AI-Driven Threat Detection: Leveraging Machine Learning for Real-Time Cybersecurity in Cloud Environments

Abstract

The proliferation of cloud computing has revolutionized business operations across industries, offering unprecedented scalability, flexibility, and cost-efficiency. However, this shift has simultaneously expanded the attack surface for cyber threats, creating complex security challenges that traditional detection methods struggle to address effectively. This research paper explores the integration of artificial intelligence and machine learning technologies in developing robust, real-time threat detection systems specifically designed for cloud environments. Through a comprehensive analysis of current implementations, algorithmic approaches, and performance metrics, this study examines how AI-driven solutions can enhance security postures by detecting both known and emerging threats with greater accuracy and speed than conventional methods. The research further investigates the challenges of implementing such systems, including data quality issues, processing overhead concerns, and the need for continuous learning mechanisms. Three detailed case studies demonstrate practical applications across different cloud deployment models, providing empirical evidence of effectiveness. Finally, the paper proposes a framework for future development that addresses current limitations and leverages emerging technologies to create more resilient security ecosystems. This comprehensive exploration offers valuable insights for security professionals, cloud service providers, and organizations seeking to strengthen their cybersecurity defenses in increasingly complex digital environments.