Quint vs Prompt Security: When to Use Each

Every few weeks someone forwards me a bake-off spreadsheet with our logo in one column and Prompt Security's in another, and every time I sigh a little. We don't really do the same thing.

Prompt Security is built around a browser extension. When your sales manager pastes a customer list into ChatGPT, they can see it and stop it. That's the whole pitch, and it's a good pitch — because that problem is absolutely real and most orgs have nothing covering it.

We're built around an endpoint agent. When a developer's Cursor instance starts reading files it's never touched before and opens a socket to an IP none of your other machines have ever connected to, we see it. Different people, different risks, different layer of the stack.

What they're good at

Giving credit where it's due: their browser extension approach is the right tool for the job they chose. Intercepting traffic to ChatGPT, Gemini, Copilot web, all the other web AI tools — that's a lot of ground to cover, and a browser extension covers it cleanly in a way that a network proxy alone can't (TLS, auth context, page structure, you get more signal from inside the page).

Their shadow AI discovery is useful for the "what are my employees even using" problem. Most CISOs I talk to genuinely don't know, and a browser extension that just tells them is a cheap win.

Their DLP-for-AI angle is a clean extension of classic DLP into the AI context. Same instincts, tuned for outbound text to AI services. PII, secrets, proprietary code heading to third-party APIs — they see it and flag it.

On the input side, they do prompt injection screening for teams building their own LLM apps. Fine for that use case.

Where the browser layer runs out

Now the unfun part. A browser extension can only see what a browser does. That's a real boundary.

When Claude Code runs rm -rf node_modules on a developer's laptop, there's no browser involved. When Cursor reads ~/.env during an auto-suggest, there's no browser involved. When an MCP server injects a poisoned tool description into an agent's tool list, the browser never touches it. Their network proxy catches some of this if it's configured to proxy agent traffic, but:

• A native app that pipes through raw HTTPS gets a certificate error and might refuse to route. Or it just goes around.
• An agent making a syscall to read a file doesn't generate network traffic at all. There's no packet to proxy.
• MCP protocol traffic between an agent and a local MCP server is usually loopback or IPC. Off-network entirely.

Same for behavioral sequences — the "this read plus this socket plus this write" pattern. It requires maintaining state across OS-level actions. A DLP product scanning individual outbound requests doesn't do that because it wasn't built to.

And the intent-vs-action thing I keep coming back to: catching "agent claims to be editing a config" and "OS records a write to /etc/sudoers" as a mismatch requires watching both layers at once. Browser sees the declared half. It doesn't see the actual half.

What we do on the endpoint

Short version: install a macOS/Linux agent, we watch every AI process on the machine.

Per-agent baselines. Per-user, per-session. Claude Code on your platform team lead's laptop has a different rhythm than Cursor on a frontend dev's — we model each one separately, score deviations in real time. Single anomalous read? Noise. Sequence of them? Signal. More on the scoring model here.

Proxy-vs-kernel divergence. We read the agent's declared intent and the OS's actual record, compare, flag when they disagree. This is the one that catches the attacks where everything looks legit in isolation.

Coverage. If it's a process on the machine, we see it. Claude Code, Cursor, Copilot, Windsurf, some homegrown MCP agent someone vibe-coded last weekend — doesn't matter. No per-tool configuration.

Immutable audit trail. Not chat logs — actual OS receipts with process context and timestamps.

The table

| | Prompt Security | Quint | |---|---|---| | Primary user | CISO worried about employees and ChatGPT | Security team worried about agents and endpoints | | Layer | Browser extension + network proxy | OS + network + proxy on the endpoint | | Main signal | Content inspection (DLP, prompt screening) | Behavioral sequence scoring | | Shadow AI | Discovers web-app AI usage | Discovers running AI processes | | DLP for AI chats | Yes | No (not the game) | | MCP tool poisoning | No | Yes | | Behavioral baselines | No | Per-agent, per-user, per-session | | Prompt injection | Screens the input | Catches the effects if one lands | | Audit trail | Prompt/response logs | OS-level action logs | | Native app coverage | Partial (proxy-dependent) | Yes |

Which one do you want?

You want Prompt Security if the scenario that scares you is somebody in HR pasting a spreadsheet of employee records into a free ChatGPT account. That's a DLP-for-AI problem and browser extensions are the right hammer.

You want us if the scenario that scares you is the AI coding agent on someone's laptop doing something nobody asked it to. That's an endpoint behavior problem and it needs an endpoint tool. Full threat landscape here.

A lot of our design partners run both. They cover different populations (knowledge workers vs. engineers) and different surfaces (web AI apps vs. native agents), and the overlap is pretty small.

FAQ

Does Quint replace Prompt Security?

Nope. They handle browser-based AI usage DLP; we handle autonomous-agent behavioral monitoring. If you have both populations, you probably need both tools.

You both claim shadow AI discovery — what's actually different?

Different layer, same word. Prompt Security discovers that your marketing lead is using Gemini. We discover that someone on your platform team has a custom MCP agent running in the background. Neither one sees the other.

If a developer exfiltrates code via an AI agent — who catches it?

Depends on the path. Pastes it into a browser AI tool? Prompt Security. Runs an agent that reads source and POSTs it somewhere? Us. Different paths, different tools.

If you want to see behavioral monitoring on your own fleet, book a demo.