RevealSecurity
David Movshovitz has a diverse and extensive work experience spanning over several roles and companies. David is currently the CTO & Co-founder at RevealSecurity, where they are involved in developing a unique clustering engine that detects malicious insiders and imposters in enterprise applications. Prior to this, they worked as an Information Security Senior Consultant and Lecturer in Independent Consulting. David also served as the VP Technology at salesforce.com, focusing on cloud security challenges, following the acquisition of Navajo Systems, where they were the CTO and Co-Founder. David has also held positions such as VP Security Technologies at F5 Networks, CTO & VP R&D at Magnifire, VP Product Development at Taldor group, and CTO & Founder at NetAccess. Their work experience extends to their time at IDF, where they served as a Team Leader. Overall, David brings a wealth of expertise in information security, technology, and product development to their current role at RevealSecurity.
David Movshovitz completed their Ph.D. in Physics from Bar-Ilan University from 1982 to 1991. There is no additional information available regarding their primary education at Moria in Tel-Aviv. David has also obtained a certification in Salesforce Technology Basics from Salesforce Trailhead, although the specific date of attainment is unknown.
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RevealSecurity
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RevealSecurity detects malicious insiders and imposters by monitoring user journeys in enterprise applications. Powered by our unique clustering engine, RevealSecurity is ubiquitous, thereby detecting threats which originate from SaaS applications, cloud applications and custom-built applications. It protects enterprise organizations against casesin which either an authenticated user is taking advantage of permissions to abuse or misuse an application, or when an impersonator successfully bypasses authentication mechanisms and poses as a legitimate user. Tracking user journeys within applications does not rely on solution-specific rules, and is instead based on an advanced unsupervised machine learning algorithm to detect abnormal journeys which reflect abuse, misuse and malicious activities.