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Cybereason has been named to the 2018 AI 100, which ranks the 100 most promising artificial intelligence companies in the world. Further adding to this honor, we’re one of just six cybersecurity companies that made the list.

Cybersecurity is one of the toughest application domains in IT for artificial intelligence because in security we have an active, human intelligence that is still the most effective intelligence, from a broad perspective, in the world. While the advent of new technology has historically resulted in some jobs being replaced by machines, artificial intelligence will have the opposite impact in cybersecurity; it will lead to the creation of more jobs in addition to improving existing ones. Employees will have more time for analytical work instead of sifting through reams of data, a task better suited for artificial intelligence. And new employees will learn skills and gain experience faster than is currently possible, thanks to artificial intelligence showing them new ways of thinking and viewing data.

Artificial intelligence in security raises fears of black hats using it to enhance the complexity and scope of their attacks. Indeed, we’ve seen human adversaries already start to use artificial intelligence and machine learning selectively to augment human capability. This development has made our industry the best testing ground for artificial intelligence versus human intelligence and artificial intelligence versus artificial intelligence. This means our applications are improving the raw capability of artificial intelligence at a colossal rate. Being on the AI 100 is like saying “these are the companies that will matter in security and the others are asleep at the switch and fading.” Incidentally, now we have a second proving ground with our competition to apply artificial intelligence most effectively to the problem set among those of us on the list. That’s energizing and exciting to those of us doing this right.

And we believe that we’re doing it right. Cybereason’s platform was built from the ground up for scale and for artificial intelligence and machine learning at all levels: machine level, organization/enterprise level, regional/cluster and full global context levels. The trick is the right data, in the right structures, with the flexibility to bring the full toolkit to bear. That approach beats other approaches from a raw mathematics perspective where others seem to merely throw the terms around.