Real-Time Enterprise Data Processing Through AI-Driven Cloud-Edge Intelligence and Autonomous Security Management
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0209002Keywords:
real-time data processing, cloud-edge computing, artificial intelligence, machine learning, autonomous security, enterprise computing, edge intelligence, anomaly detection, cybersecurity, distributed analyticsAbstract
The rapid growth of distributed enterprise applications, Internet of Things (IoT) devices, cloud platforms, and edge computing environments has created an increasing demand for real-time data processing, intelligent decision-making, and adaptive cybersecurity. Traditional centralized cloud architectures often experience latency, bandwidth limitations, and security challenges when processing large volumes of continuously generated enterprise data. This paper proposes an AI-driven cloud-edge intelligence framework that integrates real-time data processing, distributed artificial intelligence, edge analytics, cloud orchestration, and autonomous security management. The proposed approach enables data to be analyzed close to its source while coordinating computationally intensive operations with centralized cloud infrastructure. Artificial intelligence and machine learning models are employed for predictive analytics, anomaly detection, dynamic resource allocation, and automated threat identification. Autonomous security mechanisms continuously monitor workloads, network behavior, access patterns, and data flows to identify potential attacks and initiate adaptive responses with minimal human intervention. The methodology combines architectural analysis, data-flow modeling, AI-based processing, security evaluation, and performance assessment. Key evaluation parameters include latency, throughput, resource utilization, detection accuracy, response time, scalability, and security resilience. The proposed framework aims to establish an intelligent and adaptive enterprise environment capable of supporting low-latency applications while improving operational efficiency and cybersecurity across heterogeneous cloud-edge infrastructures
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