Exabeam secures $25 million in Series B funding

Exabeam closed a $25 million financing round to further accelerate the company’s growth and continue updating its user behavior analytics (UBA) solution.

Icon Ventures led the Series B investment round. Icon Ventures’ previous security investments include Palo Alto Networks and FireEye. Exabeam’s previous investors – Norwest Venture Partners, Aspect Ventures and Investor Shlomo Kramer – also participated in the series B funding, which brings total investments in the company to $35 million.

In the face of growing data breaches, malware and other attacks, cyber security continues to be one of the fastest growing technology markets. UBA is a rapidly expanding segment within the security industry and helps organizations detect, prioritize and respond to modern cyber attacks. UBA solutions use machine learning and other advanced techniques to distinguish normal and abnormal use of employee account credentials.

“We have grown tremendously in the past year, and this round will help us continue to invest in our growth. Our biggest focus will be implementing news processes to make Exabeam run as seamlessly as possible. We are focusing on hiring and introducing new positions to the company, everything from sales and finance to hardware development and facilities management. These new positions will be filled by talented orchestrators who will put these processes into place. All around, the end goal is to strengthen our go-to-market strategy,” Nir Polak, CEO of Exabeam, told Help Net Security.

After launching its flagship product early in 2015, Exabeam has enjoyed strong market success, beating both revenue and customer adoption goals. Exabeam appliances are currently monitoring more than 1 million employees and contractors for compromised activity.

In addition to continuing its domestic expansion and product development, the company will use the additional funds to drive growth through international expansion. Exabeam is also working with security operations centers to reduce the amount of time spent on tasks that can be automated by machine learning.




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