Innovation Update: Fortscale User and Entity Behavior Analytics for Healthcare Organizations

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Fortscale Now Preventing Data Theft of Electronic Health Records (EHR) and Personally Identifiable Information (PII)

Investigation of Access to Electronic Health Records (EHR) and Personally Identifiable Information (PII)

With machine learning-based algorithms, Fortscale’s UEBA is able to accurately and efficiently detect the non-typical and unknown behavior of users and entities in the healthcare organization that pose a security threat.

Fortscale Security Ltd., the innovative pioneer in machine learning-based user behavior analytics for security (UEBA), today introduced new ways for healthcare security professionals to detect threats and stop data breaches of electronic health records (EHR) and personally identifiable information (PII).

Breaches of EHR and PII are a big problem. U.S. Department of Health and Human Services, which manages and reports on HIPAA compliance reports that:

  • 340% increase in cyber attack volume within Healthcare organizations, compared to other industries.
  • 81% of healthcare organizations have been compromised by cyber attacks within the past 2 years.
  • EHR Systems are the #1 target for malicious insiders and attackers with stolen credentials.

“EHR systems are very chaotic and dynamic. Companies lack effective means for tracking and auditing malicious internal user access to HER systems,” explained Idan Tendler, CEO and Co-Founder of Fortscale. “Existing systems such as SIEM and DLP, which are rules-based, are ineffective as they only address known threats.”

Steve Katz, Executive Advisor Security & Privacy at Deloitte and former Executive Advisor to Head of Technology Risk at Kaiser Permanente adds, “With machine learning-based algorithms, Fortscale’s UEBA is able to accurately and efficiently detect the non-typical and unknown behavior of users and entities in the healthcare organization that pose a security threat.”

For example, Fortscale’s UEBA applies unsupervised machine learning algorithms to detect anomalous access to restricted or sensitive data. This access would not be detected by SIEMs or rules-based systems, leaving healthcare organization open to unauthorized access and breaches.

Fortscale also has new user experience and interface that makes it easier for healthcare security professionals to identify, investigate and stop insider threats.

For more information about Fortscale UEBA, you can download the data sheet.

Fortscale’s innovation continues to garner recognition for fundamental advancements in UEBA technology. Fortscale was named “2016 Leader in Threat Detection” by Cyber Defense Magazine and a “Cool Vendor” in the “Gartner Cool Vendors in Identity and Access Management and Fraud, 2016” report by Gartner, Inc.

About Fortscale
Fortscale ends insider threats with a totally new generation of rule-free, autonomous behavior analytics based on machine learning. With no rules to set up, Fortscale’s user behavior analytics engine starts getting smarter the second you turn it on. Fortscale models your users and systems autonomously, on-the-fly. Fortscale spots anomalous behavior quickly, accurately and doesn’t need constant “babysitting.” It might sound like magic, but it’s mostly just really good math – and only Fortscale has it. Backed by Intel Capital and Blumberg Capital, Fortscale ends insider threats, lowers analyst stress-levels and makes your whole security operation work a whole lot better. For more information, visit fortscale.com.

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Lynn Strand
@fortscale
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