PHI found in free text.
It reads meaning, so PHI gets caught in notes, reports, and scanned documents, not just in formatted fields where patterns match.
Inside discovery and classification →Catch the PHI no pattern matches.
Secuvy learns your clinical and research data itself, in place, and finds the PHI hiding in free text where pattern rules go blind.
Clinical AI waits on data readiness.
Clinical AI depends on notes, reports, research systems, and scanned documents that are siloed, inconsistent, and constrained by compliance, privacy, and broader regulatory requirements. Before a model can scale, teams still have to integrate the sources, define a data strategy, improve quality, and control how the data may be used.
“The top obstacles when it comes to preparing data for AI include:
- 56%
- Siloed data/difficulty integrating data sources
- 44%
- Lack of a clear data strategy
- 41%
- Data quality/bias issues
- 34%
- Regulatory constraints on data use”
Secuvy works on all four.
It learns your data, not a rule set.
Secuvy learns what your data is from content and context, in place, without a large rule set or a labeling team to staff.
It proposes a classification for every file. Your privacy team confirms, and every confirmation makes the next classification sharper.
New data is what trains it, not what breaks it.
How non-pattern classification works →What changes for your teams.
Reviewers clear queues, not charts.
Your reviewers confirm Secuvy's classifications instead of reading records one by one, and the queue shrinks as the engine learns.
Why the engine learns →Proof an auditor can read.
A Data Bill of Materials records what fed each model, what was held out, and proves PHI stayed out of training and inference.
The DBOM in depth →Real-world use cases
Hospitals and health systems
Patient data sits across EHRs, imaging, lab, and research systems. Secuvy finds and classifies PHI in place and keeps one current inventory across all of them.
Clinical AI and RAG pipelines
Secuvy tags each source so your team can check what each AI tool may use. A data bill of materials records what fed every model.
Drug discovery and research
Molecules, formulations, and protocols rarely match a pattern. Secuvy classifies them by meaning, so researchers see what can feed AI and what stays out.
Clinical development
Trial data arrives from sites, CROs, and partners in many formats. Secuvy discovers and tags it as it lands, so trial analytics start from checked data.
Everything compliance has to say yes to.
- Read-only. It finds, classifies, and tags; your team decides, and records never move or change.
- Air-gapped, VPC, or hybrid. Your data and everything learned from it stay inside your environment.
- Classifies PHI, PII, and IP in place, across clinical, claims, and research sources.
- SOC 2 Type II certified, IAPP member.
- Proven channels: Carahsoft, SHI, ePlus, WWT.
- Your privacy standard on every record, without the months.
See it find PHI in your own data.
Thirty minutes on your own environment. Bring your privacy officer.
Request a demo →Does it move or copy patient records?
No. It finds, classifies, and tags in place. Records stay in the systems they live in, and your team makes every decision.
Can it catch PHI in free text, not just fields?
Yes. It learns meaning from context instead of matching patterns, so PHI in notes, reports, and scanned documents gets classified too.
Can the same clinical data be governed differently for research and patient-care AI?
Yes. Secuvy classifies the data once, in place, then evaluates what is appropriate for each AI pipeline under its policy. The DBOM records what each pipeline used, what was held out, and the status of those decisions.
Can we prove what fed a model?
Yes. A Data Bill of Materials records each model's inputs, its exclusions, and proof that PHI stayed out.
Confidently fuel every AI pipeline.