailed. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	MatchesPrompt-level visibility, DLP blocking across GenAI apps, and browser isolation that disables cut, paste and download. Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026
Rolls out through EDR or device management you run	Not in our sources	Not in our sources
Can warn or ask instead of only blockingDiffers	MatchesInterventions range from a soft nudge to a hard block. Source: Ent homepage · Read 13 Oct 2026	Not in our sources
Can block an agent, tool or action	MatchesA hard block is one of its interventions. Source: Ent homepage · Read 13 Oct 2026	MatchesAllow or block GenAI apps, and block sensitive data with DLP. Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026
Has a watch-only or simulation mode	Not in our sources	Not in our sources
Records who or what took each actionDiffers	MatchesNatural-language investigation timelines. Source: Ent press: intent-aware prevention for AI agents, 30 July 2026 · Read 13 Oct 2026	Not in our sources
Also covers SaaS, cloud or homegrown AIDiffers	Not in our sources	PartlyData security for Microsoft Copilot, including misconfigurations. Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026
Has a public price or tiers (vendor site or marketplace)	Not in our sources	Not in our sources
Built into	None	None
Works with	None in our sources	None in our sources
Price	Not published (demo request). Source: Ent homepage · Read 13 Oct 2026	Not published (demo request). Source: Zscaler: Secure the use of generative AI · Read 11 Oct 2026

Where do they differ?

On-device control: Ent describes it; Zscaler is not in our sources.
Browser extension: Ent describes it; Zscaler is not in our sources.
Network or SSE proxy: Ent is not in our sources; Zscaler describes it.
Names the coding agents it covers: Ent matches; Zscaler is not in our sources.
Decides on a tool call before it runs: Ent partly matches; Zscaler is not in our sources.
Controls what goes into browser AI: Ent partly matches; Zscaler matches.
Can warn or ask instead of only blocking: Ent matches; Zscaler is not in our sources.
Records who or what took each action: Ent matches; Zscaler is not in our sources.
Also covers SaaS, cloud or homegrown AI: Ent is not in our sources; Zscaler partly matches.

Choose Ent if

You want one policy for people and AI agents, with nudges as well as blocks
You need device and browser coverage from one agent

Choose Zscaler if

You already route traffic through Zscaler and want GenAI controls there
Your main risk is sensitive data pasted or uploaded into GenAI web apps

Which limits should you check?

Ent

Per-tool-call mechanics and rollout through MDM are not described on the pages we read.
Uses its own agent, and no price is published.

Zscaler

Coding agents, MCP servers and local agent actions are not described on the page we read.
Warn or ask responses are not described, and no pricing is published.

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