AI-Based Real-Time Security Monitoring for Cloud-Native Applications in Hybrid Cloud Environments

Abstract

In an era where cloud-native applications dominate the digital landscape, ensuring their security within hybrid cloud environments has become paramount. This paper presents an AIbased real-time security monitoring framework designed to enhance the protection of cloud-native applications operating across multiple cloud platforms. The proposed framework leverages advanced machine learning algorithms and anomaly detection techniques to continuously monitor application behavior, network traffic, and user activities, identifying potential security threats in real-time. By integrating automated response mechanisms, the framework not only detects but also mitigates security incidents, ensuring minimal disruption to operations. We evaluate the effectiveness of the proposed solution through extensive experiments conducted in a simulated hybrid cloud environment, demonstrating its capability to achieve high detection rates, low false positives, and rapid response times. The results indicate a significant improvement in the overall security posture of cloud-native applications, making them more resilient against emerging cyber threats. This research contributes to the growing body of knowledge on cloud security and offers practical solutions for organizations leveraging hybrid cloud infrastructures