Agentic AI for Autonomous Cyber Incident Response: A Multi-Agent Framework for Intelligent Threat Mitigation

Abstract: As a result of hidden-run Agentic approach, AI becomes a huge form of protecting computers against any hacker attacks. It enables systems to work autonomously based on goal-oriented reasoning, planning, and implementation. In this paper, we will try to analyses how it is possible to implement agentic AI technology (in a broader sense) to achieve autonomous cyber-attack response. We would like to know how it could help make wise affirmative action an automated one and thus transform its people-dependent nature into a defensible real-time reaction to any attack. According to the architecture proposed, there are many cooperative agents playing separate roles (detect, analyses, decide, respond, and learn). Such a system learns continually by combining reinforcement learning and threat intelligence, coupled with feedback mechanisms, which results in making a computer immune to any advanced cyber- attack and zero-day attack. Many benchmark datasets have been applied, and as a result, this system was proved to significantly outperform other systems in terms of detection accuracy, response time, and automation efficiency, despite being compared with classic/semi-automatic one.

Keywords: Agentic AI, Autonomous Cyber Incident Response, Multi-Agent Systems, Cybersecurity Automation, Reinforcement Learning, Threat Intelligence, SOC Automation, Adaptive Security Systems