Protect Privacy. Preserve Data Utility.

Enable the privacy-protecting use of multimodal healthcare data for research, AI, and collaboration through automatic, at-source PHI detection and de-identification.

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Unlock the Value of Your Data

to enable

AI

Research

Clinical Trials

Data Sharing

Real-World Evidence

Healthcare Data Has Become Multimodal. Privacy Protection Must Become Multimodal.

PHI Detector

Know what you have.

Map and inventory your data
Audit existing repositories
Verify inbound datasets
Discover hidden PHI
Inventory sensitive data

Your
Multimodal
Healthcare
Data

Protected. Private.
Powerful.

PHI Sanitizer

Share only what you should.

Prepare data for access, sharing and use
Protect sensitive information and patient privacy
Preserve data utility
Enable AI initiatives and research collaboration

The Responsible Data Journey

1

Know it.

Map and inventory what personal and sensitive data you have.

2

Audit it.

Document and assess where PHI and sensitive data reside and how it is used.

3

De-identify it.

Automatically remove PHI while preserving data utility.

4

Use it.

Confidently use data for AI, research, collaboration, and better outcomes.

Why Glendor

Built for Multimodal Healthcare Data

Specialized detection and de-identification across images, documents, audio, video, and more.

Automatic at Source

Runs entirely inside your environment behind your firewall. No data leaves your control.

Preserve Data Utility

PHI is removed while the data remains accurate, comprehensive, and ready for research and AI.

Designed for Existing Workflows

Deploy on-prem or in private cloud and integrate with your tools and data processes.

Deployment Scenarios

Use Case 1: Data Leaving Customer's Network

Use Case 2: Data Entering Customer's Network

Built for the Ways You Work

Verify Before Sharing

Identify PHI before files or datasets leave your organization.

Prepare Data for Research & AI

Remove PHI while preserving data utility for analysis and model training.

Audit Your Data

Map and inventory what personal and sensitive data you have, how it is used, and with whom it is shared.

Strengthen Privacy Governance

Extend your privacy, DLP, and DSPM programs to multimodal healthcare data.

Latest News and Publications by the Team

Glendor is 2025 Digital Health Hub Foundation Digital Health Awards Quarterfinalist

Glendor, Inc is proud to announce we were selected from 1800 companies as a Quarterfinalist for The 2025 Digital Health Hub Foundation Digital Health Awards in the Mental and Behavioral Health Track – Rising Star Category! The Digital Health Hub Foundation Awards honor outstanding health technologies and innovations dramatically transforming healthcare

BioHive HealthTech Hub presents From Bias to Balance: Safeguarding PHI for AI Research

Discover how safeguarding PHI is crucial for advancing equitable AI innovation in healthcare. This session will explore techniques to de-identify medical data, ensuring privacy while enabling robust, unbiased AI models.https://lu.ma/2yntofp2

EMERGE INNOVATION EXPERIENCE COMPETITION at HIMSS2025

🎉 Glendor is honored to be part of the Winner’s Circle in the Emerge Innovation Experience Contest at #HIMSS25. Category – Payer – Improving data & analytics capabilities and reach

Industry Perceptions Survey on AI Adoption and Return on Investment

Mitchell Goldburgh, Michael LaChance, Julia Komissarchik, Julia Patriarche, Joe Chapa, Oliver Chen, Priya Deshpande, Matthew Geeslin, Nina Kottler, Jennifer Sommer, Marcus Ayers & Vedrana Vujic 2023 Industry Perceptions Survey on AI Adoption and Return on Investment. Journal of Digital Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01147-1

Challenges of Sensitive Images: A HIMSS-SIIM Enterprise Imaging Community Whitepaper

Alexander J. Towbin, Delaney D. Ding, Moneif Eid, Heather Kimball, Julia Komissarchik, John Memarian & Seetharam C. Chadalavada Special Challenges of Sensitive Images: A HIMSS-SIIM Enterprise Imaging Community Whitepaper, Journal of Digital Imaging. Inform. med. 37, 915–921 (2024). https://doi.org/10.1007/s10278-024-00980-8