Insider Threats in Cloud-Based Network Environments: A Preventive Framework

Abstract

Insider threats represent one of the most critical challenges in securing cloud-based network environments. These threats stem from individuals with authorized access who may misuse their privileges either maliciously or unintentionally. The dynamic and distributed nature of cloud systems exacerbates the detection and mitigation of such threats. This research proposes a preventive framework that integrates behavioral authentication, anomaly detection, and fine-grained access control to mitigate insider threats effectively. The proposed model is designed to continuously monitor user behavior, identify deviations from normal usage patterns, and enforce adaptive access controls in real time. The framework aims to enhance the security posture of organizations leveraging cloud infrastructures without compromising performance or user experience. Results from simulated environments demonstrate the model’s potential in detecting unauthorized behavior with high accuracy and low latency.