LEVERAGING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR THREAT DETECTION IN HYBRID CLOUD SYSTEMS

Phani Sekhar Emmann

Authors

Phani Sekhar Emmann Technical Project Manager, IBM, United States, Author

Keywords:

Artificial Intelligence (AI), Machine Learning (ML), Hybrid Cloud Securit, Threat Detection, Cybersecurity, Anomaly Detection

Abstract

In an era where hybrid cloud systems are increasingly becoming the backbone of enterprise IT infrastructures, the complexity and sophistication of cyber threats have escalated, posing significant security challenges. Traditional security measures, while necessary, are often insufficient to address the dynamic and evolving nature of these threats. This article explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) as transformative tools for enhancing threat detection and response mechanisms within hybrid cloud environments. By leveraging the capabilities of AI and ML, including anomaly detection, pattern recognition, and predictive analytics, it is possible to develop more proactive and adaptive security strategies that can keep pace with advanced cyber threats. This research evaluates the effectiveness of AI/ML technologies in detecting and mitigating security risks, compares their performance to traditional security approaches, and discusses the integration of these technologies into existing hybrid cloud architectures. Through a comprehensive analysis of current practices and case studies, this article aims to highlight best practices, challenges, and future directions for leveraging AI and ML in the realm of hybrid cloud security. Ultimately, this study underscores the critical role of AI and ML technologies in fortifying hybrid cloud systems against a wide array of cyber threats, offering insights into how these advanced tools can be harnessed to create more secure and resilient digital infrastructures.

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