MoorAI vs Ent
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Both act on the endpoint in real time. They cover very different amounts of ground. Ent (ent.ai) is a broad intent-aware prevention platform, “Intent-Aware Security for the Enterprise” (ent.ai), that evaluates both human and AI-agent activity as it happens, using context from applications, browsers, workflows and data movement to detect insider risk, govern AI usage, prevent data loss and stop last-mile threats. It ships as one lightweight endpoint agent on Windows, macOS, Linux and the browser. (ent.ai launch material, Jun 2026)
MoorAI is narrower on purpose: content-free runtime guardrails for AI agents, apps and APIs, deepest on the coding agents your developers run — Claude Code, Cursor, Codex. It inspects prompts, context reads, MCP tool calls and outputs on the device, and by default emits only a category, a risk level and a keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival). The comparison is architecture-level: a broad endpoint-prevention platform versus a focused, content-free, open-source coding-agent guardrail. Ent is wide; MoorAI is deep on one surface and keeps prompt content on the machine.
Where MoorAI goes deeper
Ent evaluates activity and intent; MoorAI governs the coding-agent runtime itself (the tool call, the MCP layer, and the evidence trail) and does it content-free. Ent’s published material is built around behavioral context collected off the endpoint for investigation; the on-device, content-free, agent-runtime rows below are the ones a broad prevention platform does not center on. A “—” means Ent’s published material does not clearly document the capability — not that it is absent.
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| MoorAI | Ent | |
|---|---|---|
| Prompt content never leaves the device (content-free by default) | ✓content-free by default | ✗built around collected behavioral context |
| Only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓redacted signals only | ✗full behavioral context |
| On-device MCP tool-call gateway — blocks before the call runs | ✓pre-execution, on-device | —not an MCP tool-call gateway in published material |
| MCP server allow-list at call time | ✓Agency Enforcement allow-list | —unconfirmed |
| Per-tool MCP argument rules | ✓per-tool arg inspection | —unconfirmed |
| Model-endpoint allow-list (Agency Enforcement) | ✓approved-endpoint allow-list | —app control; model-endpoint allow-list unconfirmed |
| Reviews AI output, not just prompts | ✓reviews prompts + responses | —observes activity; output review unconfirmed |
| Skill Analysis — inspects agent skill files for drift / poisoning | ✓skill-file drift detector | —file-activity monitoring; agent skill drift unconfirmed |
| Rules-file poisoning detection (fingerprint only) | ✓fingerprint only, content-free | —unconfirmed |
| Lethal-trifecta / cross-server toxic-flow detection | ✓named content-free detector | —unconfirmed |
| Cryptographically signed, tamper-evident decisions | ✓per agency decision | —unconfirmed |
| Per-agent assurance score | ✓per-agent, content-free | —unconfirmed |
| On-device OCR of image inputs (macOS Vision + Windows OCR) | ✓macOS Vision + Windows OCR first-class; Linux Tesseract opportunistic, no image egress | —unconfirmed |
| On-device model escalation on ambiguity — opt-in, default off | ◐regex decides; opt-in on-device model second opinion, content-free | —unconfirmed |
| 85-threat matrix mapped to OWASP LLM Top 10 / OWASP Agentic Top 10 (ASI01–ASI10) / MITRE ATLAS / STRIDE | ✓explicit threat crosswalk | —unconfirmed |
| AI compliance packs (OWASP LLM Top 10 / NIST / ISO 27001·42001 / EU AI Act) | ✓full crosswalk | —AI governance; full pack unconfirmed |
| Open source (MIT) — the content-free claim is auditable | ✓MIT | ✗proprietary |
| Runs standalone — no account, no console to stand up | ✓open agent, no account | ✗enterprise platform + console |
Where we overlap
Ent’s AI work is real and specific, and it meets MoorAI on the surface that matters most: seeing AI activity on the endpoint and acting before sensitive content reaches an AI application.
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| MoorAI | Ent | |
|---|---|---|
| Flag sensitive content before it reaches an AI application | ✓ | ✓ |
| Govern shadow AI on the endpoint | ✓ | ✓ |
| Discover AI apps and agents on the device | ✓ | ✓ |
| Evaluate AI-agent activity in real time | ✓ | ✓ |
| Real-time policy enforcement at the moment of risk | ✓ | ✓ |
| Single lightweight endpoint agent — no cloud PoP in the path | ✓ | ✓ |
| Per-agent destination map — which model or SaaS a tool reaches | ✓ | ✓ |
Where Ent covers more ground
Ent leads on this list. Ent evaluates people as well as agents, across every application on the machine, and closes surface MoorAI does not attempt and has no roadmap to attempt. If the requirement is one prevention platform spanning humans, apps and AI across a mixed fleet, that reach is Ent’s pitch and not ours.
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| MoorAI | Ent | |
|---|---|---|
| Human insider-risk detection — people, not just agents | ✗ | ✓ |
| Whole-endpoint behavioral monitoring across applications, browsers, workflows | ✗ | ✓ |
| Data-loss prevention beyond AI — general data movement | ✗ | ✓ |
| Stop last-mile threats | ✗ | ✓ |
| Application control across arbitrary apps | ✗ | ✓ |
| Natural-language investigation timelines across the endpoint | ◐ | ✓ |
| Just-in-time interventions across human + AI activity | ◐ | ✓ |
| Governs AI chat in the browser across arbitrary web apps | ◐ | ✓ |
| Linux endpoint agent (first-class parity) | ◐ | ✓ |
| SaaS or self-hosted with data sovereignty + managed fleet console | ◐ | ✓ |
Where MoorAI is stronger. Focus, privacy, and the record. MoorAI goes deep on the coding-agent surface: blocking a secret read into context, inspecting MCP tool-call arguments, enforcing an approved-MCP and model-endpoint allow-list (Agency Enforcement), reviewing outputs, and detecting cross-server toxic flows. It does this content-free by default: prompts never leave the machine, only a category, a risk level and a keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival). When a policy requires full-fidelity capture, an administrator can turn it on explicitly; capture is opt-in, not the default. Every agency decision lands as a signed, tamper-evident record mapped to the OWASP LLM Top 10, NIST, ISO 27001·42001 and the EU AI Act. It is open source (MIT), so the content-free claim is auditable, and it runs standalone with no account or console to stand up.
Where Ent is stronger. Breadth. Ent applies one intent-aware policy across humans, AI and applications, evaluating activity across browsers, workflows and data movement to detect insider risk, prevent data loss and stop last-mile threats. It reaches Linux at first-class parity, whereas MoorAI’s Linux release is the agent alone, with no desktop app. For an org that wants a single prevention platform spanning people, apps and AI, with SaaS or self-hosted deployment and full behavioral context for investigation, that reach is the pitch.
Use them together. They are more complementary than competitive. Run Ent for broad, whole-endpoint human + app + AI prevention across a mixed fleet. Run MoorAI where developers run coding agents with terminal and filesystem access, where you need the MCP layer governed at call time, and where the evidence an auditor reads must contain no one’s prompt content at all.
Ent capabilities described here are drawn from Ent’s own published product positioning and launch material (ent.ai, 2026); quoted phrases are theirs. Comparison is architecture-level. ◐ = partial — present but narrower than the other column. — = unconfirmed, not necessarily absent — it marks a capability Ent’s published material does not clearly document. MoorAI marks reflect shipped capability. Ent is a trademark of its respective owner; this page is not affiliated with or endorsed by Ent, and every product here evolves — verify specifics against Ent’s current documentation.