If you attend any
cybersecurity conference, expect to receive a plethora of information from companies promoting their latest solutions for security and protection. These new solutions are have begun to be marketed with their innovative use of artificial intelligence at the forefront. According to ESG research,
29 percent of security professionals surveyed hope to use AI technology to speed up the virus detection process. Plus,
27 percent are looking to this technology to accelerate their incident response time. Interest in AI security stems from the intricacy of code AI can analyze in a short amount of time.
Though AI can definitely be helpful in the cybersecurity space, generally it’s not AI that’s powering these solutions. Oftentimes, trained
machine learning and AI are terms that get confused. Where AI and machine learning differ is their ability to think without proper programming. Security companies that use machine learning write complex algorithms for these technologies to best detect security breaches. But, an AI system is able to come to new conclusions without being fed any new algorithms or data. A challenge for machine learning in the security space is that malware codes are ever-changing, which means the coders behind machine learning cybersecurity technology must constantly perfect and tweak algorithms to teach the technology how to detect these new codes. But can the defenders really keep up with the hackers? That’s definitely debatable. This is a problem AI could solve. If a sentient machine can evolve at the rate its malware counterparts, we have a much better shot of defending against it.
Machine Learning Versus AI