AI, Machine Learning, and Cybersecurity

By: Chaz Wellington
Reporter, User Friendly 2.0, Saturday’s at 5:00 p.m.

AI, Machine Learning, and Cybersecurity   

There was a time when I, like many others, walked into a store and purchased a big box with a simple CD and booklet to install antivirus software.  It was a great feeling of “supposed” security and invincibility. Unbeknownst to me at that time, there was not and never will be a one-hundred percent secure system.

Technologies such as AI and machine learning have evolved over the last decade and are at the forefront of cybersecurity strategy. The old ways of protecting assets,such as the anti-virus in a box, are long gone, and we can now utilize a more robust and advanced strategy.

AI, or Artificial Intelligence, has come a long way and will continue to do so with the advancements of quantum computers.  AI is the broader concept of the ability of machines to carry out tasks deemed “intelligent.”  However, I'll narrow that concept down specifically to cybersecurity. AI is a driving force behind digital transformation and encompasses several innovations. AI in cybersecurity enhances and improves its knowledge by gathering billions of threat intelligence data artifacts and can more effectively analyze the relationship between threats. 

Technologies such as AI and machine learning have evolved over the last decade and are at the forefront of cybersecurity strategy.  The old ways of protecting assets, such as the antivirus in a box, are long gone, and we can now utilize a more robust and advanced strategy. 

On the other hand, we have machine learning in which the machine is coded to “think like humans” and connected to the internet. AI and machine learning sound very similar; however, machine learning is actually a subset of AI. AI mimics human intelligence using logic, if-then rules, decision trees and machine learning. Machine learning enables machines to improve at tasks with experience.  In this scenario, the devices have the information in hand to learn rather than having to teach machines from the start. The combination of these two lowers IT operations costs, increases cyber analyst effectiveness, and increases the speed of detection and response.

XDR, or Extended Detection and Response, could be the icing on the cake.  XDR is relatively new to the cyber world.  Gartner's 2021 XDR Report defines XDR as “a platform that integrates, correlates, and contextualizes data and alerts from multiple security prevention, detection, and response components.” XDR takes the information from the EDR, NDR, SIEM, and threat-related feeds and correlates information giving a single view.  The combination of AI, Machine Learning, and XDR in sync within cyberspace most certainly enhances the abilities of an extensive list of tools currently available.

Chaz Wellington is a United State Army Veteran, having served eight years in the military and is currently a SOC Analyst and also the Las Vegas Unit Production Lead/Staff Reporter for User Friendly 2.0.  He also serves on the Technology Committee for the Nevada Hotel and Lodging Association. Chaz has twenty-nine years experience in the casino gaming industry including but not limited to Casino Table Games Operations, Cage Operations, and Slot Operations. Article edited by Gretchen Winkler, who along with Bill Sikkens and Jeremy Winkler are the co-hosts of User Friendly 2.0 here on The Answer Saturdays at 5:00 p.m.

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