Novel Method of Improving Cybersecurity Posture with Real-Time Analytics in Security Operations Centers

Cyberattacks become more widespread and sophisticated, cybersecurity is now at the forefront of concern for many organizations. Security Operations Centers (SOCs) are essential for their ability to monitor, detect, and respond to these threats. The problem is traditional methods are ineffective within SOCs due to large data volumes, complexities of the threat landscape, and slow time-to-response. In this paper, presents a new method for enhancing the cybersecurity landscape of SOC using real-time analytics. Utilizing sophisticated data analysis methods, ML models, and automated decisions systems, we plan to improve the precision of the threat level, minimize the false positives, and speed up the incident response. It continuously monitors data streams in real time from many sources, including network traffic, system logs, and endpoint data to quickly adapt to the emergence of threats. Results from experiments show that our method offers significant accuracy gains in true-positive detection while reducing detection response times and greatly improving utilization rate when compared with traditional SOC operations. This scalable framework can efficiently address the new security concept for SOCs to counter emerging cyber threats, which can positively impact the prevailing cyber security.