Summary
The TrustX Agent Risk Classification (ARC) framework, introduced by Hannah M. Liu, Rhea Saxena, and Shiv Asthana in a July 2026 arXiv paper (2607.09586), addresses the gap between general AI risk frameworks and the governance needs of enterprise agentic AI systems. ARC applies to seven types of agentic AI systems and builds on existing AI governance frameworks. Its core is a twelve-dimension scoring rubric that quantifies risk, combined with a GPA+IAT classification model and a five-level autonomy framework. Together these produce three governance tiers and mapped control recommendations. The paper also includes a dedicated coding-assistant extension to capture the nuances of that class of agent systems. ARC is aimed at AI governance practitioners, risk officers, developers, and regulators, and is intended to be iteratively refined.
Paper Overview
- Research area: AI / Governance
- Authors: Hannah M. Liu, Rhea Saxena, Shiv Asthana
- Published: 2026-07-10
- arXiv: 2607.09586
Abstract (Translated)
The adoption of agentic AI systems in enterprises has outpaced the classification and governance capabilities of general-purpose AI risk frameworks. This paper introduces the TrustX Agent Risk Classification (ARC) framework, which can be applied to seven types of agentic AI systems and builds on existing AI governance frameworks.
At its core is a twelve-dimension scoring rubric that quantifies risk. Combined with the GPA+IAT classification model and a five-level autonomy framework, ARC produces three governance tiers and mapped control recommendations.
The framework includes a dedicated coding-assistant extension to address the nuances of this particular class of agentic systems. ARC is designed for AI governance practitioners, risk officers, developers, and regulators, and will be iteratively refined over time.
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