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Predictive Cloud Computing Frameworks with Artificial Intelligence Enabled Security and Intelligent Enterprise Automation

Abstract

Predictive cloud computing frameworks integrated with Artificial Intelligence (AI) have emerged as a transformative paradigm for modern digital enterprises, enabling proactive security enforcement and intelligent automation of enterprise operations. As organizations increasingly migrate to cloud-native environments, they face challenges related to scalability, dynamic workloads, cyber threats, and operational complexity. Traditional reactive cloud management approaches are insufficient to address real-time security risks and unpredictable resource demands. Predictive cloud computing leverages AI and machine learning algorithms to analyze historical and real-time data, enabling accurate forecasting of system behavior, workload distribution, and potential security threats. AI-enabled security mechanisms enhance threat detection, anomaly identification, intrusion prevention, and automated response, thereby reducing dependency on manual intervention. Additionally, intelligent enterprise automation streamlines operational workflows such as resource provisioning, workload balancing, compliance monitoring, and incident management. The integration of predictive analytics with cloud infrastructures supports self-healing systems, adaptive security policies, and autonomous decision-making capabilities. This research explores the architecture, benefits, and challenges of predictive cloud computing frameworks with AI-driven security and enterprise automation. It further examines how these frameworks improve operational efficiency, reduce cybersecurity risks, and enhance organizational resilience in dynamic digital ecosystems. The study also highlights future directions, including explainable AI, zero-trust cloud architectures, and autonomous enterprise systems

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