ABI: Machine Learning to Boost Cybersecurity Spending

With cyber criminals constantly adapting to industry defenses, creating new ways to commit cybercrimes, the cybersecurity industry is increasingly looking toward machine learning and artificial intelligence to help provide better deterrents, according to a new study from ABI Research.

That increased reliance on automatic, intelligent processes for deterring cyber criminals will result in an increase in big data, intelligence and analytics spending, to the tune of $96 billion by 2021, according to the report.

“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 (SIEM) and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years.”

Besides banking, government and defense, it’s the technology market sector primarily driving adoption of machine learning technologies, according to ABI. User and Entity Behavioral Analytics (UEBA), and “deep learning” algorithm designs are becoming two of the more prominent technologies in cybersecurity solutions, their research found.

The report made special note of IBM, and how its focus “will transform the way enterprises employ machine learning in every market sector, from healthcare to enterprise analytics to cybersecurity.”

“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,” Pavlakis said. “The proliferation of machine learning is also causing an explosion of agile startups, such as JASK, focusing more on SIEM complementary network traffic analysis … .”