The most common attacks are malware, phishing, and spear phishing, all of which are backed by social engineering functions such as open source data gathering, pre-texting and gathering information from social media, news outlets and other resources. How we respond to these threats will help determine how successful we are, according to McReynolds.
“We have decided that we as human beings do not have the speed, the dexterity, or the accuracy to keep up with the way a lot of these attacks are utilized. So, we are really leaning in on AI, machine learning and neural networking in order to power up security tools to combat these threats,” McReynolds said.
Anti-malware solutions are one form of machine learning used for cybersecurity defense, McReynolds said. It leverages a machine-learning database that uses previously detected forms of malware to identify various indicators of compromise. Anti-malware can be very adept at recognizing and protecting against malware variants. AI-based security tools are another form of protection. These tools can automate and orchestrate defense mechanisms and are available to respond and operate 24/7. Another popular trend is reflected in AI analytics algorithms, which are based on standard network behavior. These can yield highly accurate, timely and unique identification of unusual behavior from a system or user that we as human beings may not catch.
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