Organizations with established insider threat detection programs often deploy security solutions that are optimized to perform network log monitoring and aggregation, which makes sense given that these systems excel at
Enterprise security teams responsible for preventing insider threats have mixed feelings about acquiring and analyzing data on individuals. Sure, that data contains a wealth of knowledge about the potential for
The best insider threat mitigation programs often use combinations of analytic techniques to assess and prioritize workforce risk, according to a recent report by the Intelligence and National Security Alliance
At Haystax Technology, we are proponents and early adopters of principled approaches to machine learning (ML) and artificial intelligence (AI) for cybersecurity. We use the term ‘principled AI’ to describe what
Haystax Technology has been named in Gartner Inc.’s latest Market Guide for User and Entity Behavior Analytics (UEBA) as a representative vendor in the specialized use-case category of employee monitoring
For purposes of scientific discovery, the field of insider-threat detection often lacks sufficient amounts of time-series training data. Moreover, the limited data that are available are quite noisy. For instance, Greitzer
Haystax’s security analytics platform applies artificial intelligence techniques to reason like a team of analysts and prioritize risks in real time at scale for more efficient protection of critical assets.