Healthcare & Life Sciences
AI is already touching protected health information in your clinical and administrative workflows — often without anyone tracking it. We help you find it, govern it, and build toward TRE accreditation and de-identification.
The risk
Clinicians and administrative staff increasingly rely on copilots, transcription tools, and AI-assisted documentation. Vendor AI features are also being embedded directly into EHR-adjacent SaaS tools. Without visibility, PHI can flow into AI systems that were never evaluated for HIPAA implications.
- Clinical documentation & transcription copilots
- Administrative and back-office AI assistants
- Vendor AI embedded in EHR-adjacent SaaS
- Research and analytics environments handling patient-level data
Regulatory context
HIPAA implications of shadow AI, AI use in clinical and administrative workflows, vendor AI embedded in EHR-adjacent SaaS, and accreditation for Trusted Research Environments all raise questions your compliance team needs answered — with evidence, not assumptions.
What we assess
We help you build evidence and controls aligned to HIPAA and TRE accreditation frameworks. We never claim to "make you compliant" — compliance is a program you own; we help you build the evidence and controls behind it.
- Where PHI enters copilots, transcription tools, and chat assistants
- Vendor AI risk in your EHR-adjacent SaaS stack
- De-identification and disclosure-control gaps
- Readiness evidence for TRE / Five Safes accreditation
- Access governance for clinical and administrative AI users
- Board- and auditor-ready reporting
TRE accreditation, backed by evidence
Trusted Research Environments (TREs) — secure enclaves where researchers analyze sensitive health and life-sciences data without it ever leaving a controlled setting — are increasingly expected to demonstrate accreditation against frameworks like the Five Safes and ISO 27001-aligned controls. If you operate or are building a TRE, we help you assemble the evidence accreditation reviewers expect: environment security reviews, governance documentation, disclosure-control processes, and researcher access attestations. This is advisory and assessment work available today through the 30-Day Assessment and Control Tower — not a future product.
Safe People
Only trained, authorized researchers get access, with clear accountability.
Safe Projects
Every use of data is reviewed and approved before it starts.
Safe Settings
Access happens in a controlled environment, not on local machines.
Safe Data
Data is de-identified or reduced to what the project actually needs.
Safe Outputs
Anything leaving the environment passes disclosure-control review.
De-identification, built for AI
We're building a de-identification solution purpose-built for healthcare AI workflows — so PHI can be stripped or masked before it ever reaches a copilot, chatbot, or model, instead of relying on a vendor's promise after the fact. It's designed to support the same disclosure-control standard TRE outputs are held to. Not available yet; the 30-Day Assessment is how we find where this is needed most in your environment today.
- HIPAA Safe Harbor–style removal of the 18 direct identifiers
- Expert Determination–style statistical disclosure review
- Pseudonymization and tokenization for linked research data
- Disclosure-control checks before outputs leave a research environment
See how this fits our broader roadmap on the Products page.
PHI doesn't need to leave your network to become exposed — it just needs a copilot with the wrong permissions.
Know what AI is already touching PHI.
Start with the 30-Day AI Security Readiness Assessment, or the TRE Security Assessment if you run a research environment pursuing accreditation.