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 →Classify petabytes no rule was written for.
Secuvy learns your research data itself, in place, and gets more accurate as the data changes, exactly when pattern rules break.
The cluster waits on data classification.
Every AI project on research data stalls at one question: what is in the files, and who can prove it?
“Two thirds of organizations report having a formal tagging system in place. Only half describe their approach as comprehensive or automated.”
Classifying files by hand cannot reach a petabyte. Pattern rules only catch data that repeats; simulation outputs, instrument data, and files from collaborators almost never do.
At petabyte scale, classification breaks first—the GPUs were never the bottleneck.
An engine that learns the data itself.
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 proposes 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 →What your teams get back.
Stewards clear queues, not file shares.
Your data stewards confirm Secuvy's classifications instead of searching project shares by hand, and the queue shrinks as the engine learns your data.
Why the engine learns →Lineage a sponsor can audit.
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 →Real-world use cases
Simulation and modeling outputs
Simulation runs leave large outputs no one has classified. Secuvy discovers and classifies them in place, so teams know what each result holds before it is shared.
Instrument and experimental data
Telescopes, sequencers, and beamlines write data faster than anyone can review it. Secuvy tags it as it lands, so researchers find what they need.
Export-controlled and CUI research
Export-controlled results hide in CAD files, notebooks, and results directories. Secuvy finds them by content and tags them, so stewards can check access.
Collaborator and sponsor data
Partners and sponsors share data under different terms. Secuvy tags each source, and a data bill of materials shows a sponsor what fed each model.
Everything a lab has to say yes to.
- Read-only. It finds, classifies, and tags; your team decides, and files never move or change.
- Air-gapped, VPC, or hybrid. Fully disconnected deployments are supported, and what it learns stays inside.
- Classifies export-controlled, CUI, and classified content where it lives.
- Aligns with NIST 800-171 and CMMC. SOC 2 Type II certified, IAPP member.
- Proven government channels: Carahsoft, SHI, ePlus, WWT.
- Your experts' judgment on every file, without their hours.
See it learn your data.
Thirty minutes on your own environment. Bring your export-control officer.
Request a demo →Does it run air-gapped?
Yes. It runs fully disconnected, and what the engine learns from your data never leaves the environment it learned in.
Can it distinguish collaborator data from export-controlled research?
Yes. Secuvy classifies files from content and context, including how they relate to the surrounding project data. It tags which data fits a pipeline, while your research and export-control teams make the final decision.
Our data looks like nothing a rule expects. Can it classify it?
Yes, that is the design. It learns meaning from context instead of matching patterns, so unfamiliar formats are exactly what it handles.
Can we show a sponsor what fed a model?
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.