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Explore an innovative AI-driven cybersecurity approach in this 45-minute conference talk that introduces a novel strategy for identifying hidden security vulnerabilities in complex policy ecosystems. Learn how traditional configuration analysis often misses contextual "crumbs" left behind by temporary fixes, ad-hoc tweaks, and legacy testing exercises that can create unexpected attack vectors. Discover how an AI agent, inspired by software garbage collection concepts, proactively analyzes unstructured fields including free-text names, descriptions, annotations, and multilingual notes to detect contextual misconfigurations before they become high-risk liabilities. Understand the role of advanced natural language processing in semantic analysis and contextual classification, enabling the system to decode intent behind free-text entries and organize policy components by interpreting diverse contextual cues. Gain insights into shifting from static policy management to dynamic, context-rich analysis that reveals hidden security gaps missed by conventional tools, transforming how organizations approach security policy validation and misconfiguration detection in modern complex environments.
