Being a cybersecurity analyst at a large company today is a bit like looking for a needle in a haystack -- if that haystack were hurtling toward you at fiber optic speed. Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that "needle" and stop it before it does any damage. The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.

The workshop will be led by two AI security experts with years in the field, and will demystify Artificial Intelligence before exploring its application in cyber defense. Dr. Kalyan Veeramachaneni is currently a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and has been conducting research in the field for years. He will be joined by Dr. Ignacio Arnaldo, the Chief Data Scientist at PatternEx and former researcher at MIT CSAIL. Together they will facilitate an interactive, vendor agnostic discussion which will provide a foundation for understanding emerging artificial intelligence solutions in InfoSec. "During the time that we have been talking with security leaders from around the world we have found a strong interest in the potential of AI in InfoSec, but there is too much jargon and marketing speak out there," comments Travis Reed, CMO for PatternEx.

Cybersecurity threats are among the most pressing concerns for businesses and institutions that need to protect information, but today's security systems are limited. Most security systems fall into two categories: human analyst or machine learning. Now, a new research paper from MIT shows that a combination of human experts with a machine learning system--in other words, supervised machine learning--provides better results than either human or machine alone. "AI squared," which uses a system developed by PatternEx, is 10 times better at catching threats than machine learning alone, and reduces false positives by a factor of five. This, said MIT's researchers, is three times better than current benchmarks.