MNPI found, wherever it sits.
It reads meaning, so MNPI, client data, and IP get classified in deal rooms, mailboxes, and shared drives, not just in databases.
Inside discovery and classification →FINANCIAL SERVICES
Account numbers have a shape. A deal underway does not. Secuvy learns your data itself and classifies both, in place, across every source.
THE CEILING
Your models want the documents your firm runs on, and those documents are where MNPI and client data live.
58% of IT leaders call classifying data for AI their hardest technical problem.
Pattern rules catch account numbers and card numbers because they have a shape. A deal in progress, a client's position, a draft filing: no shape, no match.
What moves markets never looks like a pattern.
THE SHIFT
Secuvy classifies unsupervised, in place, across your sources, and builds its own understanding of your documents from content and context.
It recommends a classification. Your compliance team confirms, and every confirmation makes the next call sharper.
New deals, new documents, no new rules to write.
How Secuvy classifies without patterns →FOR RISK AND DATA TEAMS
It reads meaning, so MNPI, client data, and IP get classified in deal rooms, mailboxes, and shared drives, not just in databases.
Inside discovery and classification →Your compliance team confirms Secuvy's recommendations instead of sampling documents by hand, and the queue shrinks as the engine learns the firm.
Why the engine learns →A Data Bill of Materials records what fed each model and what was held out, ready for auditors, examiners, and model risk.
The DBOM in depth →MEETS YOUR BAR
Thirty minutes on your own environment. Bring your compliance officer.
Book a demo →PLAIN ANSWERS
No. It finds, classifies, and recommends in place. Documents stay in the systems they live in, and your firm decides everything.
Yes. MNPI rarely matches a pattern, so Secuvy learns meaning from context instead. You define what matters, and it finds it in the data.
Yes. Secuvy keeps the document’s sensitivity and business context attached, then evaluates it against the policy for each pipeline. The DBOM records the decision separately for every use case, including what was used and what was held out.
Yes. A Data Bill of Materials records each model's inputs, its exclusions, and when each entry last changed.
Confidently fuel every AI pipeline.