LogRhythm Launches Industry’s First Multi-Dimensional Behavioral Analytics for Big Data; Bolsters Advanced Threat and Real-Time Breach Detection
Adds New Layer to its Award-Winning SIEM Big Data Security Analytics Platform
BOULDER, Colo.—October 16, 2012—LogRhythm, a leader in cyber threat defense, detection and response, today announced the enhancement of its award-winning SIEM Big Data security analytics platform with the industry’s first Multi-Dimensional Behavioral Analytics. Leveraging innovative and patent-pending behavioral whitelisting as well as advanced statistical and heuristic behavioral analysis, the [e]nhanced SIEM solution empowers organizations of all sizes to detect breaches and the most sophisticated cyber threats of today, faster and with greater accuracy than ever before.
According to the 2011 Verizon Data Breach Report, 86 percent of breached organizations failed to detect that their networks were hacked. The tide has turned amongst IT security professionals who now believe it’s no longer a matter of if they’ll be breached but when. In reality, there is a very good chance they have already been breached and simply don’t know it.
Today, with its latest innovation to the award-winning SIEM platform, LogRhythm is enabling organizations to baseline normal, day-to-day activity across multiple dimensions of the enterprise. The system then analyzes against that baseline the massive volume of log, flow and machine data generated every second to discover anomalies in real time. By doing so, LogRhythm is enabling IT administrators and security professionals alike to detect and respond to even the most sophisticated threats and breaches.
“Today’s cyber threats are more advanced and, in many cases, more stealthy than ever before. Organizations need to understand what ‘normal’ behavior is across multiple dimensions of their electronic enterprise so they can detect abnormal activity indicative of a threat or breach,” said Chris Petersen, CTO/CoFounder, LogRhythm. “Adding the multi-dimensional behavioral analytics layer to our SIEM2.0 platform delivers on that need and, once again, sets a new standard for advanced threat and breach detection.
Some first generation SIEMs provide behavioral analysis, but it is most often against a silo of data (e.g., Netflow logs, authentication logs), rather than the universe of enterprise activity data (i.e., logs, flow and machine data). For many organizations, defining normal behavior is a manual process. But manually determining what is normal is extremely difficult if not impossible for most organizations. In either scenario, IT and security personnel remain blind to much of the behavior of today’s advanced hackers because the evidence of their activities are buried amidst massive volumes of false positive security events, or they’re miscategorized altogether as benign or ‘normal’ activities.
Further increasing the crippling volume of false positive events in first generation SIEMs is the inherent lack of data corroboration in these tools. Traditional uses of behavioral and correlative analysis are handled via separate technologies that don’t integrate. LogRhythm’s multidimensional approach integrates advanced correlative, statistical, behavioral and pattern recognition techniques to corroborate the identification of threats and breaches in real-time with unprecedented accuracy.
“Early generation SIEM techniques for correlation and behavioral profiling face a number of challenges in helping midsized-to-large enterprise organizations detect and respond to today’s sophisticated threats,” said Scott Crawford, managing research director, Enterprise Management Associates. “By adding multi-dimensional behavioral analysis to its SIEM platform, LogRhythm introduces a practical, highly intuitive and easy-to-use approach to building a deep level of analysis of log, flow and machine data., placing richer security analytics within reach for enterprises of all sizes.”