Comparative Analysis of Modern Technological Approaches to Insider Threat Detection

Abstract — The increasing cybersecurity risk of insider threats stems from authorized personnel potentially misusing their access to compromised vital data, intellectual assets, and essential infrastructure. Conventional rule-based detection systems often struggle to identify complex insider attacks owing to their dynamic and context-specific nature. This study offers a comprehensive literature review of AI-based approaches for detecting insider threats, emphasizing methodologies, obstacles, and future research avenues. Utilizing the PRISMA framework, we performed a structured analysis of research published from 2018 to 2025. We identified crucial AI techniques such as deep learning models (CNNs, RNNs, BERT), hybrid methods incorporating graph neural networks (GNNs), and reinforcement learning. [10][15] We investigated real-world examples, including DS-IID for synthetic threat detection[1] and Ripple2Detect[2], for multistep evidence extraction. [3][14] Our results underscore significant challenges, such as adversarial AI threats, dataset biases (e.g., CERT dataset limitations), scarcity of data, and rapidly evolving attack strategies. [2][13] Additionally, we delve into the practical implications of AI-driven detection methods in corporate settings, concentrating on integration with Security Information and Event Management (SIEM) systems, and ethical issues related to employee privacy and false positives. The study concludes by proposing recommendations for standardized evaluation benchmarks, real-time detection frameworks, and human-in-the-loop AI systems to improve the dependability of insider threat detection solutions