Prelert Unveils Behavioral Analytics for Elastic Stack
February 18, 2016
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Prelert announced Behavioral Analytics for the Elastic Stack, a new software product that enables Elasticsearch users to automate the analysis of large data sets, identify anomalous activities and link specific behaviors together within the Elastic Stack.

The technology integration between Prelert and Elastic enables users to quickly gain behavioral insights from data stored in Elasticsearch and visualize it within a custom Kibana application.

“There’s a whole new world of possibilities for the way users extract value from their data that’s being driven by combining the power of analytics with the speed of search,” said Tanya Bragin, Director of Product Management at Elastic. “Prelert’s automated behavioral analytics are a powerful addition to the Elastic Stack and further demonstrates how developers can build an application using Kibana’s UI framework.”

Behavioral Analytics for the Elastic Stack is powered by Prelert’s machine learning algorithms and capabilities, which have been proven in Prelert’s other analytics products including its widely deployed Anomaly Detective solution. Using automated analysis of each customer’s data, Prelert’s analytics create highly-accurate, always up-to-date statistical baselines of normal behaviors. From these baselines, Prelert’s software detects, scores and links unusual activity that could indicate IT operations problems, IT security incidents, or business interruptions. Since the automated analysis flags real issues as they’re happening, it eliminates the need for traditional data monitoring rules and thresholds that return false positives if set too strictly, miss activity if set too loosely, and become outdated over time. Prelert’s analytics include new statistical influencer tracking, which provides critical contextual data for each detected anomaly so the root cause of issues can be identified and resolved quickly.

“Organizations are increasingly in need of better tools to gain greater insight from their data,” said David Monahan, Research Director at Enterprise Management Associates (EMA). “This partnership enhances Elastic’s already powerful search and discovery capabilities with Prelert’s automated behavioral analytics to help organizations reduce the time to detect both threats and performance issues before they cause larger problems saving them valuable time and money.”

Some of the key benefits of Prelert’s Behavioral Analytics for the Elastic Stack include:

- Early Problem Detection – identifies IT operations problems, IT security incidents and business interruptions before users report them.

- Root Cause Discovery – uses statistical influencer tracking to provide critical context for investigating root causes of cyber attacks, IT operations issues, or business operations issues.

- False Positive Reduction – because of its automated baselining, accurate anomaly detection and the additional context provided by its statistical influencer tracking, false positive alerts are drastically reduced when compared to traditional monitoring and alerting approaches.

“Security, IT operations and business operations teams that have not adopted automated analytics such as Prelert’s continue to suffer from ‘alert fatigue,’ dealing with thousands of alerts per day. Valuable time is wasted weeding out false positives, making it nearly impossible to identify the most critical security, performance, or business-related issues before they cause a larger financial impact,” said Mark Jaffe, CEO of Prelert. “As we encounter more and more organizations adopting Elasticsearch for their machine data, Prelert’s automated analytics are a powerful extension to Elastic’s impressive search, discovery and analysis capabilities.”

Behavioral Analytics for the Elastic Stack is expected to be available for download in March 2016.

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