Imagine a world where AI could automatically scan complex cloud environments for hidden threats, proactively building defenses before attackers even strike. That's the promise of LLMCloudHunter, a groundbreaking new framework that uses the power of Large Language Models (LLMs) to transform how we approach cloud security. The sheer volume of cyber threat intelligence (CTI) available online is overwhelming, especially for cloud environments. Much of this valuable data sits in unstructured formats like blog posts and security reports, requiring painstaking manual analysis. LLMCloudHunter tackles this challenge head-on. It automatically extracts key information from these unstructured sources, including crucial API calls and Indicators of Compromise (IoCs), and then translates them into powerful detection rules. These rules, formatted in the industry-standard Sigma format, can be directly plugged into security tools like Splunk, effectively automating the threat-hunting process. In tests using real-world cloud threat reports, LLMCloudHunter demonstrated impressive accuracy, achieving a 92% precision rate for identifying threat actors' API calls and a near-perfect 99% for IoCs. Moreover, almost all the generated detection rules were successfully deployed in Splunk, proving their practical value. This innovation marks a significant step towards proactive cloud security. By automating the analysis of unstructured threat intelligence, LLMCloudHunter empowers security teams to anticipate attacks and build stronger defenses. While challenges remain, including the complexities of MITRE ATT&CK TTP identification and the cost of commercial LLMs, the potential of this framework to revolutionize cloud security is clear. Future research will explore enhancements such as on-premise adaptation and automated playbook generation, pushing the boundaries of AI-driven threat hunting.