MONITOR
Watch and log AI usage without changing a thing. The recommended way to start.
INFERENCE
Every prompt, screened before it leaves.
Your teams use enterprise ChatGPT, Copilot, Claude, and Gemini. Secuvy connects as an MCP server between them and your data, and screens every prompt before it leaves your environment.
Enterprise ChatGPT ·Microsoft Copilot ·Claude ·Gemini ·Internal RAG apps ·LLM APIs
THE RUN-TIME GAP
You cannot catch a leak you cannot recognize.
Classifying and tagging your training data was the first problem, and knowing what every file is solved it. Inference is the next one: prompts, retrieval, and API calls pull that same data into production on every request.
58% of IT leaders call classifying data for AI their hardest technical problem.
KOMPRISE, 2026 →A guardrail can only stop what it recognizes. Pattern rules miss the file that matters, and native controls stop at one vendor's walls.
The classification that tagged your data is the same thing that makes a leak visible at run time. That is the piece almost nobody has in place.
THE GO-BETWEEN
One control layer, in front of every model.
Secuvy connects as an MCP server between your people and the models they use. It reads every prompt and file in context, and acts on your policy before anything leaves your environment.
Your people
a prompt, a file, an agent, an API call
Secuvy MCP server
reads in context · monitor · block · mask · redact
Any enterprise LLM
ChatGPT · Copilot · Claude · Gemini
powered by your data, learned in place, so it knows what is sensitive
BLOCK
Stop a high-risk prompt or file before it is sent.
MASK
Send the request with sensitive fields hidden.
REDACT
Remove the sensitive content and let the rest through.
One policy, set once, holds across enterprise ChatGPT, Copilot, Claude, Gemini, your internal RAG apps, and your LLM API calls. Ordinary work passes untouched.
THE CORE PLATFORM
It can act at inference because it knows your data first.
The go-between recognizes a leak only because the platform underneath already learned your data. Two pieces put that knowledge in place, the same two that run on the homepage.
1.0 · DISCOVER & CLASSIFY
The reason it can detect anything at all.
Secuvy learns your data in place across 250+ sources, so at inference it already recognizes your IP, source code, contracts, CUI, PHI, and board material as your organization writes them. A check that knows your data catches what a generic filter cannot.
2.0 · PROVE
The classified picture it works from.
A Data Bill of Materials records what your data is and what each pipeline may use: inputs, exclusions, and status. Put it in place once, and the go-between has the picture it needs to make the right call at inference.
Explore the DBOM →Put these in place once, and the go-between has everything it needs at run time.
IN PRODUCTION FAST
In front of your models by end of day.
<1 hr
up and running
<24 hrs
first results on your data
250+
sources supported
0
files moved, changed, or touched
See it in front of your own models.
Book a demoDoes Secuvy work with more than one LLM?
Yes, it is model-agnostic. One policy holds across enterprise ChatGPT, Copilot, Claude, Gemini, internal RAG apps, and LLM API calls, so you set rules once instead of rebuilding them per vendor.
How can it catch a leak that pattern rules miss?
Because it already learned your data in place, it recognizes your sensitive files in context, not by regex. The same classification that tagged your data is what makes a leak visible at inference.
Will it slow my users down?
Checks run in milliseconds to low seconds, and you choose where deeper analysis applies. Ordinary work passes untouched, and people are stopped only on genuinely sensitive content.
Does Secuvy see or keep my data?
Classification runs inside your tenant. Nothing trains a global model, and you control logs, retention, and deletion. When you remove Secuvy, your data goes with it.
Confidently fuel every AI pipeline.