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Alert Logic’s Security-as-a-Service platform keeps data and infrastructure safe and compliant wherever it resides – including public and private clouds, hybrid environments or on-premises – through a set of fully managed products and services. The company maintains partnerships with the largest cloud and hosting service providers and offers its customers continuous protection down the application stack through a 24×7 Security Operations Centre that analyses, escalates and works with customers to remediate threats with actionable intelligence.
“Integrating Prelert’s anomaly detection engine into our big data platform creates a powerful combination of security analytics techniques, allowing us to identify unknown and advanced threats across petabytes of machine data we manage for our customers.” said Alert Logic’s Chief Strategy Officer, Misha Govshteyn. “Our objective has always been to help our customers respond to the most relevant security incidents before they impact their business. Working with Prelert allows us to leverage massive amounts of machine data we process every day to identify precursors to security breaches at the earliest possible moment and maintain our historically high degree of accuracy, even when advanced attackers employ sophisticated tactics to avoid detection.”
Prelert’s Anomaly Detective engine uses advanced analytics based on unsupervised machine learning to process and cross-correlate millions of data points in real-time, automatically learning normal behaviour patterns and identifying statistical outliers that may indicate successful breaches and data exfiltrations. In May 2014, Prelert opened its API giving enterprise application developers, technology vendors and cloud service providers such as Alert Logic the ability to utilise its machine learning engine in their products and environments.
“Security paradigms solely reliant on identifying already ‘known’ threats are proving inadequate when used against today’s advanced cybercriminals,” said Mark Jaffe, Prelert’s CEO. “As a result, leadership organisations are starting to aggregate data accumulated from security devices, web servers and network equipment, and then processing it with advanced machine learning analytics to identify suspicious activities that would otherwise go unnoticed.”
