Autonomous Policies Using LLMs to Sweep Up Misconfigs

Security policies and engines today are complex ecosystems - not just defined by rigid, structured configurations but also by many layers of free-text and meta-data that tell the real story behind a rule or a configuration. Traditional configuration analysis can flag obvious issues, yet it often overlooks the contextual “crumbs” left behind by temporary fixes, ad-hoc tweaks, or legacy testing exercises. These remnants, much like orphaned data in a software system, can create unexpected vulnerabilities that attackers are eager to exploit. In this talk, I introduce a pioneering AI-driven strategy inspired by the concept of a software Garbage Collector. Just as a Garbage Collector continuously cleans up memory leaks, our AI agent proactively sifts through the meta-configuration, analyzing unstructured fields—free-text names, descriptions, annotations, and even multilingual notes—to identify contextual misconfigurations before they evolve into high-risk liabilities. Advanced NLP is key: semantic analysis decodes the intent behind free-text entries, while contextual classification organizes policy components by interpreting diverse cues. This integrated approach detects anomalies that traditional tools may miss, shifting policy management from static settings to dynamic, context-rich narratives that reveal hidden security gaps.