More than "plausible nonsense": A rigorous eval for ADÉ, our security coding agent

The recent discourse in AI circles highlights the limitations of generic evaluations, emphasizing the need for tailored frameworks that align with specific problem-solving goals. In cybersecurity, there is skepticism toward AI tools, especially in Large Language Model (LLM) code generation, due to concerns about generating plausible but flawed detection rules. To address this, a three-pillar framework focusing on Detection Accuracy, Robustness, and Economic Cost has been developed to objectively evaluate AI-generated detection rules. This framework assesses whether rules effectively stop unique attacks, resist evasion, and are economically viable to produce and maintain. By comparing AI-generated rules to human-crafted ones, the framework demonstrates that AI can quickly create high-precision rules, enhancing detection capabilities while maintaining economic efficiency. The framework's adaptability ensures continuous improvement, integrating adversarial testing to teach AI agents to generalize detection rules effectively. This approach not only measures performance but also strengthens machine learning models over time, fostering customer trust through transparency and realistic evaluations. The full evaluation framework is detailed in a paper available on ArXiv, and its findings will be presented at the Conference on Applied Machine Learning in Information Security (CAMLIS).