Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. The idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. These systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. Automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. The risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. Decision systems actively plan responses based on danger recognition and risk assessment results. These responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. This makes it easier for them to spot and stop new cyber threats. The creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battle for digital security.