How AI and Machine Learning Are Impacting Cyber Security

When cyber security experts piece together their strategy to defeat their never-ending battle with hackers and cybercriminals, they leverage every technology advantage they can find. Today, that extra oomph they put into their cyber security plans is AI and machine learning—both powerful tools that have revolutionized the cyber security field. According to the Deloitte State of AI in the Enterprise Survey, 63 percent of companies employ machine learning in their businesses, 82 percent claim a positive financial return on their AI investment, and 88 percent will increase AI spending this year. 

And where will you find AI and machine learning in the cyber enterprise? Capgemini reports that the most popular application is network security, but AI is also being used to improve protocol for things like data security, endpoint security, and identity and access management, to name a few. The Capgemini report also reveals that 69 percent of organizations believe that they will not be able to respond to cyberattacks without AI and that the average increase in 2020 budgets for nearly one in ten organizations will be more than 40 percent higher than 2019. 

And by 2025, Zion Market Research predicts the global cyber AI market will reach $30.9 billion, with a CAGR of over 23 percent from 2019 to 2025. It’s not easy for even a large, highly skilled cyber security team to handle such a large influx of creative attacks on their infrastructure, so many are turning to AI and machine learning to lighten the load. 

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The Emerging Challenge of Machine Learning as a Service

AI and machine learning are key components of the cyber security toolkit—unfortunately, that goes for the bad guys and the good guys. According to a recent cyber security trends report, open-source AI tools used by security teams are easy to compromise by the black hats, who are adept at finding vulnerabilities. Indeed, many of those same machine learning frameworks are available “as a service” from cloud vendors like AWS, Azure, and Google Cloud. Cybercriminals now have access to ready-made infrastructure to build machine learning models. They do it at low cost and will certainly produce a growth in machine learning-driven attacks. 

Standards and Guidelines for Cyber AI

The US government is getting in on the action too, of course. The National Science and Technology Council (NSTC) Machine Learning and Artificial Intelligence (MLAI) Subcommittee is now assessing challenges and opportunities in cyber AI. Guidelines dictate that AI investments must advance both theory and practice of cyber AI deployment. Those efforts should produce secure training, build defensive models, verify system robustness, fairness, and privacy, and ensure AI-based decision-making is based on reliable methods and AI-human systems. 

NSTC work reveals various cyber AI techniques, including network monitoring to detect suspicious activity and anomalies, analysis to identify coding vulnerabilities, and the ability to create defensive patches at the first sign of an attack. AI runs these analyses almost immediately—far faster than their human counterparts. And given how fast cyberattacks can penetrate infrastructure, analysis and response should take place within seconds, not days or weeks. 

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Areas Where AI is Becoming Vital

AI is handy in recognizing intrusions the moment they occur. According to AI Authority, it does this by instantly reviewing the large database of digital footprints that past hackers leave when attempting to access an internal system. Only AI can perform this task so quickly. Embedded systems such as video cameras, printers, and IoT devices are particularly vulnerable to attack. 

The report also cites other examples of cyber AI and machine learning such as: 

  • Spam filter apps like Gmail. AI is trained by billions of active Gmail users and their spam recognition.
  • Fraud detection. An example is with MasterCard, which is using AI algorithms to predict and recognize customer behavior and see if it is out of the ordinary. 
  • Botnet detection. AI can readily detect botnet attacks that usually rely on multiple “users” that perform a repeated request or attack on a website, all driven by a master script. 

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Getting Your Teams the Right Cyber Skills

Bad actors in cyber security are on a never-ending quest to bring down corporate networks, and their skill sets are formidable. Cyber security teams must follow suit and keep up with the latest skills, technologies and techniques to stay in the game. 

Cyber Security Experts combine a wide range of skills training to secure data, run risk analysis and mitigation, architect cloud-based security and achieve cyber security compliance. AI engineers are now critical players for cyber teams. They are trained to create real-world applications using a wide range of intelligent tools and techniques. Machine learning experts are additional assets that master hands-on modeling to create tangible, effective defensive cyber systems. And, when it comes to mastering the right computer language for cyber AI, look no further than Python, which has become vastly popular. Leveraging AI in your cyber security plan is perhaps the most important move you can make in today’s world. 

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About the Author

Stuart RauchStuart Rauch

Stuart Rauch is a 25-year product marketing veteran and president of ContentBox Marketing Inc. He has run marketing organizations at several enterprise software companies, including NetSuite, Oracle, PeopleSoft, EVault and Secure Computing. Stuart is a specialist in content development and brings a unique blend of creativity, linguistic acumen and product knowledge to his clients in the technology space.

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