The Governance Gap: A Systematic Analysis of Architectural Deficiencies in AI Agent Security Frameworks

The rapid deployment of AI agent systems capable of autonomous action has outpaced the development of governance infrastructure necessary to ensure safe operation. This paper presents a systematic analysis of fourteen critical gaps in current AI agent security frameworks, documented through examination of published standards (OWASP Top 10 for Agentic Applications 2026), industry research, peer-reviewed security literature, and real-world incidents. We find that existing frameworks address authentication, authorization, and content filtering but fail to provide behavioral trust assessment, outcome-based learning, cross-layer propagation, or intent verification at the protocol boundary.