Inspecting a prompt in the cloud means it already left the building.
Nightfall and most DLP platforms scan content after it reaches their service — over an API, a proxy, or an integration with a sanctioned app. That's a fine model for files sitting in Google Drive. It doesn't work for a sentence someone is typing into ChatGPT right now, from a personal account, on a browser tab nobody's watching.
| Cloud DLP (e.g. Nightfall) | Kavara | |
|---|---|---|
| Where detection happens | After the request reaches their cloud or API — the data has already left the device. | In the browser, before the request is sent anywhere. |
| Personal AI accounts | Usually blind to them — most integrate with sanctioned, managed SaaS tenants only. | Covered — works on any account, in any tab, on the device. |
| What has to leave the device to detect a leak | Content (or a fingerprint of it) typically has to reach their service to be classified. | Nothing. Detection runs against on-device patterns — no reference data leaves the browser. |
| Time to a first real signal | Often weeks — proxy or API integration, sanctioned-app onboarding, policy tuning. | Minutes — install the extension, share an enrollment code. |
| If the vendor gets breached | Depends entirely on how much inspected content that vendor retains. | Nothing sensitive to lose — the database only ever holds a category and a count. |
| Enforcement | Typically alert or block after the fact, once the request has already gone through. | Tokenize in place — the AI tool still gets a usable prompt, nothing sensitive in it. |
"Cloud DLP" describes how that category of tool works architecturally — inspection happens after content reaches the vendor's service. Nightfall is named as the concrete, well-known example; the comparison is about the model, not a specific feature list.
A proxy can only protect what crosses it.
Claude Desktop, a personal ChatGPT tab, a local MCP server — none of that touches a corporate network or a sanctioned SaaS tenant. If detection lives in the cloud, all of that is invisible by construction, not by a missing feature. Moving detection into the browser, onto the device, is the only way to see the moment it actually happens.
Questions worth asking any vendor here.
How is this different from Nightfall or other cloud DLP tools?
Cloud DLP inspects traffic after it reaches a proxy or API — which means the raw prompt has to leave the browser (and often the country) before anyone decides whether it's sensitive. Kavara makes that call on-device, before the request is ever sent, so there's no raw payload in transit for a cloud scanner to see or store in the first place.
Does Kavara read or store our prompts?
No. Detection happens in the browser, and our database has no field for raw prompts, responses, or secrets. We persist a category and a count — never the content itself.
Will it slow my team down?
Tokenization happens locally and the AI tool still gets a usable prompt, so the workflow is unchanged. Most teams start in Monitor mode, which is completely invisible to employees.
Run it next to whatever you have today.
Kavara installs in minutes and doesn't require ripping anything else out. Pilot it on a real team and see what it catches that your current stack can't.