AI-Driven Cloud Framework for Real-Time Database Management with Cybersecurity and KNN Optimization
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805007Keywords:
AI-driven cloud architectures, Multi-modal deep learning, Life insurance platforms, Smart insurance, Fraud detection, Underwriting automation, Data fusion, Cloud-native AIAbstract
The increasing complexity of modern data systems demands frameworks that combine real-time processing, advanced analytics, and robust cybersecurity. This study proposes an AI-driven cloud framework for real-time database management, integrating K-Nearest Neighbor (KNN) optimization to enhance data retrieval, processing efficiency, and predictive accuracy. By leveraging cloud-native architectures, the framework ensures scalability, low-latency performance, and seamless integration with distributed database environments. The inclusion of advanced cybersecurity mechanisms protects sensitive information against evolving threats, ensuring data integrity and system resilience. Experimental evaluations demonstrate that the proposed framework significantly improves real-time query performance, optimizes resource utilization, and strengthens security posture, highlighting its potential for critical applications in finance, healthcare, and enterprise IT infrastructures. This approach exemplifies the synergy of AI, cloud computing, and machine learning techniques to deliver intelligent, secure, and high-performance database management solutions.
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