// overview

Who it's for

MoorAI lets your team use AI tools while keeping your company's information out of them.

// the problem, in plain terms

Your team wants AI. Your company can't afford the risk.

Your employees already use AI — ChatGPT, Claude, Copilot — whether or not IT approved it (that's shadow AI). It's on your machines right now, and you can't see how it's being used.

To get useful answers they paste in real company information — source code, API keys, customer records. Once it's sent, it can be stored or learned from, and you can't take it back.

Leaks are not the only risk: there is also whether AI is used responsibly at all. MoorAI gives you visibility and control over both, without reading a single line of what your people write.

// who it's for

One agent, two kinds of user.

The same on-device engine, scoped to whoever's using it. In every case the checking happens locally — content stays on the machine, and no one reads what was typed.

// everyone at work

Employees

Your team already pastes source code, credentials, and customer data into ChatGPT, Claude, and Copilot. MoorAI checks what's about to be sent on the machine, then coaches, warns, or blocks before anything sensitive leaves. The console never receives the prompt unless an administrator turns on a capture tier.

Coach · alert · block · content-free signals

// coding agents

Developers

Enforcement goes deepest here. MoorAI hooks the agent itself: it blocks a secret being read into context, inspects MCP tool-call arguments, enforces an approved-MCP allow-list at call time, and reviews what the model says back, in addition to what gets typed.

Claude Code — hook enforcement, validated end to end
Codex / Copilot CLI — blocks via hook, not yet validated live

// vs the alternatives

On-device,
not in the cloud.

Most AI-security tools review prompts in a cloud, a gateway, or a vendor's own service, so content leaves the endpoint to be inspected. MoorAI reviews prompts on the device, so the prompt is not sent anywhere to be inspected, and it wraps the AI agents you already run. The head-to-heads below compare architectures, including where each alternative is stronger.

The AI-integrated development pipeline starts on the developer's machine: that's where prompts are written, files are read into context, and MCP tools are called. MoorAI is the first control point on that pipeline, upstream of the CI, SCA, and code-scanning tools that only see risk once code is already committed.

MoorAI vs Operant → MoorAI vs Lakera Guard → MoorAI vs SentinelOne → MoorAI vs Netskope → MoorAI vs BigID → MoorAI vs Harmonic → MoorAI vs Zenity → MoorAI vs Cycode → MoorAI vs Bifrost → MoorAI vs Ent →

// how to think about it

Where the prompt is inspected is the whole question.

Three questions security leaders are being asked about AI agents, and how an on-device model answers each.

// non-human identity

An NHI inventory for the developer endpoint

The market now counts ~144 non-human identities per human user — and coding agents, their MCP servers, and the local models on disk are exactly that: identities that act on your behalf. MoorAI’s agent discovery + AIBOM is the register for the ones living on the developer endpoint, where the highest-privilege agents actually run.

// the audit gap

Evidence you could hand an auditor in 90 days

Grant Thornton’s 2026 AI Impact Survey found 78% of 950 senior leaders lack strong confidence they could pass an independent AI-governance audit within 90 days. MoorAI closes that gap with signed decisions, an AIBOM, and framework packs — evidence that is auditable by construction, not a dashboard screenshot.

// the content-free wedge

Where inspection runs, and what leaves

Every AI-security tool inspects prompts. They differ on where that inspection happens and what leaves: a category, a risk level, and a keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival) on the device, or the actual content in a vendor’s cloud. MoorAI inspects prompts and responses, on the machine, so the inspection never becomes egress.

In compliance terms: data minimization and residency by construction. Because the check runs where the prompt is typed, most “which jurisdiction, which lawful basis” questions don’t arise: there is nothing to transfer.

// choosing a product

Is MoorAI the right fit?

Answer these three before you build a shortlist; each answer says where MoorAI belongs and where something else does.

// 01 · where the risk sits

Is the risk the AI agents on developer machines, or AI use across the whole organization?

Organization-wide: if the concern is SaaS chat, copilots inside office apps and agents a vendor hosts, a governance platform built for that scope is the better primary tool. MoorAI doesn't see SaaS or hosted agents. Its browser extension guards 8 chat apps, but only on devices where MoorAI is installed. Run MoorAI next to the platform to cover the developer endpoint.

Developer machines: this is the case MoorAI is built for. The agent runs on the laptop, and so does the check.

// 02 · what you already run

Do you already have endpoint, network and DLP tooling, or do you need one platform?

You have the stack and need the coding-agent gap closed: MoorAI adds the layer those tools lack. It sees secrets being read into an agent's context, MCP tool-call arguments, shell commands and model output, and it replaces none of what you run. Pick it when prompt content must stay on the device: only a category, a risk level and a keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival) leave the machine.

You want one platform for everything: MoorAI won't be it. It is not a sandbox and not a SASE platform; its egress proxy enforces declared egress rules only on the connections routed through it.

// 03 · developer-specific priorities

Is the priority coding agents, MCP servers, skills and rules files?

Yes: that is MoorAI's core. A PreToolUse hook inside the agent, an MCP gateway with an approval-gated allow-list and drift detection, moorai scan as a pre-install gate for skills, and integrity checks on files like CLAUDE.md and .cursorrules.

Review it
on the device.

Coach, alert, or block — before the prompt reaches the agent.

Community agent on GitHub Read the docs ↗