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Bridging knowledge gap: the contribution of employees’ awareness of AI cyber risks comprehensive program to reducing emerging AI digital threats
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Bridging knowledge gap: the contribution of employees’ awareness of AI cyber risks comprehensive program to reducing emerging AI digital threats
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Journal Article
Bridging knowledge gap: the contribution of employees’ awareness of AI cyber risks comprehensive program to reducing emerging AI digital threats
2024
Overview
Purpose
In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.
Design/methodology/approach
Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.
Findings
A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.
Research limitations/implications
This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.
Practical implications
It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.
Social implications
Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.
Originality/value
Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.
