Adaptive Security in Hybrid Cloud Environments: Leveraging AI and Machine Learning

Yamini Kannan

Abstract: In today's dynamic digital landscape, hybrid cloud environments have become essential for organizations seeking to balance scalability, flexibility, and cost-efficiency. However, this integration of private and public cloud infrastructures brings unique security challenges that traditional, static security measures struggle to address. This paper explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing security within hybrid cloud environments. By leveraging AI and ML, organizations can implement adaptive security measures that dynamically adjust to evolving threats. We discuss key components such as real-time threat detection and response, predictive analytics for threat prevention, and anomaly detection and behavior analysis. Additionally, practical implementation strategies, tools, and real-world case studies demonstrate the effectiveness of these technologies in bolstering security. The findings underscore that AI and ML are not just enhancements but essential elements of a robust security posture in hybrid cloud landscapes.

Keywords: Hybrid Cloud, Adaptive Security, AI, Machine Learning, Threat Detection, Predictive Analytics, Anomaly Detection, Cybersecurity