Machine Learning & Risk Engines for Security Data Analysis

Is your security data telling a story? And if it is, do you know what it’s saying? Unfortunately, many companies are unable to accurately or effectively analyze their vast amounts of security data, which provides critical information regarding their security posture and top threats.

The truth is, security data analysis is both an art and a science, which has made it challenging for organizations to take advantage of. Not to mention, in the past decade, it has evolved from a “nice to have” to a “must have” element of an effective security strategy.

Due to the evolution of machine learning and risk engines, the insights gained from security data have never been more powerful. At the same time, the growing hype around machine learning has caused more confusion and made it difficult to sort through the noise – What’s real? What’s truly effective? And what’s bogus?

In this webinar, Jeremiah Cruit, CISO of ThreatX, and Dave Shackleford, a SANS Analyst, address:

• Myth busting around the possibilities of machine learning, risk engines, and statistics in security data analysis
• Tips for optimizing your strategy to obtain comprehensive and trusted application protection
• Tools to help you sort through the noise and make security data analysis more science than art