Automating Cyber Threat Response Using Agentic AI and Reinforcement Learning Techniques

Abstract

The essay proposes an agentic artificial intelligence (AI) and reinforcement learning (RL) framework, design and validation approaches to significantly automate response to continued cyber-attacks. First-generation AI tools are analyzed and then variations of them that qualify them as cyber-agents are proposed. A new typology and a 11-dimensional notation framework for them are used. A typology and a 11-dimensional notation framework for nascent, cyber-agentic anti-antagonistic hullsir response tree (BRT)-based AI functions are used and the automatic location of them with an alliance of tabu search (TS) techniques is obtained. On the outcomes derived from the case of the international cyber-attack against the ESB in 2021, the validation of the research is presented, along with potential directions for future work .

An escalating sequence of cyber-attacks have been waged against Ireland and the ESB, attempting to overwhelm the relatively nascent national cyber-security infrastructures. Consequently, an intensified research effort in the country to find new means and tools to enhance its national cybersecurity measures is under way. It would be useful to automate emergency response to cyber-attacks. As a consequence of evolving AI research, it became possible to propose variations of cyber-defender architectures that qualify them as cyber-agents; the simpler bots can be cyber-agents. Up to now there is no comprehensive taxonomy, typology, or notation framework that correctly accounts for the new cyber-agentic AI. A new typology and an 11-dimensional notation framework for them are proposed. Intense work is under way to make it possible to automatically design those cyber-agents. Another typical case is the USA. The American federal government, tired of the excesses of a few multinational rogue corporations, gave those companies an ultimatum about their cyber-violations of American law.