Is Zscaler or Netskope better for shadow AI discovery?

Both vendors publish broad discovery claims. Zscaler describes AI assets and lineage across more than 2,900 applications. Netskope describes continuous inventory across cloud and network, with endpoint coverage, plus inline inspection across more than 1,800 AI apps. Catalog size alone cannot settle the decision. Test the applications and account types your workforce actually uses, then measure discovery delay and event detail.

Do both vendors provide an AI gateway?

Both current product pages name an AI gateway. They do not publish matching provider lists, identity schemas, streaming behavior, failure settings or export fields. Buyers should test those fields against the same workload before treating the modules as substitutes.

Can either platform see every agent action?

Coverage depends on the action and transport. Routed HTTP model calls, remote MCP traffic, browser activity, endpoint execution, and local STDIO create different observation points. Ask which module sees each hop and retain a gap owner for every action outside that module.

Should a company deploy both platforms?

Running both broad SSE platforms can create operational duplication. A company may still combine one SSE platform with a narrower request control where a regulated model route needs different identity or evidence. The architecture diagram and acceptance results should drive that decision, rather than an assumption that another product always fills a gap.

Zscaler vs Netskope AI: Match the Control to the Traffic Path

Zscaler and Netskope each sell more than a browser control for public chatbots. Their current product pages describe AI discovery and data protection. They also describe runtime inspection and AI gateway functions that include agent controls. That breadth makes a one-column feature checklist almost useless. The buying decision turns on which traffic reaches which enforcement point. It also depends on the identity that arrives with the traffic and the record that survives after the decision.

I would draw the routes on a whiteboard before opening either vendor questionnaire. Put the managed browser and SaaS session on separate lines. Add the internal AI application and agent below them. Include the MCP connection and direct model API call. A green check beside "AI security" says less than the arrow showing where a request actually crosses inspection.

TL;DR

Zscaler presents an AI security platform spanning asset discovery, secure user access, application protection, endpoint coverage, and an AI gateway.
Netskope Skylight presents discovery and control across users, self-hosted AI, agents, and data, backed by its SSE and endpoint footprint.
Compare the products route by route. Browser controls, DLP, model API inspection, agent mediation, identity context, and evidence are separate buying tests.
DeepInspect applies only to authenticated user or agent HTTP model traffic deliberately routed through its proxy.

The portfolio labels hide several control planes

The official Zscaler AI security page divides its offer into AI Asset Management, Secure Access to AI, Secure AI Apps and Infrastructure, and AI Gateway. Zscaler says asset management discovers models, agents, MCP servers, and shadow AI across more than 2,900 applications. Its access layer controls user access and filters sensitive data. The gateway is described as routing and governing AI transactions with inline DLP and Zero Trust policy.

The official Netskope Skylight AI Security page organizes the problem around users and self-hosted systems, with separate surfaces for agents and data. Netskope describes continuous inventory across cloud and network, with endpoint coverage. It also names GenAI App Security, Agentic Broker, AI Gateway, AI Guardrails, and Agent Action Control as runtime products. Its user section says inline inspection covers more than 1,800 AI apps and distinguishes corporate accounts from personal use.

Those descriptions establish portfolio scope, not equivalent visibility or evidence. Procurement should test both properties.

Browser and SSE plus SaaS discovery

Both vendors enter the comparison with mature traffic and data-security footprints. That matters when employees open public AI applications. They may upload files or paste text in managed browsers. Personal-account sign-in needs its own test. The SSE path can identify destinations before the session continues. Endpoint visibility can add local context.

Zscaler emphasizes inline coverage across users and devices as well as APIs. Its asset inventory claims lineage across a large application catalog. Netskope emphasizes continuous discovery across the network and endpoint, with cloud context and account-instance controls that distinguish a company login from a personal one.

A workforce program with unmanaged SaaS usage needs discovery depth and device coverage. Test a managed laptop and a contractor device, then repeat with a personal account. The browser result says nothing about a Python service calling a model API.

DLP and model traffic need separate acceptance tests

DLP works only where the enforcement point can read the content. For a browser route, test typed text and a file upload. Include a sanctioned corporate account and a personal account. Record the policy and user experience. Preserve the incident artifact sent to the security team.

An internal application creates a different path. The application may assemble a system instruction and user prompt, then add retrieval context and tool output before sending one JSON request to a model. Test that complete payload through the vendor's documented AI gateway or runtime module. Confirm supported providers and streaming behavior, then check request size and response inspection. Test redaction and failure handling separately. A broad claim about inline DLP cannot substitute for that route test.

Zscaler places inline DLP in its AI Gateway description. Netskope says runtime protection can redact or block sensitive data before it reaches a model. Neither page supplies your deployment's exact parser behavior or identity schema. Test both products with the same payloads, then obtain written answers on failure settings and evidence retention.

Agent and gateway coverage is now part of both stories

Agent traffic complicates the old "SSE versus AI gateway" shorthand. Zscaler describes policy across enterprise AI agents and lists MCP servers among the assets it discovers. Netskope names Agentic Broker and Agent Action Control, plus MCP server context and AI Gateway. Each vendor is extending beyond a browser-only story.

The test still begins with transport. An agent can call a model over HTTP, invoke a remote MCP server, run a local tool, or use STDIO on the same host. It can also trigger an ordinary SaaS workflow. One policy point rarely sees all of those events. Ask the vendor to mark each hop on the diagram and identify the module receiving it.

For an HTTP model request, validate prompt and response handling plus the identity propagated for the agent and its delegating user. For MCP, inspect tool discovery and invocation separately. Local execution and STDIO remain endpoint or workload concerns unless their effects create another routed HTTP request. I would reject any proposal that turns the word "agent" into proof of universal coverage.

Identity and evidence decide the regulated use case

Identity can mean a device or browser user. It can also mean a SaaS account, workload, API credential, agent, or delegating person. Those subjects are not interchangeable. A provider key may authenticate an application while hiding the employee who initiated it. An SSE session may identify the employee without the model call's application purpose.

Run one allowed request and one denied request for each important route. Export the resulting decision record. Look for the originating subject and agent or workload. Then inspect the destination and policy version. Review the content classification and action, followed by the timestamp. Then change the policy and repeat the request. Six months later, a reviewer should be able to explain the earlier decision using the policy version that actually ran.

This is where the existing DeepInspect versus Zscaler analysis and DeepInspect versus Netskope analysis help separate broad workforce controls from request-level model authorization. Those comparisons cover different buyers; this one compares the broad portfolios by route.

A route-by-route buying matrix

Use seven rows in the working evaluation, even if the procurement system wants one product score.

Browser and SSE: Test devices under managed and unmanaged conditions. Include the relevant personal SaaS accounts. Exercise uploads and pasted text. Preserve coaching or block screenshots plus exported events.
SaaS discovery: Compare application catalogs and tenant awareness. Then measure risk metadata and the delay between first use and inventory appearance.
DLP: Replay the same synthetic markers through a structured model API payload. Repeat the test through browser text and file upload. Record parsing and action differences.
AI gateway: Test direct HTTP model calls, streaming, provider changes, retries, and an unavailable policy service. Document the actual failure posture.
Agents: Separate model requests and remote tools. Test MCP and browser actions. Treat local execution as a distinct hop with a named owner.
Identity: Verify the natural person when present and the workload or agent. Add the delegation context and downstream credential.
Evidence: Export allowed and modified decisions, then export blocked decisions. Verify policy version and route. Use the event timestamps to check retention and searchability.

The physical artifact should be a route map with packet arrows and sample event IDs, not a scorecard with unexplained checkmarks. That opinion may annoy procurement, which is why it belongs in the review.

Selection guidance

Zscaler deserves the closer look when the organization already standardizes on the Zero Trust Exchange and wants AI controls connected to that operating model. Its public scope is especially relevant to teams evaluating AI asset discovery, access policy, endpoint AI activity, AI application testing, and centralized gateway policy.

Netskope deserves the closer look when the organization wants AI controls integrated with its existing Netskope SSE and data-security program. Its public emphasis on account instances, cloud and endpoint discovery, self-hosted AI, agent controls, and NewEdge performance gives that buyer a concrete test plan.

A proof of concept should include both vendors only if the routes and success criteria are identical. Otherwise, one vendor gets tested on browser coaching while the other gets tested on an API gateway, and the resulting score is theater. Use the same identities, payloads, applications, agent sequence, and evidence questions. Keep unsupported capability claims out of the final recommendation.

DeepInspect

DeepInspect governs deliberately routed HTTP traffic between authenticated users or agents and LLM endpoints. It evaluates application-supplied identity and context before applying policy to the model request and response. Each evaluation writes a per-decision record. The control can complement the selected SSE platform when that specific HTTP route needs an independent policy point.

Its boundary is narrower than either broad portfolio. DeepInspect does not discover arbitrary SaaS use, inspect local endpoint activity, govern STDIO, or replace browser and network controls. The identity-aware AI gateway architecture shows the request path and the context an application must supply.

Book a technical deep dive at deepinspect.ai.