Quint vs Prompt Security: When to Use Each for AI Agent Security
Prompt Security and Quint both show up in AI security evaluations, but they're solving for different personas and different risks. Prompt Security is focused on employees using AI, primarily browser-based tools like ChatGPT, Gemini, and Copilot chat. Quint is focused on autonomous AI agents running on endpoints, things like Claude Code, Cursor, and MCP-connected tools that read files, execute commands, and make network calls on their own. The overlap is smaller than you'd think.
What Prompt Security does well
Prompt Security has built a solid product for a real and urgent problem: employees pasting sensitive data into AI chat interfaces.
Their browser extension approach is well-suited for this. It intercepts traffic to AI SaaS products (ChatGPT, Gemini, Copilot web, and others) and can inspect what employees are sending. If someone pastes a customer list, an API key, or a block of proprietary source code into ChatGPT, Prompt Security can flag or block it.
Their shadow AI discovery deserves credit. Organizations genuinely don't know which AI tools their employees are using. Prompt Security's browser-level visibility gives them a catalog of every AI web app employees access. That's useful data for a CISO who's trying to get a handle on the problem.
Their DLP (data leak prevention) for AI is a natural extension of traditional DLP, adapted for the AI context. Scanning outbound text to AI services for PII, credentials, and proprietary data is the right instinct, and they've executed on it.
They also cover prompt injection prevention on the input side, scanning prompts before they reach the model. This is useful for organizations building their own LLM-powered products.
What Prompt Security is not designed to do
Prompt Security's architecture is oriented around browser extensions and network proxies. This gives them strong coverage of web-based AI interactions. It also defines their boundaries.
Autonomous agent actions. When Claude Code executes a shell command, reads a file from the local filesystem, or spawns a subprocess, those actions don't flow through a browser. They happen at the OS level, as direct system calls. A browser extension has no visibility into what a local process does on disk or on the network outside of browser traffic.
Behavioral sequence analysis. Prompt Security inspects individual interactions: this prompt, this response, this data transfer. It doesn't build a behavioral model of an agent over time. It can tell you "an employee sent source code to ChatGPT." It can't tell you "Claude Code read .env, then opened a socket to an IP it has never contacted before, then wrote to ~/.ssh/authorized_keys." The second pattern requires maintaining state across OS-level actions and scoring the sequence, not the individual event.
MCP tool poisoning. MCP tool descriptions are loaded by the agent runtime, not through a browser. A poisoned tool description that tells the agent to silently exfiltrate data never passes through a web interface. It's injected at the protocol level between the MCP server and the agent. Prompt Security's browser and proxy layer doesn't intercept MCP protocol traffic. (We covered this attack class in MCP Tool Poisoning.)
Endpoint-level agent visibility. If a developer installs a new AI tool that runs as a native application (not in a browser), Prompt Security's browser extension won't see it. Their network proxy might catch outbound API calls if configured correctly, but won't see what the agent does locally: file access, process creation, filesystem traversal.
Intent vs. action divergence. The agent tells the LLM it's "reading documentation." The OS says it's reading /etc/passwd. Detecting this kind of divergence requires watching both the agent's declared actions and its actual syscalls simultaneously. Browser-level monitoring sees the declared side. OS-level monitoring sees the actual side. You need both layers observed in parallel to catch the gap.
What Quint does differently
Quint deploys at the endpoint. We install an agent on the machine (macOS, Linux) that uses OS-level instrumentation to observe everything every AI agent does, regardless of whether it runs in a browser, a terminal, an IDE, or a standalone application.
Per-agent behavioral baselines. We model what normal looks like for each agent, each user, each session. Claude Code on a security engineer's machine has a different baseline than Cursor on a frontend dev's laptop. When behavior deviates from the baseline, we score it. A single anomalous file read is noise. A sequence of anomalous actions is a signal. (We explain this scoring model in Behavioral Security for AI Agents.)
Proxy/kernel divergence. We observe the agent at two layers: what it says it's doing (the tool calls, the API traffic) and what it actually does (the syscalls, the file I/O, the network connections). When those two layers disagree, that's our primary detection signal. This is how we catch attacks where every individual action looks legitimate but the sequence is a breach.
Full agent coverage. We see Claude Code, Cursor, Copilot, Windsurf, custom MCP agents, and anything else that runs on the endpoint. No configuration per tool. If it runs as a process, we observe it.
Immutable audit trail. Every action, every behavioral score, every deviation gets logged. Not chat transcripts. OS-level action logs: which files were read, which network connections were opened, which processes were spawned, with timestamps and process context.
Comparison table
| Capability | Prompt Security | Quint | |---|---|---| | Primary focus | Employee AI usage (ChatGPT, Gemini, Copilot web) | Autonomous AI agents (Claude Code, Cursor, MCP tools) | | Operating layer | Browser extension + network proxy | OS + network + proxy | | Detection signal | Content inspection (DLP, prompt analysis) | Behavioral sequence scoring | | Shadow AI discovery | Yes (browser-level: which AI web apps are used) | Yes (endpoint-level: which AI processes are running) | | DLP for AI chats | Yes (core capability) | No (not our focus) | | Detects MCP tool poisoning | No | Yes | | Behavioral baselines | No | Yes (per-agent, per-user, per-session) | | Detects prompt injection | Yes (input scanning) | Detects the effects of successful injection | | Audit trail | AI interaction logs (prompts + responses) | OS-level action logs (files, network, processes) | | Deployment | Browser extension + proxy | Endpoint agent | | Covers non-browser AI tools | Partial (proxy may catch API calls) | Yes (all processes on the endpoint) |
When to use Prompt Security
Your primary concern is employees using AI web apps and potentially leaking sensitive data through those interfaces. You want to know which AI tools your workforce is using. You want DLP policies that cover AI chat services. You're worried about someone pasting production credentials into ChatGPT or uploading a proprietary dataset to a fine-tuning service.
This is Prompt Security's strength. They've built specifically for this problem, and their browser extension approach gives them granular visibility into web-based AI interactions.
If your risk is "employees sending data to AI services they shouldn't be using," Prompt Security is a strong fit.
When to use Quint
Your primary concern is AI agents running on endpoints and taking autonomous actions. Your developers use Claude Code, Cursor, or other coding agents that read files, execute shell commands, make network calls, and interact with MCP servers. You need to know what those agents actually do, detect when they deviate from normal behavior, and maintain an audit trail that shows the full action history.
If your risk is "agents doing things on our machines that nobody asked them to do," that's what Quint is built for. (The full threat landscape is mapped in The AI Agent Threat Model.)
When to use both
You have employees using ChatGPT and Gemini for day-to-day work, and you have developers running Claude Code and Cursor on their machines. The first group needs DLP and shadow AI controls at the browser layer. The second group needs behavioral monitoring and audit trails at the OS layer. These are genuinely different attack surfaces.
Prompt Security covers the browser-based AI usage. Quint covers the endpoint-based agent activity. The overlap between them is minimal, which means combining them isn't redundant.
FAQ
Does Quint replace Prompt Security?
No. Quint doesn't do DLP for browser-based AI chat. Prompt Security doesn't do OS-level behavioral monitoring of autonomous agents. If you have both problems, you need both tools.
Both claim shadow AI discovery. What's the difference?
Prompt Security discovers AI web apps employees access through the browser. Quint discovers AI agent processes running on the endpoint. An employee using ChatGPT in Chrome shows up in Prompt Security. Claude Code running in a terminal shows up in Quint. A new MCP server a developer connected to their IDE shows up in Quint. Different layers, different coverage.
Which one catches a developer accidentally exfiltrating code through an AI agent?
If the developer pastes code into a browser-based AI tool, Prompt Security catches it. If an AI agent running locally reads source files and sends them to an external endpoint via a network call (not through a browser), Quint catches it. The exfiltration path determines which tool sees it.
Quint monitors what AI agents actually do. If you want to see it on your own fleet, request a demo.