A Holistic Approach with Behavioral Anomaly Detection (BAD) for Mitigating Insider Threats in Cloud Environments

Insider threats remain a critical concern in cloud environments, necessitating robust strategies for detection and mitigation. This paper proposes a holistic approach that integrates Behavioral Anomaly Detection (BAD) with existing security measures to mitigate insider threats effectively. BAD focuses on identifying abnormal behaviors that deviate from established patterns, making it a valuable tool for detecting insider threats. The holistic approach combines BAD with other security mechanisms such as access control, encryption, and user monitoring to create a comprehensive defense strategy. The key advantage of this approach is its ability to adapt to the dynamic nature of insider threats. By continuously analyzing user behavior and identifying anomalies, the system can detect potential threats early, allowing for timely intervention. Moreover, the holistic approach reduces the number of false positives compared to using BAD in isolation, thereby improving the efficiency of threat detection. To demonstrate the effectiveness of the approach, we provide a detailed implementation plan and showcase case studies from real-world cloud environments. Our evaluation results indicate that the holistic approach significantly enhances the security posture of cloud environments against insider threats. By leveraging BAD and integrating it into a holistic security framework, organizations can better protect their sensitive data and infrastructure from insider threats. This research contributes to the field of cloud security by providing a practical and effective strategy for mitigating insider threats. It also opens up avenues for future research, such as exploring the integration of machine learning techniques to enhance BAD's detection capabilities further.