Intelligent Cloud Data Protection through AI-Driven Streaming Analytics and Predictive Failure Detection
DOI:
https://doi.org/10.15680/IJCTECE.2026.0904014Keywords:
Intelligent cloud data protection, artificial intelligence, streaming analytics, predictive failure detection, machine learning, anomaly detection, cloud security, data integrity, fault tolerance, disaster recoveryAbstract
Cloud computing environments generate continuous streams of operational, transactional, and security-related data that require effective protection against failures, unauthorized access, and service disruptions. Conventional data protection mechanisms often depend on predefined rules, periodic backups, and reactive monitoring, which may be insufficient for identifying emerging threats and predicting infrastructure failures in dynamic cloud environments. This study investigates an intelligent cloud data protection framework that integrates artificial intelligence-driven streaming analytics with predictive failure detection to improve data security, availability, and operational resilience. The proposed framework combines real-time telemetry collection, stream processing, machine learning-based anomaly detection, predictive modeling, and automated recovery recommendations. Streaming analytics examines access patterns, storage performance, data transfer behavior, system logs, and infrastructure metrics to identify suspicious activities and abnormal operating conditions. Predictive models analyze historical and real-time observations to estimate the likelihood of storage failures, service interruptions, and potential data loss. The research adopts a quantitative experimental methodology to compare conventional protection mechanisms with an AI-enhanced approach using detection accuracy, prediction precision, recall, failure prediction lead time, recovery duration, and resource consumption. Additional considerations include encryption, access control, backup integrity, data consistency, and secure recovery procedures. The proposed framework aims to support proactive risk mitigation, reduce recovery delays, and strengthen cloud data protection through continuous intelligence. The study provides a foundation for developing adaptive, scalable, and resilient cloud security systems capable of anticipating operational failures while preserving data confidentiality, integrity, and availability
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