DeepInspect vs Zscaler AI Guard

AI governance in 2026, compared across six problems.

Last updated: May 2026

Why This Comparison

Enterprise teams evaluating AI governance platforms in 2026 routinely ask how DeepInspect compares to Zscaler. Both products inspect AI traffic, apply policy, and produce logs.

The right answer depends on how deep AI sits in the organization's strategy. For employee productivity use through public chatbots on managed browsers, Zscaler alone is often sufficient. For organizations embedding AI into their products, running agentic workflows, supporting developer-led AI integration, or facing regulated customers who ask for AI audit evidence, a second control plane is needed at a layer Zscaler does not operate in. The two products are complementary in that case. Zscaler continues to cover the browser path. DeepInspect covers the product, developer, and audit-evidence layer.

This document walks through the overlap and the material differences, using the six problems that define AI governance conversations today. The comparison is grounded in Zscaler's publicly available product documentation, solution briefs, and partner integrations as of May 2026, and in DeepInspect's live product.

Executive Summary

DeepInspect is an AI governance and security platform. It sits inline between users, agents, and AI applications and the LLMs or AI APIs they consume. The platform does six things:

1. Enforces real-time, identity- and data-aware policies on every AI interaction with LLMs, agents, or AI-driven applications.
2. Prevents sensitive data leakage and unauthorized access.
3. Performs forensic deep analysis to detect and surface hidden risks like fraud, sophisticated attacks, and evolving threat vectors.
4. Creates evidence-grade audit for every AI interaction and decision.
5. Keeps organizations compliant with business policy and industry regulation.
6. Provides operational governance capabilities: per-user and per-agent attribution on every transaction, token-level cost tracking, model routing across pools, an organization-wide kill switch for active incidents, and natural language policies evaluated by a customer-hosted LLM/SLM.

Zscaler AI Guard is a runtime AI security capability inside the Zscaler Zero Trust Exchange. It governs user web traffic to public GenAI apps like ChatGPT, Gemini, and Copilot. It does this well for organizations already invested in the Zscaler platform.

The two products solve overlapping problems at different layers of the stack.

The Six Problems That Define AI Governance in 2026

1. Stop sensitive data from leaving the enterprise through AI interactions.
2. Apply different rules to different people, teams, and agents using the same AI tools.
3. Prove what happened, by whom, with what data, for audit and incident response.
4. Detect fraud, abuse, and sophisticated attacks across AI usage.
5. Track and govern the cost of AI usage.
6. Contain damage quickly when something goes wrong.

Both products address some of these. The fit depends on which problems matter most and where the AI traffic actually flows.

How Each Product Maps to the Problems

Problem 1: Stopping Data Leakage

Both products inspect prompts and responses in real time and act on sensitive content.

Zscaler enforces DLP-style detection with block and allow actions, plus browser isolation as a stronger control for risky web sessions. The detection set covers PII, secrets, intellectual property, and similar categories.

DeepInspect supports five enforcement actions: allow, redact, tokenize, anonymize, and block. Tokenization is the operationally important one. It replaces sensitive fields with reversible placeholders so the model still receives useful context, then detokenizes the response for the authorized user. This preserves AI utility while protecting the data.

Both products solve this problem. The choice depends on whether you need block-or-allow or richer transformation actions.

Problem 2: Different Rules for Different Users

Zscaler enforces identity-based access to AI applications. It decides who can reach ChatGPT and who cannot. This is access control.

DeepInspect enforces identity-based outcomes on AI content. The same prompt sent to the same model produces a different result for a finance user, an HR user, or a contractor: allowed, redacted, tokenized, or blocked, based on role. This is content-level control.

Both controls have value. Access control limits who can use a tool. Content control limits what the tool can do for a given user.

Problem 3: Audit and Proof

Both products log AI usage. The difference is in the class of evidence produced.

Zscaler maintains logs of users, apps, prompts, responses, policies, and actions, stored in a customer S3 bucket. Compliance reporting maps to NIST AI Framework, EU AI Act, and OWASP Top 10 for LLMs. This is sufficient for general compliance reporting.

DeepInspect produces cryptographically signed, hash-chained audit records for every transaction. The chain is verifiable. The records include policy ID, policy version, user identity, agent identity, model, decision, and content hash. They are designed to withstand the evidence scrutiny that the EU AI Act, Colorado AI Act, and similar frameworks introduce.

In a regulator review or a litigation discovery, the distinction matters. Standard logs are reportable. Signed, hash-chained records are defensible.

Problem 4: Detecting Fraud and Sophisticated Attacks

Zscaler AI Guard runs 18+ specialized detectors on each prompt and response, covering prompt injection, jailbreak, PII, secrets, toxicity, brand reputation, and malicious URLs. These detectors work per transaction with multilingual support.

DeepInspect runs the same class of per-transaction detection and adds forensic correlation across transactions. Staged data exfiltration that hides in five separate prompts, multi-turn jailbreak chains, agent misuse over a session, and fraud signatures that only emerge when you look across users are patterns that single-prompt detectors will not surface. Per-transaction detection catches the obvious attacks. Cross-transaction correlation catches the patient ones.

Problem 5: Managing AI Cost

This is outside the scope of an SSE platform. Zscaler does not track token usage or AI cost.

DeepInspect tracks token usage and cost per application, per user, and per model. Finance gets attribution. Engineering gets budget visibility. Security gets context on which users and apps are consuming AI and at what rate. Cost data also feeds anomaly detection: a user suddenly spending 10x their usual tokens is often the first signal of misuse.

Problem 6: Containing an Active Incident

Zscaler can revoke access policies and block AI applications, but this is a policy-change operation rolled out across the Zero Trust Exchange.

DeepInspect provides an organization-wide and per-application kill switch. When a security team realizes they are under attack or that an AI application is misbehaving, one switch halts all interactions immediately, scoped to the entire organization or to a single application. The rest of the business keeps running. This is built specifically for active incident response and damage control.

Bonus: Natural Language Policies with Privacy

This is a capability Zscaler does not currently offer.

DeepInspect lets customers write policies in plain English. These policies are evaluated against live traffic by a customer-configured LLM or SLM running on the customer's own infrastructure. The prompts being evaluated never leave the customer environment. The output of the evaluation feeds the same evidence-grade audit log as the deterministic policies.

This matters in two ways. Security teams can author and revise policies without engineering cycles. Regulated buyers in financial services, healthcare, and the public sector get a privacy story their procurement teams will accept: sensitive content is evaluated by the customer's own model in the customer's own environment.

A Concrete Scenario: Seeing What Developers Send to Claude Code

This scenario comes up in nearly every customer evaluation, so it is worth walking through in detail. The enterprise wants to see what each developer is sending to Claude Code, attribute it to the right user, redact sensitive content like API keys or customer data, and produce an audit record.

With Zscaler

Visibility requires three conditions to hold:

1. The Zscaler Client Connector is installed on the developer's machine.
2. SSL inspection is enabled, so HTTPS traffic to the LLM provider can be decrypted.
3. The developer's traffic actually traverses the Zero Trust Exchange (managed laptop on corporate network, VPN, or always-on agent).

When those conditions are met, Zscaler can decrypt the API call, identify the developer via endpoint identity, run AI Guard detectors on the payload, and log the result.

The architectural limits show up outside that envelope. Claude Code running in CI/CD pipelines, container workloads, agent runtimes, build servers, or unmanaged developer machines does not traverse the endpoint agent. Server-to-server and agent-initiated traffic is a known gap in SSE platforms. Coverage is partial by design.

With DeepInspect

Claude Code is pointed at the DeepInspect gateway endpoint through a single environment variable or proxy configuration. Every request goes through the gateway regardless of origin: developer laptop, CI/CD pipeline, container, agent runtime, build server, or production service. DeepInspect attributes each call to the developer or service account, applies policy, redacts or tokenizes sensitive code and secrets, and produces a signed audit record.

The visibility is the same whether the traffic originates from a developer in Toronto or a CI job in us-east-1.

This pattern generalizes. Anywhere AI is consumed via API from inside the enterprise (copilots, agents, internal apps, RAG pipelines, developer tools), DeepInspect sees it. Zscaler sees the subset that traverses its endpoint or proxy.

Capability Comparison

Where Each Product Is the Right Choice

Zscaler AI Guard is sufficient when AI is a productivity tool.

If AI usage at the organization is employees using ChatGPT, Gemini, or Copilot through a managed browser, and the security goal is preventing sensitive data from leaving through that channel, Zscaler covers it well. The detectors are mature, integration with existing Zscaler infrastructure is clean, and general compliance reporting is adequate. Adding a second control plane for this profile alone is operational overhead without payoff.

DeepInspect is the control plane when AI is part of the business.

The SSE model was designed to inspect employee browser traffic going to the internet. The moment AI moves outside that envelope, Zscaler's architecture runs out of coverage by design. The following describes most serious enterprises by mid-2026:

• AI features built into the product, exposed to customers.
• Agents calling models and tools autonomously, often from cloud workloads.
• Developers integrating LLMs through Claude Code, Copilot, and direct API calls. Managed laptops with the Zscaler Client Connector are visible. CI/CD pipelines, container workloads, and build infrastructure are not.
• Customer security questionnaires asking for AI audit evidence and policy enforcement records that cover the full AI surface, not just the browser slice.
• Audit requirements where records need to hold up under regulator scrutiny or litigation review, not just satisfy a checkbox.

When two or more of these describe the organization today, or will within twelve months, the AI surface Zscaler does not see becomes the larger one. Zscaler's current portfolio does not include a product purpose-built for that surface. DeepInspect is.

In this scenario, the two products are complementary. Zscaler governs the browser path. DeepInspect governs everything else.

Notes on Sources

Zscaler capabilities described above are based on Zscaler's published product pages, solution briefs (AI Guard, GenAI Security), the Zscaler IT whitepaper on securing AI, and partner integration documentation (LiteLLM, NeMo Guardrails, Portkey).

DeepInspect capabilities are based on the public product documentation as of May 2026.