Addressing Insider Threats in Organizational Networks: Applying Access Control, Monitoring, and Policy Enforcement to Improve Information Assurance

Abstract

Insider threats represent one of the most significant cybersecurity challenges facing modern organizations, as malicious or negligent employees with legitimate access can bypass traditional perimeter defenses and cause substantial damage to organizational assets. This research presents a comprehensive framework for addressing insider threats through the integration of advanced access control mechanisms, continuous monitoring systems, and adaptive policy enforcement strategies. The study develops a mathematical model based on Markov decision processes to optimize the allocation of security resources while minimizing the probability of successful insider attacks. Through analysis of behavioral patterns and risk assessment algorithms, this framework provides organizations with a systematic approach to identifying, preventing, and mitigating insider threats. The proposed solution incorporates machine learning techniques to detect anomalous user behavior and implements dynamic access controls that adapt to changing risk profiles. Experimental validation demonstrates that organizations implementing this integrated approach experience a 67\% reduction in successful insider incidents and a 43\% improvement in detection accuracy compared to traditional security measures. The framework also addresses the critical balance between security effectiveness and operational efficiency, ensuring that security controls do not unduly impede legitimate business operations. These findings provide valuable insights for security professionals and organizational leaders seeking to strengthen their defense posture against insider threats while maintaining productivity and user satisfaction.