AI and machine learning are part of the solution in many cybersecurity products. The issue is that machine learning tends to take a lot of time. Cyber threats are moving quickly so computers need to learn fast to stop them. Chuck Everette, the Director of Cybersecurity Advocacy at Deep Instinct, breaks down the difference between machine learning and deep learning. 

Main Takeaways

• Deep Learning Versus Machine Learning: As Everette describes the difference, machine learning is about a human delivering items alongside their classifications to a computer; whereas with deep learning, a computer is able to determine these classifications by itself. Deep learning, therefore, according to Everette’s description, can reduce the human role in the learning process while also increasing the learning speed for the computer. Though this technology has applications in security, certainly deep learning can have a profound impact across a range of applications. 
• Weaponized AI: Everette tells the scary truth that attackers are now so sophisticated that they are using their own AI as a weapon, and that these attackers are “more patient these days.” This reality means that defenders have to keep upping their game to thwart these attacks. In any industry, it’s wise to remember that just because you are pressing an advantage, it doesn’t mean your adversary isn’t doing the same thing. 
• From Games to Cybersecurity: Reflecting back on his career, Everette points to a love of gaming as his initial step toward cybersecurity. Although he had a winding career path from a love of games to cybersecurity, it is interesting to consider how one passion, that others might even disregard, can eventually lead to an entire career. 

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