The rare form of machine learning that can spot hackers who have already broken in

In 2013, a group of British intelligence agents noticed something odd. While most efforts to secure digital infrastructure were fixated on blocking bad guys from getting in, few focused on the reverse: stopping them from leaking information out. Based on that idea, the group founded a new cybersecurity company called Darktrace.

The firm partnered with mathematicians at the University of Cambridge to develop a tool that would use machine learning to catch internal breaches. Rather than train the algorithms on historical examples of attacks, however, they needed a way for the system to recognize new instances of anomalous behavior. They turned to unsupervised learning, a technique based on a rare type of machine-learning algorithm that doesn’t require humans to specify what to look for.