This white paper presents a structural coverage analysis of the Argus governance architecture against the OWASP Top 10 for LLM Applications (2025). Argus is an inference-governance middleware platform built on the Universal Intelligence Architecture™ (UIA) and operates at the orchestration layer between an application and a probabilistic inference engine. The paper explains how Argus governs model behavior before inference through threat classification, during inference through provider-aware sequential escalation, and after inference through behavioral conformity certification and release control. It maps these mechanisms to the OWASP risk categories with an explicit distinction between strong coverage, partial coverage, and categories that fall outside Argus’s intended architectural scope. The analysis argues that Argus is not a full-stack AI security system, but a deterministic governance layer for inference-time risks such as prompt injection, improper output handling, system prompt leakage, and unbounded consumption. It also introduces a structural validation methodology based on threat-family, stress-intensity, and model-level testing rather than raw adversarial prompt volume.