Orgs Say Yes to AI Use But Ask “What Is It?”

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Organizations across the US and Japan have plans to increase their use of artificial intelligence (AI) and machine learning (ML) this year, yet many don't really understand the technology, according to a new report from Webroot.

A survey of 400 IT professionals from businesses across the US and Japan, conducted by LEWIS between November 26 and December 5, 2018, asked participants whether they plan to implement AI and ML. The results, published today in the global report, Knowledge Gaps: AI and Machine Learning in Cybersecurity, revealed that the vast majority (71%) said yes; however, more than half (58%) of those respondents said they are not exactly sure what the technology really does.

Equally notable was the survey finding that 76% of respondents said they don’t care if their companies leverage the technologies, yet an overwhelming number (86%) of IT professionals believe cyber-criminals are using AI/ML tools to attack public and private organizations.

While 83% of IT professionals are confident their organization has everything it needs to defend against advanced AI- and ML-based cyber-attacks, 36% reported their organization has suffered a damaging cyber-attack within the last 12 months despite their having used AI/ML security tools.

"AI and ML continue to present a troubling knowledge gap, particularly given the amount of confusing hype in the cybersecurity industry. A company cannot properly defend against advanced AI and ML attacks when less than half of its IT professionals are comfortable using the tools needed to defend against those attacks,” said Hal Lonas, CTO, Webroot.

“To level the playing field, organizations need to partner with vendors that have the historical data and skilled staff required to deliver the highest level of efficacy and automation to their customers. And even though 70 percent of professionals in the survey say it's very important that vendors mention the use of AI and ML in their advertising, advertisements should be validated by quality data."

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