A new artificial intelligence (AI) system developed by MIT researchers promises to offer increased threat detection capabilities and reduce false positive rates, boosting incident response and productivity in the security world. The team, based at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), detailed in the paper AI2: Training a big data machine to defend [PDF], how the new platform achieves three times higher prediction capabilities, and is able to deliver significantly fewer false positive rates than current analytics models. The team showcased the AI2 platform last week at the IEEE International Conference on Big Data Security, and released the study to the public earlier today. The paper explains how the tool combines AI with'analyst intuition' to create a learning model whereby intermittent human analyst feedback is layered into a continuous unsupervised machine learning system. "You can think about the system as a virtual analyst," commented CSAIL research scientist Kalyan Veeramachaneni, who designed AI2 alongside PatternEx chief data scientist and former CSAIL researcher, Ignacio Arnaldo. "It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly," he added.