The increasing sophistication of ransomware attacks poses significant challenges for organizations attempting to secure their digital infrastructures. Simulating such attacks requires innovative solutions that can adapt to rapidly evolving threats and provide comprehensive testing of security measures. A novel approach utilizing autonomous AI systems for ransomware simulations offers unprecedented scalability and adaptability, significantly enhancing the traditional red teaming process. This paper explores how AI-driven attack simulations generate novel, previously unseen ransomware vectors, evaluating their success rates, stealth capabilities, and effectiveness against various defensive countermeasures. Through a modular architecture, the framework described integrates reinforcement learning to continuously refine attack strategies, ensuring that it remains effective in both simple and complex network environments. The results demonstrate that AI-generated simulations not only match but often surpass the capabilities of traditional red teaming methods, especially in terms of novelty and detection evasion. Moreover, the scalability of this approach allows for continuous, large-scale testing without requiring significant human oversight, making it an ideal solution for organizations aiming to stay ahead of emerging ransomware tactics. Future work could focus on refining the AI models further and expanding their defensive applications to create a fully integrated cybersecurity solution.