Imagine a world where AI could automatically pinpoint hidden security flaws in software, before hackers even have a chance. That's the promise of a new research project called IRIS, which combines the power of large language models (LLMs) with traditional code analysis techniques. Software vulnerabilities are a constant threat, with thousands reported each year. Existing tools often miss critical bugs or generate so many false alarms that developers struggle to find the real threats. IRIS tackles these challenges head-on. It uses LLMs to understand the context of code and identify potential security weaknesses that traditional tools might overlook. Think of it like giving a security expert an AI assistant to help them analyze massive codebases. The researchers tested IRIS on a challenging dataset of real-world Java projects and found it significantly outperformed existing tools. Not only did IRIS find more known vulnerabilities, but it also uncovered previously unknown bugs, highlighting its potential to proactively improve software security. While LLMs alone aren't perfect at reasoning about code, combining them with static analysis creates a powerful synergy. LLMs bring their vast knowledge of code patterns and potential vulnerabilities, while static analysis provides a structured way to navigate and understand the code's logic. This research opens exciting new doors for AI-powered security analysis. Imagine a future where security vulnerabilities are caught automatically during development, making software safer and more secure for everyone. While challenges remain, such as the computational cost of running LLMs and the need for further refinement, IRIS represents a significant step towards a future where AI plays a crucial role in protecting our digital world.