Real-Time Intelligent Database Security Framework Using AI Agents and Deep Neural Networks in Cloud Environments

Abstract

The rapid growth of cloud-based database systems has introduced significant security challenges due to increasing cyber threats and unauthorized access attempts. This paper proposes a real-time intelligent database security framework that integrates AI agents and deep neural networks to enhance threat detection and response. The system continuously monitors database activities and analyzes user behavior to identify anomalies in real time. Deep neural networks are utilized to detect complex attack patterns, while AI agents enable automated and adaptive decision-making for immediate threat mitigation. The proposed framework improves detection accuracy, reduces false positives, and ensures efficient handling of large-scale cloud data. Furthermore, it supports dynamic and scalable environments, making it suitable for modern cloud infrastructures. Experimental evaluation demonstrates that the framework outperforms traditional security methods in terms of accuracy, response time, and reliability, providing a robust solution for securing cloud-based database systems.