MoorAI vs SentinelOne

Where MoorAI goes deeper

Agent-native depth, and the content-free evidence trail that comes with it. A firewall blocks and redacts what an agent is about to transmit; an action-centric governor blocks the call before it runs — including local tool activity that never crosses the network for a proxy to inspect.

Where they overlap

SentinelOne’s AI work, the Prompt Security engine, is real and specific. It names the same coding agents MoorAI wraps, inspects prompts and outputs, discovers shadow AI, maps MCP servers, and verifies the integrity of agent skill and configuration files, a direct analog of MoorAI’s rules-file poisoning detection. If you are evaluating both, this is where they collide.

Where SentinelOne covers more ground

SentinelOne reaches much further here. The Prompt Security engine inspects content in the path, is aimed at the whole organization rather than one class of developer tool, and sits inside a full XDR platform, so SentinelOne covers a large surface MoorAI does not attempt and has no plan to. If the requirement is “one guardrail over every employee’s AI use, with inline redaction, wired into the SOC,” that is their product and not ours.

The rows worth explaining. On content leaving the device SentinelOne gets ✗ because the Prompt Security mechanism requires reading the content to redact it, and it publishes no content-free egress guarantee. MoorAI emits category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival) and nothing else, the agent is MIT, and anyone can read the code and confirm there is no content path off the device. The marks record that architectural difference. On browser AI chat MoorAI is partial: a companion extension covers the major chat surfaces, where SentinelOne’s extension is built for exactly that job across the whole fleet and 15,000+ services.

Where MoorAI is stronger. The tool call, and the record of it. SentinelOne’s AI enforcement is content- and egress-centric: it inspects what an agent is about to transmit and redacts or blocks it, which catches data leakage. It does not see the action: an agent about to run a destructive shell command, read a credential file, or drive a local MCP server over stdio whose filesystem, git and database calls never touch the network, so a content-inspecting firewall sees nothing at all. MoorAI sits at that exact point: allow-list the server, constrain the arguments, block before execution, and land a signed, content-free record mapped to ISO 42001, NIST AI RMF, the EU AI Act and the OWASP LLM Top 10.

Where SentinelOne is stronger. Breadth, inline redaction, and the platform. One guardrail over every employee’s GenAI use (thousands of SaaS tools, browser chat, and your own homegrown AI apps), with PII, secrets and source code scrubbed in real time, plus pre-deployment red-teaming and a full XDR / SOC platform behind it, is a real procurement argument MoorAI has no answer to. MoorAI is macOS, Windows and Linux and governs coding agents and the agent services wired to it, not the whole company’s chatbot use. Those are different purchases for different problems.

Deploying both. Mostly, they complement each other. Run SentinelOne if you want one enforcement point over org-wide GenAI use with inline DLP across every employee and application, wired into an XDR/SOC platform. 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.

SentinelOne’s AI-security capabilities are the former Prompt Security, which SentinelOne announced its acquisition of in 2025 and is folding into the SentinelOne platform; they are mapped from SentinelOne’s and Prompt Security’s own published product and extension listings as of 2026, and quoted phrases are theirs (sentinelone.com, Chrome Web Store). ◐ = 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. SentinelOne and Prompt Security are trademarks of their respective owners; this is an independent capability comparison, not an endorsement, and every product here evolves — verify specifics against their current documentation.

Also in MoorAI, all on-device and content-free: a content-free browser extension guarding prompts across eight AI chat apps (ChatGPT, Claude, Microsoft Copilot, Gemini, Perplexity, Mistral, DeepSeek, Grok), the agent on Linux as well as macOS and Windows (a .deb or tarball for amd64 and arm64; the desktop app and its on-device OCR run on macOS and Windows), an opt-in, default-off model second opinion (a loopback Ollama model on the device, or, only if an administrator chooses it, Anthropic’s API with an API key already on the device), MDM fleet rollout via Intune and Jamf, Skill Analysis (a content-free inventory and intent-labelling of the entire skill surface an agent auto-loads: SKILL.md, subagents, slash commands, MCP configs, hook-bearing settings), a per-agent destination map of the hosts and MCP servers each agent actually reached, on-device OCR of pasted screenshots (macOS Vision / Windows OCR), a lethal-trifecta detector, rules-file poisoning detection (CLAUDE.md / .cursorrules), a per-agent assurance score, cryptographically signed agency decisions, and in-app signup with auto-enroll. Detections are scored against a 85-threat matrix mapped across the OWASP LLM Top 10, MITRE ATLAS, STRIDE, NIST AI RMF, ISO 42001 and the EU AI Act.