Imagine a world where fixing security vulnerabilities isn’t just the domain of tech wizards but a collaborative effort between AI, security teams, and everyday users. This isn't science fiction, it’s the focus of exciting new research exploring how Large Language Models (LLMs) can transform vulnerability remediation. Traditionally, patching security flaws has been a complex, often reactive process. Security professionals scramble to identify and fix vulnerabilities before they're exploited, often with minimal input from the users whose systems are at risk. This research dives into a new approach: a collaborative vulnerability remediation process supported by LLMs. The researchers implemented a three-step study within a large organization, observing how LLMs could help. First, they examined the existing remediation process, which revealed a critical gap—user satisfaction was low. People felt left out of the loop and uninformed. This led to the design of a two-pronged LLM-assisted approach. The first component aimed to boost user engagement by sharing alerts and solutions directly with them. The second equipped security technicians with LLM tools to develop more effective fixes. The results were intriguing. The collaborative approach significantly improved user engagement, particularly for simpler vulnerabilities. Users armed with LLM-generated, user-friendly guidance were more actively involved in fixing their systems. Furthermore, the LLM support helped security technicians address complex issues faster by generating quality preliminary fixes and flagging often-overlooked problems. However, it wasn't all smooth sailing. For simple issues, the generic nature of LLM-generated solutions sometimes increased the remediation time, and some users weren’t thrilled about having new security responsibilities. This study paints a multifaceted picture of LLMs in security. While LLMs show great promise, they are not a silver bullet. The real magic lies in fostering collaboration between humans and AI, empowering users, and building a security-conscious culture. Future research will likely explore fine-tuning LLM-generated solutions, tailoring them to individual system configurations and user expertise, and optimizing the human-AI collaboration model to balance responsibility and efficiency.