The War with Algorithms: Why Your Next Security Strategy Includes A.I. and Machine Learning

The image of a hooded individual illuminated by the glare of a computer screen hacking into a company’s network is the classic picture of what a cyber attack looks like. The reality, though, is these attackers are almost never a one-man band, but rather a sophisticated team armed with the same technology companies often deploy against them. But what makes these threats from these attackers so devious is not the technology they use, but rather that they never use the same approach twice.

“Cyber is big business and unfortunately it's causing a tremendous amount of business disruption. They continue to increase in sophistication and they have new and novel approaches. There are constantly unknown threats and new threat actors and that's part of the reason it gets very challenging. In some ways, a lot of the focus is people love to hear about all these cool techniques they use, but it's the fact that they're constantly evolving and changing the techniques that make it so difficult for security teams and for what I think of as static security tools to stay current against this range of threats.”

Nicole Eagan is the Chief A.I. Officer at Darktrace, a leader in autonomous cybersecurity A.I. On this episode of IT Visionaries, Nicole dives into why A.I. is a good tool, but not one powerful enough to prevalent today’s cybercriminals. Plus, she explains how security systems must constantly be learning, the impact IoT devices have had on security threats,  and why algorithms are at a war with one another.

Main Takeaways

• Update Available: Companies are utilizing A.I. to predict and prevent attacks, but there is one major issue: the data sets they are using to prevent attackers from entering their networks are often filled with old datasets. By using old data, your artificial intelligence cannot understand how to protect you from new attacks.
• The War of Algorithms: Future cyber attacks are no longer going to simply come from hackers infiltrating a network. Instead, these attackers are becoming more sophisticated and will use machine learning techniques to understand the type of behaviors the user is using in order to build a sophisticated attack. The only way to combat these types of attacks, is to use real-time self-learning A.I. systems that can detect abnormalities within a system.
• Stop Acting in Silos: Security prevention needs to stop operating in silos when it comes to preventing attacks. Cyber criminals are no long attacking users through email or through loca networks, so security teams need to stop focusing on a niche area. Instead, develop an all encompassing self-learning platform that is constantly monitoring all aspects of your business and your employees.

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