Ransomware has rapidly evolved into one of the most significant threats to organizational cybersecurity, with its ability to disrupt operations and cause extensive financial and data losses. In response to this escalating challenge, the application of reinforcement learning in Blue Team operations introduces a novel approach to cybersecurity, characterized by adaptive, dynamic defense mechanisms that continuously learn and evolve alongside the threat landscape. The research presented in this article explores the development and evaluation of a reinforcement learning agent designed to enhance the detection, response, and mitigation of ransomware attacks within a simulated network environment. Comprehensive simulations demonstrated the agent's superior performance in comparison to traditional security measures, with significant improvements in detection time, response accuracy, and network resilience. The findings highlight the potential of reinforcement learning to transform Blue Team strategies, enabling a proactive defense posture that anticipates and neutralizes threats more effectively. Furthermore, the agent's adaptability across various network configurations and its robustness in handling multiple ransomware variants demonstrate its versatility and utility as a key component in modern cybersecurity frameworks.