Impact of Artificial Intelligence on Employee Strain and Insider Deviance in Cybersecurity

Abstract

This paper examines the impact of AI technologies like Performance Monitoring Tools (PMTs) and Automated Decision-Making Systems (ADMSs) on employee strain and the development of insider deviant behavior. Drawing on General Strain Theory (GST), the study explores how workplace stressors exacerbated by AI-driven PMTs and ADMSs may increase the risk of deviant behaviors such as fraud, sabotage, and social engineering. This study employs a quantitative methodology, using surveys to gather data on employee perceptions of AI-driven PMTs and ADMSs on employee strain and insider deviance. We expect that the findings will show AI-induced stress and negative emotions increase the likelihood of insider deviance. This study aims to contribute to research on cybersecurity threats and provide practical insights for organizations implementing AI technologies by offering strategies to mitigate workplace stress and insider threats. Future research will explore the relationship between AI integration, employee strain, and organizational security vulnerabilities.