Cybersecurity professionals have a hard job. Not only are they tasked with developing solutions to constantly changing risks, but they cannot know what those attacks will consist of until after they've already been launched. Though cybersecurity experts can certainly offer insights into what digital dangers may come next, these predictions are limited and make proactive solution development challenging. Luckily, by increasing the use of machine learning, cybersecurity groups are able to take advantage of advanced pattern recognition technologies to better determine what attacks are on the horizon. On the surface, it seems like cybersecurity professionals would be focused on designing stronger barriers to attack and establishing firmer encryption standards, but at its core, the field is driven by data.

Henry Ford revolutionized the automobile industry--and manufacturing in general--with the concept of the assembly line. Vehicles existed and were being manufactured before Ford introduced the assembly line but streamlining and automating the manufacturing process allowed vehicles to be produced significantly faster. The world of cybersecurity is at a similar impasse and facing a similar revolution in terms of speed--thanks to machine learning (ML) and artificial intelligence (AI). The concepts of vulnerability scanning and vulnerability management are not new. The idea that organizations need to monitor application activity and behavior to identify indicators of compromise and detect suspicious or malicious activity is also relatively common.