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 →MNPI has no pattern. Secuvy finds it.
Account numbers have a shape. A deal underway does not. Secuvy learns your data itself and classifies both, in place, across every source.
Every AI pilot faces a choice: long timelines or exposed risk.
Your models want the documents your firm runs on, and those documents are where MNPI and client data live.
“42% of banking executives cite insufficient data readiness as a cause of AI project failure.”
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.
It learns what sensitive means at your firm.
Secuvy classifies unsupervised, in place, across your sources, and builds its own understanding of your documents from content and context.
It proposes 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 →What changes for the firm.
Compliance confirms, not combs.
Your compliance team confirms Secuvy's classifications instead of sampling documents by hand, and the queue shrinks as the engine learns the firm.
Why the engine learns →Proof for model risk.
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 →Real-world use cases
Investment banking and deal teams
Deal rooms, pitch books, and draft filings carry MNPI with no fixed pattern. Secuvy classifies them by meaning in place, so they are tagged before any copilot connects.
Retail and commercial banking
Account, loan, and customer records spread across core systems, mailboxes, and shared drives. Secuvy finds and classifies client data where it lives, in one current inventory.
Wealth and asset management
Client positions, portfolios, and research notes look ordinary to a rule. Secuvy classifies them from content and context and tags what can feed analytics and AI.
Model risk and examinations
Examiners ask what fed each model. A data bill of materials records the inputs and what was held out, so your team can prove it.
Everything risk has to say yes to.
- Read-only. It finds, classifies, and tags; your firm decides, and documents never move or change.
- Air-gapped, VPC, or hybrid. The engine and everything it learns stay inside your perimeter.
- Classifies MNPI, PII, and IP in place, across documents, mailboxes, and shared drives.
- SOC 2 Type II certified, IAPP member.
- Proven channels: Carahsoft, SHI, ePlus, WWT.
- Your standard on every document, without the sampling.
See it read your documents.
Thirty minutes on your own environment. Bring your compliance officer.
Request a demo →Does it move or copy our documents?
No. It finds, classifies, and tags in place. Documents stay in the systems they live in, and your firm decides everything.
Can it find MNPI without a rule for it?
Yes. MNPI rarely matches a pattern, so Secuvy learns meaning from context instead. You define what matters, and it finds it in the data.
Can the same document be tagged for one AI use case and held out of another?
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.
Can we show an examiner what fed a model?
Yes. A Data Bill of Materials records each model's inputs, its exclusions, and when each entry last changed.
Confidently fuel every AI pipeline.