TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them. In this paper, we introduce the TrustX Agent Risk Classification Framework, a structured, repeatable instrument that can be applied to seven types of agentic AI systems and is grounded in foundational pre-existing AI governance frameworks. At the core of the framework is a twelve-dimension scoring rubric that robustly quantifies the risk. This rubric is combined with other components, such as the GPA + IAT classificati
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- PossiblePossibly related (embedding) · 59%The foundational elements of AI architecture that IT leaders need to scale →
- PossiblePossibly related (embedding) · 56%agentscope-ai/agentscope →
- PossiblePossibly related (embedding) · 56%Securing the future of AI agents →
- PossiblePossibly related (embedding) · 56%The real cost, security, and culture problems behind enterprise AI agents →
- PossiblePossibly related (embedding) · 54%Agent confidence on the technical frontier →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “TrustX Agent Risk Classification Framework (ARC): Risk-Tieri” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Hannah M. Liu →
“TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems”
- LinkedLinked via arxiv author · 85%Rhea Saxena →
“TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems”