What’s the difference between ML and AI in Cybersecurity?

Many don’t understand how AI works in terms of cybersecurity, despite most agreeing that it is necessary to implement it going forward. According to a recent survey by Webroot, nearly 75 percent of IT professionals stated they intend to incorporate more artificial intelligence (AI) solutions into their cybersecurity initiatives in 2019. However, the same study reveals that a staggering 58 percent of these same respondents don’t fully understand how this technology works.

In this article by Security Boulevard, writer Kurt Abrahams explains,”For starters, it’s a common misconception that AI and machine learning are interchangeable. While the two are linked, AI is the concept of making systems “smart,” enabling them to complete tasks that humans typically complete. Machine learning is one of many methods used to build AI, leveraging data and patterns to replicate human behavior with limited human direction.”

The main difference between AI and ML according to MixMode’s own Chief Technical Officer Dr. Igor Mezic is, “ML is the set of underlying mathematical methodologies that make it possible to develop an AI system.“

And as far as its potential for use in cybersecurity…

“It’s not a nice thing for companies to have anymore. It is a necessity. If you don’t want to be messed with, have your data stolen or be held for hundreds of thousands of dollars by Ransomeware, you need this technology. Because the hackers already have it. It’s not a matter of if, it’s when, and it will happen if you’re not prepared. Right now is a bad time to be messing up with your network security,” Mezic said.

To read more about Mezic’s patented context-aware AI, check out this blog he wrote for our company thinktank, Mixmode Institute.

AI Assistants Will Soon Be Like Human Security Guards for Networks

This article for CIO Applications put into words exactly what Mixmode’s Context Aware AI does for a network. It goes through the hack alerts generated by regular cybersecurity software and can determine which are true attacks and effectively eliminate false positives.

This is vital to the advancement of the cybersecurity industry as a whole because the statistics surrounding false positives show that current software is practically useless if security teams aren’t able to use it effectively.

A typical IT or security team spends an average of 395 hours a week and over $1.3 million a year chasing false positive alerts according to the Ponemon Institute.

This article by CIO Applications describes what well designed AI should do for a cybersecurity platform, which is use predictive analytics based on pattern recognition that are more effective than a manual, human approach. Read More

Florida City Paralyzed By Ransomware Hack Demanding $600,000

The City of Riviera Beach, Florida was hit with a ransomware attack that resulted in them paying over $600,000. This is yet another hack in a string of ransomware attacks on US cities.

Just last month we witnessed Baltimore City suffer a hack that left their government locked out of their system for nearly a month. The hack froze hundreds of computers, disrupted email servers and billing processors for the local water supply.

We’re seeing similar extortion practices here.

“On Monday, the City Council unanimously agreed to have its insurance carrier pay the hackers 65 Bitcoin, a hard-to-trace digital currency, amounting to about $592,000. By making the payment, the City Council hopes to regain access to data encrypted in the cyberattack three weeks ago, though there is no guarantee the hackers will release the data once payment is received,” reporter Patricia Mazzei wrote for The New York Times.

By Ana Mezic, Marketing Coordinator at MixMode