Agentic AI-Driven Intrusion Detection and Autonomous Incident Response

Abstract: Limitations associated with the traditional IDS systems which are based on pre-defined rules and human intervention have been highlighted due to the quick rise of the advanced cyber-attacks. Goal-driven software agents which can constantly watch the events in the network, recognize malicious activities, plan for the defence plans and take intelligent reaction actions independent of human intervention are called as agentic artificial intelligence (AI). This novel Agentic AI-Driven Intrusion Detection and Autonomous Incident Response Framework proposed in this paper is comprised of the explainable AI, machine learning, reinforcement learning, threat intelligence and autonomous decision making. In real-time, the proposed framework can detect anomalies, learn adaptive policies, automate threat containment and recover incidents having minimal false positives and reaction time. According to the experimental results, the proposed approach outperforms traditional IDS systems in terms of detection accuracy, precision, response time and resilience. A proactive and intelligent cybersecurity architecture is provided by the framework for the enterprise networks, cloud environments, Internet of Things ecosystem and vital infrastructure that needs continuous protection against new cyber threats.

Keywords: Agentic AI, Intrusion Detection System, Autonomous Incident Response, Cybersecurity, Threat Intelligence, Reinforcement Learning, Explainable AI, Network Security.