Real Time Data Analytics with AI: Improving Security Event Monitoring and Management

Abstract

In an era where cyber threats are increasingly sophisticated and pervasive, the need for real-time data analytics and advanced security event monitoring is more critical than ever. This paper explores the integration of Artificial Intelligence (AI) with real-time data analytics to enhance security event monitoring and management systems. By leveraging machine learning algorithms and big data technologies, the proposed framework aims to provide a comprehensive and proactive approach to cybersecurity. The study focuses on the application of AI techniques, such as anomaly detection, predictive analytics, and automated incident response, to detect and mitigate security threats in real time. The methodology involves collecting and analyzing large volumes of network traffic data and system logs to identify patterns and anomalies indicative of potential security breaches. Key performance metrics, including detection accuracy, false positive rates, response times, and resource utilization, are evaluated to assess the effectiveness of the AI-driven system. Our findings demonstrate that the AI-enhanced system significantly improves the accuracy and speed of threat detection compared to traditional methods. The system achieves a detection accuracy of 94.5%, with a false positive rate of 2.1%, highlighting its reliability and efficiency.