Organizational Environment, Protection Motives, Adoption of Machine Learning, and Cybersecurity Behavior

Abstract

The research presented in this intensive study uncovers the complex interaction between organizational and individual drivers affecting cybersecurity behavior within organizations. Its major focus is analyzing the impact of organizational policies and culture, training, and awareness programs on the cybersecurity conduct of employees. This paper also examines self-protective behavior related to risk perception, perceived self-efficiency, and response efficacy, which explains how these variables may influence self-protective behaviors. According to the Protection Motivation Theory (PMT), the study unravels how individuals' different elements of threat appraisal, coping appraisal, and cost-benefit analysis contribute to their decisions to apply cybersecurity behaviors. It also underlines that machine learning is being used more and more in cybersecurity and demonstrates some important ML concepts in modern cybersecurity, such as picking up the right data to train with, coping with adversarial attacks, and, last but not least, making the models interpretable. Ultimately, having a comprehensive understanding of these cybersecurity behaviors may shed new light on mechanisms that can be used to develop cybersecurity strategies and technologies to help protect against the rapidly changing landscape of cyber threats. All research papers have an abstract. It can be created by simply compiling the main points (summary) of each section of the research paper.