{"id":2120,"date":"2017-01-19T12:09:57","date_gmt":"2017-01-19T17:09:57","guid":{"rendered":"http:\/\/telesystem.ca\/fr\/non-classifiee\/intersets-impressive-300-growth-is-fueled-by-demand-for-security-analytics-driven-enterprise-ready-threat-detection\/"},"modified":"2017-01-30T10:30:50","modified_gmt":"2017-01-30T15:30:50","slug":"intersets-impressive-300-growth-is-fueled-by-demand-for-security-analytics-driven-enterprise-ready-threat-detection","status":"publish","type":"post","link":"https:\/\/telesystem.ca\/fr\/non-classifiee\/intersets-impressive-300-growth-is-fueled-by-demand-for-security-analytics-driven-enterprise-ready-threat-detection\/","title":{"rendered":"INTERSET\u2019S IMPRESSIVE 300% GROWTH IS FUELED BY DEMAND FOR SECURITY ANALYTICS-DRIVEN, ENTERPRISE-READY THREAT DETECTION"},"content":{"rendered":"
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Ottawa, ON, Jan. 18, 2017<\/strong> \u2013 Interset<\/a>, the premier provider of security analytics<\/a> for detecting insider and targeted outsider threats quickly and with certainty, today announced it has closed out 2016 with record 300% year-over-year bookings growth and again doubled its customer base. Interset enhanced its industry-first big data machine learning platform, specifically architected for enterprise-scale security operations by expanding to over 200 analytics models and tripling use cases coverage. Interset also won a host of awards, and continued operational rollouts at In-Q-Tel customers in the U.S. Intelligence Community.<\/p>\n

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\u201cInterset\u2019s hyper-growth comes as the market recognizes that security analytics with machine learning is the only way security operations centers can quickly and accurately pinpoint real threats,\u201d said Interset CEO Mark Smialowcz. With many enterprises experiencing tens of thousands of security alerts daily, more intelligent threat detection is essential to determine where to focus limited resources.\u00a0 \u201cYet Security Incident and Event Management (SIEM) tools, which have been central to many security team\u2019s playbooks, were never designed to handle the massive amounts of data required to capture and surface threats in real-time. Force fitting these tools only exacerbates the problem, with universal rules creating false positives and manual processes taxing the limited resources of most organizations,\u201d Smialowcz added.<\/p>\n

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The well-known limitations of SIEM only partially explain Interset\u2019s rapid adoption. Most User and Entity Behavioral Analytic<\/a> (UEBA) products remain based on rudimentary rules-based analytics lacking machine learning and built on traditional SQL architectures. As a result, their success is quickly hampered by the same challenges facing SIEM \u2014 false positives and an inability to scale. Interset, with the investment, partnership, and guidance of the U.S. Intelligence community, has benefited from extensive testing and deployment of its technology on end-to-end big data architectures. This has enabled private sector enterprises adopting the Interset approach to easily deploy the industry\u2019s only out-of-the box security analytics platform to leverage the powerful combination of machine learning and big data.<\/p>\n

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Interset customer wins in 2016 covered multiple use cases including a mix of fraud and targeted attack detection at several financial services companies. Additionally, Interset closed insider threat detection projects at a large US utility and a global hi-tech company, as well as several IP protection projects at software companies in the application, gaming and security markets. The wide breadth of use case project wins underscores the extensibility of Interset\u2019s security analytics engine.<\/p>\n

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Interset introduced extensive new product capabilities, expanding well beyond traditional UEBA with the addition of:<\/p>\n