Insider Threat Detection Methodologies

Insider threats, originating from individuals with legitimate access to sensitive systems and data, represent a significant cybersecurity challenge, unlike external attacks, insider threats are harder to detect, as they often exploit legitimate credentials to bypass conventional security measures. These threats can result in severe consequences such as data breaches, financial losses, and system disruptions. Traditional detection methods, such as rule-based approaches and classical ma- chine learning models, struggle to identify evolving and sophisticated insider behaviors due to their reliance on predefined patterns and static detection criteria. Recent advancements in artificial intelligence (AI), deep learning, cryptographic security and hybrid detection frame- works have significantly enhanced the ability to detect and mitigate insider threats. Deep learning models, such as Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), excel at identifying subtle behavioral anomalies, while cryptographic techniques, such as blockchain-based authentication and data encryption, reinforce security by preventing unauthorized access. Hybrid approaches that combine AI-driven anomaly detection with structured security control mechanisms have emerged as the most effective solution, offering multi-layered protection against insider attacks. The primary objective of this paper is to present a comprehensive review of insider threat detection methodologies, comparing traditional and AI- based approaches, including specification-based detection, behavioral monitoring, anomaly-based models and cryptographic security measures. The study highlights the strengths and limitations of each method and explores future research directions, including the development of self-supervised learning models, explainable AI and optimized real-time detection systems. A holistic security strategy, integrating AI, cryptographic security and policy-driven risk mitigation is n