Machine Learning Has Become Critical to Effective Cybersecurity

John Flores
January 10, 2019
Advanced Analytics | Blogs | Cybersecurity

Cybersecurity has always been something of a cat-and-mouse game, with security experts constantly implementing new measures and hackers finding new weaknesses to exploit. Machine learning is making it possible to breed a smarter cat — but the mouse is getting smarter, too.

In a recent survey conducted by Wakefield Research, 95 percent of IT security professionals said that machine learning has become a critical component of an effective cybersecurity strategy. Machine learning is a form of artificial intelligence (AI) that automates the building of analytical models. It enables computer systems to use data to continually improve on their ability to perform specific tasks by learning from experience rather than being explicitly programmed.

AI and machine learning have become practical in recent years thanks to processors capable of performing all the necessary calculations and cloud platforms that provide near-infinite data storage. Some common machine learning applications include image and speech recognition, medical diagnosis, and trading systems in the financial sector.

Humans are much smarter than machines but we aren’t very good at processing large volumes of data. That has always been a hindrance to effective cybersecurity, which involves the collection and analysis of massive amounts of data from system logs and user activity. The sheer number of alerts generated by many cybersecurity tools is enough to overwhelm human analysts.

But that’s where machine excel. Machine learning makes it possible to quickly perform pattern recognition, anomaly detection and predictive analytics to identify potential threats and weed out false positives. This cuts down on the “noise” so humans can focus on the most serious threats.

That’s the future envisioned by respondents to the Wakefield survey. Overall, 99 percent of U.S. cybersecurity professionals believe AI could improve their organization’s cybersecurity, particularly when it comes to time-critical threat detection tasks. Eighty-seven percent report that their organization is currently using AI as part of their cybersecurity strategy, and 97 percent say their organization plans to increase budget for AI and machine learning tools within the next three years. Three-quarters believe that, within the next three years, their company will not be able to safeguard digital assets without AI.

However, 91 percent are concerned about hackers using AI against companies. In fact, cybercriminals are beginning to use AI and machine learning to develop more advanced threats. As they continue to innovate, organizations will have to get creative to stay ahead of them.

There are a number of things to keep in mind if you plan to incorporate AI and machine learning into your cybersecurity strategy. First, you should recognize that these technologies cannot replace humans. Machine learning requires human training and oversight. The machines must be taught what is bad, what is good, and when to flag unknown threats to humans.

Second, let the machines solve the simpler problems, so human experts have more time to think of new ways to solve more complex problems. Deploy AI and machine learning technologies to automate and speed up security operations and repetitive tasks.

And, finally, accept that your systems will be compromised at some point even if you implement machine learning. Rather than viewing this as a net-negative, organizations can learn from breaches by analyzing normal and abnormal network behavior to gain a greater understanding of threats and how to respond. Hackers are employing advanced AI tools to launch more sophisticated cyberattacks. To stay ahead in this cat-and-mouse game, organizations should begin incorporating machine learning into their cybersecurity strategies.

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