Data Privacy & Security: We have reached the point of insanity!

Data Privacy & Security We have reached the point of insanity! Blog

Written by Mary Beth Chalk | Originally Posted on LinkedIn

In a November 6th article in MIT Technology Review entitled, “It’s shockingly easy to buy sensitive data about US military personnel” the author cited that “for as little as $0.12 per record, data brokers in the US are selling sensitive private data about active-duty military members and veterans, including their names, home addresses, geolocation, net worth, . . . religion, and information about their children and health conditions.”

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4th Quarter Buzz at BeeKeeperAI 🐝

BKAI Blog Image 4th Q Buzz

As we bid farewell to 2023, it's time to look back at a year buzzing with achievements and milestones! We've gathered some sweet highlights from our hive in the last quarter to keep you in the loop. 


We're excited to share with you the buzz others have been spreading about us.

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PRESS RELEASE: BeeKeeperAI Expands Privacy-Enhancing Innovation with New Patent, New Product, and New Advantages to Accelerate Responsible AI in Healthcare

BKAI Blog Images

SAN FRANCISCO, Calif. — December 12, 2023 — BeeKeeperAI, Inc., a pioneer in privacy-enhancing, multi-party collaboration software, today announced the next step in its innovation strategy for EscrowAI™ with automated, privacy-enhancing workflows to advance the responsible development and deployment of AI for healthcare. 

The company is expanding its novel privacy-enhancing capabilities to address confidential federation and confidential training in ways that continue to accelerate the approval …

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PRESS RELEASE: BeeKeeperAI Raises $12.1 Million Series A to Accelerate AI Development on Privacy Protected Healthcare Data

BKAI PR Series A

SAN FRANCISCO, Calif. - June 27, 2023 – BeeKeeperAI, Inc., a pioneer in zero-trust, collaboration software for the development and deployment of artificial intelligence (AI), today announced that it has closed $12.1 million in Series A financing. The round was led by Santé Ventures, with participation from the Icahn School of Medicine at Mount Sinai, AIX Ventures, Continuum Health Ventures, TA Group Holdings, and UCSF. The new funding will be used to expand the features of its EscrowAI platform…

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PRESS RELEASE: BeeKeeperAI Announces Commercial Release of its Patented, Zero-Trust Collaboration Platform to Accelerate Healthcare AI Development on Protected Information

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Available now in the Microsoft Azure Marketplace, the EscrowAI™ privacy-preserving collaboration platform delivers a zero-trust environment protecting both algorithm intellectual property and real-world data 

SAN FRANCISCO, Calif. — May 17, 2023 — BeeKeeperAI, Inc., a pioneer in zero-trust, real-world data collaboration software, today announced the general availability of EscrowAI, a patent-protected zero-trust collaboration platform. EscrowAI leverages Azure confidential computing to resolve…

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Generative AI Misses the Mark in Healthcare – What It Needs to Succeed

Generative AI HC Blog

By Michael S. Blum, MD, CEO of BeeKeeperAI™

Key Points:

  • ChatGPT is impacting industry and society in unprecedented ways
  • The opportunity for GPTs to improve healthcare delivery is enormous, but the technology is immature and not sufficiently reliable for general healthcare use
  • The AI models need additional training on real-world healthcare data to perform adequately in healthcare, but accessing that data is challenging due to patient privacy concerns
  • New confidential computing technologies…

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PRESS RELEASE: BeeKeeperAI Applies Sightless Computing Technology to Pediatric Rare Disease Project

Powered by the BeeKeeperAITM sightless computing platform, Novartis scientists used health data from a UCSF patient cohort to refine a disease detection solution for a rare childhood condition

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Solving the Data Access Challenge in Healthcare AI Using Confidential Computing

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Solving the Data Access Challenge in Healthcare AI Using Confidential Computing

Many companies claim to provide secure environments for developing artificial intelligence-based algorithms and models for healthcare use. But the pace of development has remained painfully slow. A major challenge is that most algorithm developers only have access to limited, often narrow datasets to validate and train their models. As a result, the models are not generalizable across different clinical settings.


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