TrustX ARC Framework Classifies Agentic AI System Risks
▶ The 2-minute explainer
Key takeaways
- The TrustX ARC Framework helps classify and govern risks of agentic AI systems.
- It uses a twelve-dimension rubric and integrates existing AI governance models.
- The framework produces a three-tier governance output with control recommendations.
- It is designed for AI governance practitioners, risk officers, developers, and regulators.
Who benefits
Summary
The TrustX Agent Risk Classification (ARC) Framework provides a structured method for risk-tiering internally created agentic AI systems, addressing the gap in general-purpose AI risk frameworks. It uses a twelve-dimension scoring rubric, a GPA + IAT classification model, and a five-level autonomy framework to produce a three-tier governance output with control recommendations.
Why it matters
For organizations deploying or developing agentic AI, this framework provides a critical tool for systematically assessing and managing risks, ensuring responsible AI adoption and compliance with emerging governance standards.
How to implement this in your domain
- 1Download and review the interactive TrustX ARC Framework to understand its components and methodology.
- 2Identify all agentic AI systems currently in use or under development within your organization.
- 3Apply the twelve-dimension scoring rubric to each identified agentic AI system to quantify its risk profile.
- 4Utilize the framework's classification models to assign a risk tier and implement the corresponding control recommendations.
- 5Establish a regular review process to re-evaluate agentic AI systems using ARC as they evolve or new ones are introduced.
Original post by Hannah M. Liu, Rhea Saxena, Shiv Asthana
"arXiv:2607.09586v1 Announce Type: new Abstract: 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 Clas…"
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Originally posted by Hannah M. Liu, Rhea Saxena, Shiv Asthana on X · view source
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