The intersection of big data analytics and IT security brings forth a new era in cybercrime prevention. Today's sophisticated attacks can only be detected by applying equally sophisticated data science to huge volumes of data collected by IT security teams. The challenge is that there are very few individuals who possess the requisite blend of domain expertise in security and data science.
To address this gap, Anomaly Detective 3.0 uses machine learning predictive analytics to automatically learn the normal behavior patterns of populations of individual users, devices and resources. Sophisticated population analytics cross-correlate multiple data sources, in real time, to identify the anomalous behaviors that are the indicators of advanced threats.
"Today's cyber attacks have become increasingly difficult to detect with current technologies, but these attacks leave fingerprints in vast amounts of data available to IT security teams, creating a lot of interest in Big Data analytics for Security," said Mark Jaffe, CEO at Prelert. "Uncovering these attacks requires very sophisticated data science that is beyond the abilities of most security experts. The good news is Prelert has packaged that data science into a downloadable application that can be leveraged easily to identify these advanced threats in real time."
Responsys, a marketing cloud software and services leader, takes data security and compliance seriously. Its security team realized that its existing security tools, designed to identify and prevent 'known' threats, didn't go far enough to reduce the risk of malicious attacks. Advanced hackers know better than to use 'known' attack profiles. Responsys realized that the best way to identify these advanced threats was through behavioral analytics.
"Finding the 'bad guy' wasn't going to be a winning battle if we could only use 'known bad' searches and rules," said Craig Merchant, senior security architect at Responsys. "With Prelert's Anomaly Detective, we can proactively monitor our environment for 'unknown' advanced threats and quickly identify behaviors that are categorically diﬀerent than 'normal.'"
"To be effective against modern cyberthreats, IT security has to be data driven. It is beyond human ability to manually analyze the volume of data," said David Monahan, research director, risk and security management, at Enterprise Management Associates Inc. "Prelert's behavior-based machine learning analytics engine is a timely entry to the market, moving beyond traditional log management and SIEM. Prelert provides advanced Security Analytics capabilities to automatically identify anomalies that IT teams need to know about."
Anomaly Detective 3.0 End-User Case Study Webinar
Prelert is hosting a free webinar on Anomaly Detective 3.0 titled "Detecting Security Anomalies with Machine Learning Analytics," on Wednesday, Nov. 20, 2013, at 12PM EST. The webinar will be presented by Craig Merchant, senior security architect at Responsys and Rich Collier, director of product management at Prelert. To register, go to http://info.prelert.com/responsys-webinar-registration.
Availability and Pricing
Anomaly Detective 3.0 is now available and easily downloadable from the Prelert website and from Prelert resellers. Pricing is based on the amount of data analyzed per day, starting at $100 per month for environments indexing more than 500MB of data per day.
Additional information about Anomaly Detective 3.0 can be found by downloading the data sheet or visiting the Prelert website.
Mining answers from the volume of data available today to drive business decisions requires advanced analytics approaches that, until now, have required the expertise of data scientists. Prelert is the first vendor to provide this data science packaged to provide a time to value in minutes and enable IT and business professionals to make better decisions. Prelert: Data science for everyday decisions. www.prelert.com.