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Almost two thirds of UK workers have received no training on spotting AI-driven cyber threats

Those that did, received it as part of generic training, not dedicated AI-oriented education.

Almost two thirds of UK workers have received no training on spotting AI-driven cyber threats

A recent survey has revealed that a significant majority of UK workers, 61%, have received no training at all on how to detect AI-driven cyber threats. This lack of training has put organizations at risk as AI-powered cyber attacks become more prevalent. Younger employees, aged 18-24, are now expressing the lowest level of confidence in spotting AI scams compared to their older counterparts.

The research, conducted by QA, also highlights an alarming gap in how prepared businesses are against these threats. For instance, while 48% of owners/partners claimed they were very confident in identifying AI-generated phishing emails, only 9% of non-management workers felt the same. Moreover, only 25% of workers who received any form of training described it as being specifically focused on AI-driven threats.

The survey also pointed to a concerning trend where AI is being used to create deepfake videos and audio, further complicating the task of spotting AI-driven cyber threats. A notable example is the case of Paolo Molesini, former chairman of Fideuram-Intesa Sanpaolo Private Banking, who fell victim to an AI-powered phishing attack and wired over $100 million overseas.

Furthermore, 22% of the respondents mentioned that their organizations lacked policies to combat phishing emails, deepfake voice scams, or AI-generated fake invoices. This lack of preparedness coupled with the increasing sophistication of AI-powered threats has made the younger workforce, who have grown up with technology, the least confident in preventing cyberattacks. As such, there is an urgent need for organizations to build foundational AI literacy among their employees to effectively combat these emerging threats.

Written by urgent.news from TechRadar's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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