Imagine an AI that could predict and prevent cyberattacks on your bank. That's the promise of threat modeling using Large Language Models (LLMs). Traditionally, identifying vulnerabilities in banking systems has been a slow, manual process, prone to human error. Experts meticulously analyze system designs, mapping out potential threats like spoofing or data tampering. But what if an AI could automate this? Researchers have developed ThreatModeling-LLM, a new framework that uses LLMs to analyze banking system designs and automatically identify potential threats, suggesting mitigations based on industry standards like NIST 800-53. This framework operates in three stages: creating a dataset of banking system designs and their vulnerabilities, carefully engineering prompts to guide the LLM's analysis, and fine-tuning the model to understand the nuances of banking security. The results are impressive. ThreatModeling-LLM significantly outperforms existing automated tools, showing a remarkable ability to identify threats and suggest appropriate countermeasures. For example, the accuracy of identifying the correct mitigation codes improved drastically when tested on models like Llama-3.1. While this research focuses on banking, its implications are far-reaching. This approach could revolutionize cybersecurity across various sectors, from healthcare to critical infrastructure, by providing a faster, more accurate, and automated way to identify and mitigate security risks. However, challenges remain. Adapting this technology to other domains requires specialized datasets and further refinement of the models. Furthermore, efficiently scaling this approach to even larger, more complex AI models presents ongoing research questions. But the potential is clear: AI-powered threat modeling could be a game-changer in the fight against cybercrime.