Every sector has the same structural problem. The most sensitive material sits in documents that have to move to be useful. What changes is what’s inside those documents, who is on the other end, and which regulator asks about it.
Confidencial protects the document itself. Sensitive spans get encrypted, tokenized, or redacted; the rest stays readable. Access travels with the file and can be revoked after it lands.
INDUSTRIES WE SERVE
Privilege is a legal standard. Most firms enforce it with folder permissions.
Matter files and privileged communications shared with co-counsel, experts, and clients
Document management systems where access is broad by default
Legal AI tools that need matter context without ingesting client confidences
Data protection for legal →
Examiners do not accept "we have a policy."
Customer records and account documentation moving between systems and service providers
M&A, and third-party and vendor exchange where control ends at send
Audit evidence that has to be produced on demand, not reconstructed
Data protection for financial services →
Your most valuable asset is a document you have to hand to someone else.
Regulatory submissions moving to agencies and partners
Clinical trial data shared with CROs and investigator sites
Formulation and process IP that has to travel through the supply chain
Data protection for pharma →
Claims and underwriting run on unstructured documents nobody has inventoried.
Claim files and underwriting submissions arriving from brokers and third parties
Policyholder information buried inside attachments, not fields
Adjuster and vendor workflows that require partial access, not full access
Data protection for insurance →
Deal data leaks before the wire clears. Then you have a different problem.
Diligence documents, CIMs, and deal books circulating outside the fund
Portfolio company data with inconsistent security maturity across the book
LP information handled by a small team with no security function
Data protection for private equity →
Source code, roadmaps, and unreleased features need protection that scales with velocity.
Source code, roadmaps, and architecture documentation shared with partners
Customer data handled under contractual security commitments
Security reviews and questionnaires that stall enterprise deals
Data protection for technology →
By problem, not by sector
M&A affects legal, finance, and pharma. Insider risk affects everyone. If your problem is clearer than your category, start here.
01
Protect IP from insider and third-party exposure.
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02
Secure document exchange while the deal is live.
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03
Reduce insider risk without blocking work.
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04
Control what leaves with vendors and partners.
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05
Protect submissions end to end.
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Govern sensitive data in AI pipelines.
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One platform underneath
Finds and classifies sensitive content.
Applies persistent protection at scale.
Handles external sharing, requests, and signature.
Protects data before it reaches AI pipelines.
Applies protection automatically as files move through connected systems.
Protects content in bulk as it moves into and between cloud repositories.
Gives teams and outside parties a shared place to work on protected files.
Every industry page above runs on the same selective protection engine.
Platform overview →Answers
01
No. Confidencial is one platform. Industry configuration changes what gets classified as sensitive, which policies apply, and which regulatory frameworks the reporting maps to. The underlying protection is identical.
02
The pages above reflect where we have the deepest customer footprint. The platform is industry agnostic. If your work depends on sensitive documents leaving your environment, the fit is the same.
03
DLP inspects content and decides whether to allow or block, and it fails open when it guesses wrong. Confidencial applies cryptographic protection to the sensitive spans themselves, so an unauthorized reader gets nothing even if the file gets out.
04
Yes. Selective protection means non-sensitive content stays readable while sensitive spans are encrypted, tokenized, or redacted. Models get the context they need without the material they should not have.
Start here
Run a data risk assessment against your own environment. No data leaves your tenant.