Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents

Abstract

The increasing scale, sophistication, and velocity of cyber attacks has shown that there are fundamental flaws in the current Security Operations Centers (SOCs) that are founded upon manual analysis and hardened rules and ad-hoc procedures. The current paper aims to address these issues with the help of Next-Generation SOC resilience framework, which is built upon autonomous detection and adaptation to incidences or accidents with the help of Cognitive Artificial Intelligence (AI) agents. The proposed system will introduce multi-agent AI structures, where deep learning-based anomaly detection is used, the optimization of responses can be achieved with the reinforcement learning process, and the contextual reasoning is supported with the help of knowledge graphs. Some of the ways include unsupervised and semi-supervised model of learning unknown threats, cognitive agents to correlate the alert messages using heterogeneous data sources, and adaptive playbooks based on feedback to develop. Experimental evaluation is conducted with a simulated environment of SOC on the enterprise, where real-world data, including network traffic and endpoint telemetry and security logs are taken into account. The results indicate that the accuracy of the detection (as much as 18 per cent higher than the base SIEM systems) is much higher, the mean time to detect (MTTD) by 35 per cent, the mean time to respond (MTTR) by 42 per cent. These findings affirm that cognitive AI agents enhance the resilience of SOC by leading to independent decisions, enhancing fatigue of analysts, and boosting the efficiency and scale of incident response efforts.