Machine learning, the new standard in the cybersecurity revolution

Data breaches and online security lapses cost companies more than just money. It’s also about their reputation and the effort needed to restore systems and enhance security after an attack. This leaves us wondering what the real cost of cybercrime is and how strategies can be improved to fight off attacks.

As cybercrime damages are forecast to reach a staggering $2 trillion globally by 2019, machine learning applications aiming to better safeguard IT infrastructures from unknown threats will boost investments in big data, intelligence and analytics to $96 billion by 2021, according to ABI Research.

“We are in the midst of an artificial intelligence security revolution,” says Dimitrios Pavlakis, Industry Analyst at ABI Research. “This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years.”

The implementation of machine learning applications will increase anomaly detection rates in systems. User and Entity Behavioral Analytics (UEBA) and Deep Learning algorithm designs will stand out as the driving technologies behind cybersecurity strategies.

The sectors that will immediately embrace machine learning when under attack by cybercriminals are government and defense, banking and technology.

 “This radical transformation is already underway and is occurring as a response to the increasingly menacing nature of unknown threats and multiplicity of threat agents,” concludes Pavlakis.

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