Controlled content, in any form.
CUI and export-controlled content get caught by meaning, whether it sits in a CAD file, a notebook, or a results directory, not just where a pattern matches.
Inside discovery and classification →HPC & RESEARCH
Secuvy learns your research data itself, in place, and gets more accurate as the data changes, exactly when pattern rules break.
THE CEILING
Every AI project on research data stalls at one question: what is in the files, and who can prove it?
58% of IT leaders call classifying data for AI their hardest technical problem.
Classifying files by hand cannot reach a petabyte, and pattern rules only catch data that repeats. Simulation outputs, instrument data, and files from collaborators almost never repeat.
The GPUs were never the bottleneck. Classifying the data was.
THE SHIFT
Secuvy classifies your data unsupervised, in place, across your sources. It builds its own model of your data from content and context, not from rule sets.
It recommends a classification. Your stewards confirm. Every confirmation teaches the engine, and every new run gives it more to learn from.
What breaks a rule set trains Secuvy.
Inside non-pattern classification →FOR RESEARCH TEAMS
CUI and export-controlled content get caught by meaning, whether it sits in a CAD file, a notebook, or a results directory, not just where a pattern matches.
Inside discovery and classification →Your data stewards confirm Secuvy's recommendations instead of searching project shares by hand, and the queue shrinks as the engine learns your data.
Why the engine learns →Every pipeline carries a Data Bill of Materials: what fed it, what was held out, and when the record last changed.
The DBOM in depth →MEETS YOUR BAR
Thirty minutes on your own environment. Bring your export-control officer.
Book a demo →PLAIN ANSWERS
Yes. It runs fully disconnected, and what the engine learns from your data never leaves the environment it learned in.
Yes. Secuvy classifies files from content and context, including how they relate to the surrounding project data. It recommends which data is appropriate for a pipeline, while your research and export-control teams make the final decision.
Yes, that is the design. It learns meaning from context instead of matching patterns, so unfamiliar formats are exactly what it handles.
Yes. Each pipeline carries a Data Bill of Materials listing what fed it, what was held out, and when it last changed.
Confidently fuel every AI pipeline.