Alert Logic Adds Machine Learning Analytics to Security as a Service

Alert Logic is adding machine learning analytics capabilities to its security-as-a-service offerings through an OEM partnership with Prelert, which calls itself “the anomaly detection company.” Through the partnership, Alert Logic will add Prelert’s machine learning functions to enhance its existing capabilities for detecting threats that are designed to bypass traditional, signature-based approaches.

Alert Logic has several security-as-a-service offerings available on public clouds, and the partnership with Prelert will mean the addition of new advanced analytics capabilities. 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. According to the company, the technology automatically learns normal behavior patterns and identifies statistical outliers that may indicate successful breaches and data exfiltrations.

“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 Misha Govshteyn, co-founder and chief strategy officer at Alert Logic, in a prepared statement. “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.”

The OEM partnership announcement follows four months after Prelert opened its API to provide cloud services providers and organizations with the ability to use its machine learning engine technology 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, CEO of Prelert, in a prepared statement. “As a result, leadership organizations 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.”