AI-Based Insider Threat Detection in Cloud Environments Using Behavioral Profiling
Abstract
Insider threats remain a significant challenge for securing cloud-based systems. This paper presents an AI-based insider threat detection framework that uses behavioral profiling and anomaly detection to identify malicious insider activity. The framework collects user activity logs, access patterns, and system interactions, applying machine learning models to detect deviations from normal behavior. Detected anomalies trigger automated response actions, including session termination and privilege revocation. Performance tests show high accuracy in detecting insider threats with minimal false positives. The study concludes that AI-driven behavioral profiling enhances the ability to identify and mitigate insider threats in cloud environments.
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