MoorAI vs dope.security

Where MoorAI goes deeper

Agent-native depth, and the data-residency invariant that comes with it. A network gateway classifies what an agent is about to transfer; an action-centric guard classifies the call — before it runs, and without the plaintext leaving the device. The sharpest divergence is the one row that names dope’s own mechanism: to classify, dope sends the text off-device; MoorAI does not.

Where the two genuinely overlap

dope’s AI work is real and specific, and it deserves to be stated plainly rather than marked absent. Both keep the enforcement on the endpoint with no cloud PoP rerouting traffic, both inspect an AI prompt or upload in real time before it leaves the machine, and both discover shadow AI — and shadow MCP — across browsers, desktop apps and CLIs. Anyone evaluating the two should expect a real overlap here.

Where dope.security covers more ground

This is the honest half of the page, and it is the one where MoorAI is marked absent. Because dope’s on-device proxy sits in front of everything that speaks HTTPS, it covers an enormous surface MoorAI does not attempt and has no roadmap to attempt — general web security, CASB, and full web-traffic TLS inspection that ships today where MoorAI’s is only a roadmap layer. If the requirement is “one agent that sees and filters all of our web and AI egress,” that is dope’s product and not ours.

The two rows worth explaining. On traffic-to-process attribution MoorAI is honestly partial: it maps which model or SaaS each agent reaches, but it does not attribute arbitrary web traffic from every process on the machine the way an OS-level SSL-inspecting proxy does — that is dope’s to own. And on browser AI chat MoorAI is partial too: a companion browser extension covers the major chat surfaces, where dope’s proxy covers every client with content-aware DLP and no extension. Neither is marked ✓ for MoorAI, because neither ships at dope’s breadth.

Where dope.security is stronger. Breadth, and it is not close. On-device SSL inspection covers anything that speaks HTTPS — browsers, thick clients, cloud AI desktop apps, tools nobody has written an integration for — and attributes traffic back to the process that generated it. It brings a full web-security stack (URL filtering, anti-malware, cloud-app controls, CASB) that MoorAI does not attempt, and its published telemetry research across thousands of devices gives it real category authority. If your problem is “we have no idea what AI our whole company is reaching,” that is dope’s strength, not MoorAI’s.

Where MoorAI is stronger. Depth, and data residency. A proxy sees a request; MoorAI sees the decision — which tool, which arguments, allowed or blocked, before execution. That includes the surface a network gateway structurally cannot observe: a local MCP server over stdio, a file read, a shell command, an IDE plugin acting on the repo on disk. And the classification itself runs on the device: prompt content is not extracted and forwarded to a third-party API to be judged sensitive, so the guard can never become the leak. The exception is named rather than buried — an administrator can turn on a capture tier, which does store the matched text it was enabled to collect; it is opt-in, not the default. Every decision lands as a signed, content-free record — with in-toto/SLSA attestation of the action chain and a CycloneDX/SPDX AIBOM of the agent’s model, MCP and skill surface behind it — mapped to ISO 42001, NIST AI RMF, the EU AI Act, and both the OWASP LLM Top 10 and the OWASP Agentic Top 10 (ASI01–ASI10), and MoorAI’s agent is open source (MIT) — so “the plaintext never leaves” is auditable rather than asserted. The endpoint agent now runs on macOS, Windows and Linux, and when a prompt is ambiguous an opt-in, default-off on-device model — a loopback Ollama or the device’s own provider key already present — can add a second opinion without any content leaving the machine. On-device OCR stays first-class on macOS and Windows and only opportunistic on Linux, so no row claims Linux OCR at parity.

The clearest example is dope’s own DLP path. To decide whether a Claude prompt or an uploaded file is sensitive, Dopamine DLP extracts the text and sends it to dope.cloud, which forwards it to OpenAI — “The endpoint sends the extracted text to the dope.cloud Dopamine DLP API,” which “uses OpenAI’s zero data retention APIs.” (dope.security/post/meet-dopamine-dlp) Zero-retention is a real and good property — the text is not stored or trained on. It is not the same property as content-free. The plaintext still leaves the endpoint and is read by two external services to be classified. MoorAI’s line was never “we refuse to inspect” — it is that plaintext never leaves the device: it is classified in place, and only category · risk · keyed one-way hash go to the console. On-device inspection paired with off-device classification is not content-free, and that distinction is the whole argument.

Claude Desktop shows the other gap. Its most sensitive MCP servers — filesystem, git, a local database — run locally over stdio. That traffic never touches the network, so an on-device SWG inspecting SSL egress sees nothing at all. MoorAI installs as an MCP proxy in Claude Desktop’s own config, so every tool-call passes through the same policy engine, allow-list and argument rules as the Claude Code hook — and lands as a signed, content-free record. The converse, stated plainly: Claude Desktop’s chat traffic to the model API is not intercepted by that proxy — it governs tool-calls, and MoorAI covers the chat surface today only through discovery and the browser extension. Local TLS inspection is on the roadmap as an optional, opt-in layer, off by default — and it is not shipping today, so no row on this page credits it. When it ships it holds the same invariant as everything else: decryption and classification both happen on the device, content is never sent to a third-party API to be classified, and only category · risk · keyed one-way hash leave the machine.

Use both, honestly. They sit at different layers and are more complementary than competitive. Run dope.security for organization-wide web and AI egress control across a mixed fleet — every app that speaks HTTPS, a full web-security stack, CASB. Run MoorAI where developers run coding agents with terminal and filesystem access, where you need the MCP layer governed at call time, where prompt content must never leave the machine, and where the evidence an auditor reads must contain no one’s prompt content at all.

Comparison reflects dope.security’s publicly published product and blog material as of 2026 and MoorAI’s shipped capabilities; quoted phrases are dope’s own, from dope.security/post/meet-dopamine-dlp. ◐ = partial — present but narrower than the other column. — = unconfirmed, not necessarily absent: it marks a capability their published material does not clearly document. MoorAI marks reflect shipped capability; local TLS inspection is on the roadmap and is not shipping, and no row here credits it. dope.security is a trademark of its respective owner; this is an independent capability comparison, not an endorsement, and every product here evolves — verify specifics against their current documentation.