MoorAI vs Zscaler
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Zscaler is one of the largest security platforms on the internet, and on breadth MoorAI does not come close. Zscaler is a SASE/SSE platform: the Zero Trust Exchange, a global cloud that brokers every connection between user, app and internet and inspects it inline. Layered on that platform is AI Guard (its GenAI data-protection line): inline network DLP for SaaS GenAI that blocks PII, health data and source/IP pasted into ChatGPT, Gemini and Copilot at the network layer, 18+ prompt and response detectors (prompt injection, jailbreak, secrets, toxicity, malicious URLs), and app-visibility with access control over sanctioned-vs-unsanctioned GenAI apps, including Microsoft Copilot governance.
The mechanism is inline inspection in the network path. A connector or client steers traffic to Zscaler’s cloud, where the Zero Trust Exchange decrypts and inspects it; AI Guard applies DLP and its prompt/response detectors to the GenAI apps a user reaches. It is a powerful architecture and the standard one: the prompt is inspected in a provider’s inspection tier, after it has left the machine where it was typed, and only if it crosses the network at all.
MoorAI does not inspect traffic. It is action interception on the endpoint: a PreToolUse hook inside the coding agent, an MCP stdio gateway in front of the tool servers, and a companion browser extension. It evaluates a 85-threat matrix locally, decides before the tool call runs, and by default emits only category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival). Prompt content never reaches a gateway because none exists. The agent is open source (MIT), so “content stays on the device” is a claim you check by reading code, not a sentence you trust.
Zscaler would win a feature count comfortably, and the last table shows that at length, but the comparison below is not a feature count. It tracks where the action gets governed: in a provider’s network path when it crosses the wire, or on the machine where it happens, including the coding-agent tool calls (Bash, Read, local MCP) that never egress. The same architectural contrast applies to Netskope and every other inline SASE/SSE platform that inspects AI traffic in the network path.
Where MoorAI sits: around the agent, not in the network path
An inline platform puts its inspection tier between the user and the internet. MoorAI wraps the coding agent itself, on the developer’s machine — so the crossings that never reach the network (a local file read, a stdio MCP call, a skill loading) are governed too. Every control below is mapped to its MITRE ATLAS technique.
- PreToolUse hook in the agent
- MCP stdio gateway / proxy
- Companion browser extension
- Local-model escalation (Ollama)
The open knowledge base of real-world adversarial tactics and techniques used against AI systems — ATT&CK, for AI. Each control above links to the ATLAS technique it counters.
Where MoorAI is different
Most rows in this table trace back to one design decision: the decision is computed on the device where the action happens, with nothing sent anywhere to compute it. That single fact is what makes offline enforcement, fail-closed behaviour, stdio MCP coverage and a content-free audit trail possible at once. An inline platform inspects the egress; an on-device hook governs the action, including the actions that never generate any egress to inspect.
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| MoorAI | Zscaler AI Guard | |
|---|---|---|
| Detection itself runs on the device — no prompt sent to a cloud to compute a verdict | ✓85-threat matrix, local | ✗inline inspection in the Zero Trust Exchange |
| Only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓ | ✗ |
| Prompt content never traverses a gateway, proxy or PoP | ✓ | ✗ |
| Action interception — blocks the tool call before it executes | ✓ | ✗ |
| On-device MCP tool-call gateway over stdio — local filesystem, git, database servers | ✓ | ✗ |
| MCP server allow-list at call time | ✓ | — |
| Per-tool MCP argument rules | ✓ | — |
| Shadow-AI discovery on the endpoint — unsanctioned models, MCP servers and editor AI extensions vs an allow-list | ✓moorai-shadow, content-free | ◐GenAI app-visibility at the network layer, not local MCP / editor extensions |
| Governs local tool activity that never touches the network | ✓no traffic for an inline proxy to see | ✗ |
| Full enforcement offline and on air-gapped machines | ✓ | ✗cloud platform in the path |
| Break-glass / offline fail-closed — no control plane, action still refused | ✓ | — |
| Skill / rules-file analysis, content-free (CLAUDE.md / .cursorrules / skill files) | ✓ | — |
| Lethal-trifecta / cross-server toxic-flow detection | ✓ | — |
| Content-free trace / session replay — reconstructs the agent’s full action chain for incident investigation without surfacing a prompt | ✓moorai-trace | —network-side transaction logs, not the in-process tool chain |
| Model escalation — opt-in second opinion on ambiguous cases (local Ollama, or Anthropic’s API with a key already on the device) | ◐opt-in, default off; regex owns the decision, no new vendor-cloud egress | —cloud ML DLP, not an on-device opt-in escalation |
| Cryptographically signed, tamper-evident, content-free decision record | ✓ | — |
| Compliance-evidence packs — EU AI Act / NIST AI RMF / ISO 42001 control mapping from content-free signals | ✓moorai-compliance | —platform certifications, not per-decision AI-control evidence |
| Per-agent assurance score + entitlement envelope | ✓ | — |
| Anthropic Inference hooks server that runs on a host you operate, with deny reasons and alerts that carry no prompt content | ✓moorai-inference-hook: a deny names threat ids and categories, never the content | —AI Guard works with Claude Inference hooks; Zscaler’s announcement does not say where the transcript is evaluated |
| Covers the agents Inference hooks cannot see — Codex, Cursor, Copilot CLI, Gemini CLI, local Ollama, custom agents on other SDKs | ✓on-device hooks, model proxy and MCP gateway, same policy | ◐Zscaler Internet Access finds other AI, including shadow AI, in the network path, not the on-device tool call |
| Open source (MIT) — the content-free claim is auditable | ✓ | ✗ |
| Free to start — no account, no platform to stand up; console free self-hosted, no user limit | ✓ | ✗ |
Shared capabilities, different vantage points
Both products do everything in this table, so matching check marks would say nothing useful. They work from different positions (the network path versus the endpoint), with different failure modes and evidence trails, and that is what separates them. Each cell describes the approach.
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| MoorAI does it by… | Zscaler does it by… | |
|---|---|---|
| Reviewing the prompt before it reaches a model | On the device, in-process. The PreToolUse hook evaluates locally and returns a verdict before anything is sent; only category, risk and a keyed hash (plus the login and hostname, which the console pseudonymises on arrival) are ever recorded. | Inline, in the network path. The client steers traffic to the Zero Trust Exchange, where AI Guard’s DLP and 18+ prompt/response detectors inspect GenAI traffic in transit — org-wide, across every user and device. |
| Reviewing the AI’s output, not just the prompt | Output-sharing checks on the endpoint, before a summary or screenshot is shared onward; the content is never transmitted to reach that decision. | Response detectors in-line — AI Guard applies detectors to GenAI responses (toxicity, malicious URLs, secrets) as they return through the platform. |
| Discovering shadow AI | Endpoint-out. Which agents, CLIs, browser tools and MCP servers exist on this machine (including the ones nobody registered and the stdio ones that never open a socket), flagged against an allow-list. | Network-in. Which GenAI apps the whole org reaches, seen at cloud scale, with users steered toward sanctioned tools — visibility no single endpoint could assemble. |
| Constraining which model / destination an agent may reach | A per-agent destination map + endpoint allow-list, enforced locally at the tool call, mapping which model or SaaS each agent actually reaches. | Access policy at the exchange, allowing or blocking GenAI destinations for the entire fleet in the network path — sanctioned-vs-unsanctioned app control, including Microsoft Copilot. |
| Coverage of the coding agents (Claude Code, Copilot, Codex) | Wraps them directly. Hook enforcement validated end to end on Claude Code; Codex and Copilot CLI block via their pre-tool hooks, not yet validated live. Agent on macOS, Windows and Linux. | Governs their traffic as part of general GenAI-app control — broad, but egress-centric: it sees what the agent transfers, not the in-process tool call. |
| Judging Claude Enterprise prompts through Anthropic Inference hooks (claude.ai, Claude Code, Cowork) | Its own hook server, on a host you operate. moorai-inference-hook verifies Anthropic’s signature, judges prompts, tool results, attachment text and tool calls with the same runtime as the endpoint agent, and answers deny with threat ids, never content. Fail open or fail closed is the admin’s choice. | AI Guard behind the hook. Zscaler announced on August 5, 2026 that AI Guard evaluates the whole Claude conversation as it develops, with one policy defined in the Zscaler platform enforced on every Claude Enterprise touchpoint. |
| Compliance mapping (OWASP LLM Top 10 / NIST AI RMF / ISO 42001 / EU AI Act) | Attached to each signed, content-free decision, so the compliance artefact is the enforcement record itself — and it contains no prompt content. | Across the whole estate, with the platform’s own enterprise certifications (SOC 2, ISO 27001, FedRAMP) behind it — a far wider scope than one developer laptop. |
Where Zscaler covers ground MoorAI does not
Zscaler covers far more ground than MoorAI. Zscaler is a full SASE/SSE platform serving some of the largest enterprises in the world; MoorAI governs one layer: what AI agents do at the point of action, deepest on the coding agent on a developer’s machine. Almost everything a network-scale security platform does is off MoorAI’s map and has no roadmap onto it. If the requirement is broader than “coding agents on developer laptops,” the rows below are the answer and MoorAI is not the vendor.
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| MoorAI | Zscaler | |
|---|---|---|
| Inline DLP across all SaaS, web and cloud traffic — any app, no integration | ✗ | ✓ |
| Inline GenAI DLP — blocks PII / health / source pasted into ChatGPT, Gemini, Copilot at the network layer | ◐content-free browser extension guards chat apps on that device, not org-wide inline | ✓ |
| Secure Web Gateway — URL filtering, threat protection, malware/phishing blocking at cloud scale | ✗ | ✓ |
| ZTNA / private-app access | ✗ | ✓ |
| CASB — sanctioned and unsanctioned SaaS control at scale | ✗ | ✓ |
| Shadow-AI discovery at network scale, across the whole fleet | ◐discovers on endpoints where it's installed, not org-wide | ✓ |
| Governs GenAI web apps (ChatGPT, Gemini, Copilot) inline for every user and device | ◐content-free extension now guards 8 chat apps — ChatGPT, Claude, Copilot, Gemini, Perplexity, Mistral, DeepSeek, Grok — on that device, not org-wide inline | ✓ |
| 18+ built-in prompt/response detectors (prompt injection, jailbreak, secrets, toxicity, malicious URLs) applied inline | ◐overlapping threat classes in the 85-threat matrix, enforced on-device not inline | ✓ |
| Email DLP and endpoint DLP across USB, print and screenshot channels | ✗ | ✓ |
| ML-based cloud DLP with train-your-own-classifiers across the estate | ✗deterministic on-device matrix | ✓ |
| Global cloud fabric — SASE backbone with carrier-grade SLAs | ✗ | ✓ |
| Enterprise compliance and sovereignty certifications (SOC 2, ISO 27001, FedRAMP) | ✗ | ✓ |
| Covers Linux, mobile (iOS / Android) and unmanaged / BYOD devices | ◐agent on macOS, Windows and Linux; desktop app on macOS and Windows; no mobile / BYOD | ✓ |
Where MoorAI is stronger. Content stays on the device, and it still works offline. Every MoorAI verdict is computed by code you can read, on the machine where the action is about to happen, from a matrix that needs no cloud round-trip. That is what makes offline and air-gapped enforcement real, keeps policy enforced when the network is gone, makes stdio MCP servers (Claude Desktop’s filesystem, git and database servers, which never open a socket) governable at all, and makes an audit trail an auditor can read without reading anybody’s prompts. If your constraint is a regulator, an air gap, or a legal team that will not approve prompt content leaving the endpoint, that constraint is the product.
Where Zscaler is stronger. Everything except that. One platform consolidating SWG, ZTNA, CASB and inline DLP across the whole fleet is a procurement argument MoorAI has no answer to, and the inline architecture covers every app that speaks HTTPS with no integration written for it. Its AI Guard governs the GenAI your whole organization touches (public SaaS chat, Microsoft Copilot, sanctioned-vs-unsanctioned app control), not just the coding agent on a developer laptop, and it reaches platforms MoorAI does not: Linux, mobile, unmanaged devices. It meets enterprise procurement where it actually is: a global backbone, SOC 2, ISO 27001, FedRAMP. If your AI risk is broader than “coding agents on developer machines,” Zscaler is the larger product.
Layering the two. They fail in different places. Run Zscaler for organization-wide GenAI and data governance: the SaaS estate, the browser chat surfaces, the Copilot governance, the traffic your fleet already routes through the Zero Trust Exchange. Run MoorAI on the developer machines (pushed across a fleet through MDM, Intune and Jamf), where the tool call has to be refused in-process rather than judged remotely, where a coding agent’s local Bash, Read and stdio MCP calls never generate any traffic to inspect, and where the evidence an auditor reads must contain no one’s prompt content at all. Inline inspection and on-device action interception govern two different points in the same flow.
Anthropic Inference hooks. Claude Enterprise can hold each governed prompt from claude.ai, Claude Code and Cowork until an organisation’s own AI security server answers allow or deny, and Zscaler has announced that AI Guard works behind that hook. MoorAI ships a hook server too, moorai-inference-hook, which runs on a host you operate, so the transcript Anthropic sends stops there, and its deny reasons and alerts carry no prompt content. The hook only sees Claude Enterprise traffic. Codex, Cursor, Copilot CLI, Gemini CLI, a local Ollama model and custom agents on other SDKs never send Anthropic a request, which is where MoorAI’s on-device hooks, model proxy and MCP gateway apply the same policy.
Zscaler capabilities are mapped from Zscaler’s own published SASE/SSE and AI-security (AI Guard / GenAI data protection) material as of 2026. Quoted figures — the 18+ prompt/response detectors and inline GenAI DLP — appear in that material. The Inference hooks rows rest on Zscaler’s August 5, 2026 announcement of AI Guard with Claude Inference hooks and on Anthropic’s Inference hooks documentation; MoorAI’s hook server has been tested against signed sample frames built from Anthropic’s published schema, not against Anthropic’s live service. Platform-level attestations (SOC 2, ISO 27001, FedRAMP) are Zscaler’s; no AI-Guard-SKU-specific attestation is claimed here. ◐ = partial — present but narrower than the other column. — = unconfirmed, not necessarily absent: it marks a capability Zscaler’s published material does not clearly document at the endpoint-action level, not a claim the platform lacks it. MoorAI marks reflect shipped capability: hook enforcement is validated end to end on Claude Code, with Codex and Copilot CLI blocking through their own pre-tool hooks but not yet validated against the live agents, the agent runs on macOS, Windows and Linux (the desktop app on macOS and Windows), fleet rollout ships via MDM (Intune and Jamf), and MoorAI does not ship TLS/traffic inspection — its capability spec permits local TLS inspection as an opt-in layer, and no row here credits it. Image handling uses on-device OCR (macOS Vision.framework / Windows OCR); a provider-key fallback exists only on a platform with no native OCR engine, and even then goes device → your own provider directly, never to MoorAI — structurally unreachable on the macOS and Windows builds, whose native engines leave no gap. Zscaler, Zero Trust Exchange and AI Guard are trademarks of Zscaler, Inc.; this is an independent capability comparison, not an endorsement, and every product here evolves — verify specifics against current documentation.