Managing Security Hazards in BYOD: A Comparative Analysis of Artificial Intelligent Techniques

Bring Your Own Device (BYOD) policies have gained popularity in organizations, offering flexibility and increased productivity. However, they also introduce security hazards that pose risks to sensitive data and systems such as data leakage, weak authentication, malware and viruses, unsecured Wi-Fi networks, device loss or theft, and compliance issues. These problems continuously affect to the organization. Therefore, these problems can be solved by adopting artificial intelligent (AI) techniques. This paper investigates the possibility of potential solutions by adopting AI techniques such as machine learning-based anomaly detection, behavior analysis, predictive analytics, and natural language processing will be examined as potential solutions. The objective of this paper is to identify and compare various AI techniques in addressing the security hazards associated with BYOD. The methodology involves a comprehensive review of relevant articles and research papers to gather insight findings. The results of this research will provide a summary of available AI techniques in monitoring the BYOD security hazards.