BOULDER, Colo.—December 4, 2013—LogRhythm, The Security Intelligence Company, today announced that it has been granted a patent by the United States Patent and Trademark Office. U.S. Patent 8,543,694 recognizes LogRhythm’s innovations in performing multiple analytical techniques to detect high-risk security events in real-time, and to do so at very high scale. The innovations contribute to LogRhythm’s advanced security intelligence solutions, which detect even the most sophisticated IT security threats.
The patent applies to technology that provides the architectural foundation of LogRhythm’s AI Engine product. AI Engine provides real-time machine based analytics. Core to the patent is AI Engine’s ability to identify sophisticated events via hybrid analytic techniques applied across log and machine data streamed at high velocity and volume. The patent specifically references LogRhythm’s architectural approach to applying various analytical techniques – including quantitative, correlative and behavioral analysis – that can be leveraged in the same logical instance and be cross-referenced against each other. The AI Engine’s unified analytics engine enables corroborative analyses of data from a myriad of sources to detect sophisticated events with high accuracy.
The patent highlights AI Engine’s Rule Block architecture. This unique architectural approach provides analytics extensibility while also serving to simplify the task of creating and modifying complex analytic rule sets. The patented rule block architecture results in highly efficient analysis of massive amounts of log and machine data via a real-time stream from a variety of sources, enabling the detection of concerning activities such as fraud and advanced persistent threats (APTs) as they occur.
The patent also recognizes LogRhythm’s unique time re-sequencing capability referred to as TrueTime™. This architectural capability ensures that data is always analyzed based on its actual time of occurrence, not when received by the analytics engine. This is critical in environments where log data generation or collection can experience periods of latency. LogRhythm’s TrueTime capabilities are critical to ensuring the integrity and accuracy of machine-based analytics.
This latest patent builds on a comprehensive patent granted to LogRhythm in 2010 for technology underlying its log and event management platform. U.S. Patent 7,653,633 covered LogRhythm’s ability to transform unstructured message-based data into structured data ideally suited for security analytics in support of detecting and responding to highly sophisticated cyber threats.
“This newly issued patent is another valuable distinction recognizing LogRhythm’s next generation, unified analytics architecture. This provides further validation of our leadership position around machine-based analytics in support of various applications, including advanced threat detection, fraud detection and general IT analytics,” said Chris Petersen, CTO/Co-Founder of LogRhythm. “LogRhythm’s security intelligence platform, powered by our now patented AI Engine, provides organizations the analytics and intelligence capabilities required to defend themselves from today’s sophisticated and rapidly growing threat landscape.”
LogRhythm is a world leader in NextGen SIEM, empowering organizations on six continents to successfully reduce risk by rapidly detecting, responding to, and neutralizing damaging cyberthreats. The LogRhythm NextGen SIEM Platform combines user and entity behavior analytics (UEBA); network detection and response (NDR); and security orchestration, automation, and response (SOAR) in a single end-to-end solution. LogRhythm’s Threat Lifecycle Management (TLM) framework serves as the foundation for the AI-enabled security operations center (SOC), helping customers measurably secure their cloud, physical, and virtual infrastructures for both IT and OT environments. Built for security professionals by security professionals, the LogRhythm NextGen SIEM Platform has won many accolades. For more information, visit logrhythm.com.