Smart Artificial Intelligence Framework for Cloud Centric Systems and Continuous Threat Intelligence Evolution

Abstract

Cloud-centric systems have become the backbone of modern digital infrastructure, enabling scalable, flexible, and cost-efficient computing environments. However, the widespread adoption of cloud technologies has introduced complex cybersecurity challenges, including data breaches, misconfigurations, insider threats, and advanced persistent attacks. This research proposes a smart artificial intelligence (AI) framework designed to enhance security in cloud-centric systems through continuous threat intelligence evolution. The framework integrates machine learning, deep learning, and adaptive analytics to monitor cloud environments, detect anomalies, and predict potential threats in real time. By leveraging dynamic data streams from cloud platforms, the system continuously learns from new attack patterns and updates its threat intelligence models. The proposed approach emphasizes automation, scalability, and proactive defense mechanisms, enabling rapid response to emerging cyber risks. Additionally, the framework incorporates secure data handling, governance, and compliance measures to ensure data integrity and privacy. Experimental evaluation demonstrates improved detection accuracy, reduced response latency, and enhanced adaptability compared to traditional security systems. The study concludes that integrating AI with cloud security infrastructure provides a robust and intelligent solution for safeguarding cloud-based systems against evolving cyber threats.