Stolen credentials. Rogue users. Careless employees. They all amount to one thing: insider threats. When legitimate accounts are abused or compromised, their behavior changes. Traditional security solutions don’t see it. Fortscale does.
Many UEBA solutions are primarily rules-engines with machine learning capabilities layered on top, limiting the threats they can identify to those that they can write a rule for, hindering scalability, and contributing to the noise of a SOC. Fortscale is not just another rules-engine. Fortscale has been designed from the ground up as a machine learning system that uses advanced computing and mathematics to detect abnormal account behavior indicative of credential compromise or abuse.
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.
Results: Faster time to remediation and enhanced ROI from the SOC team.
“More accurate alerts for prioritized investigation and reliable detection of user-based threats.”
“Fortscale transforms the vast amounts of data into a more visually accessible and informative format. That means our analysts can investigate much faster.”