TrustX ARC Framework Classifies Agentic AI System Risks
▶ The 2-minute explainer
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.
Who benefits
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.
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…"
View on XPrimary sources
Originally posted by Hannah M. Liu, Rhea Saxena, Shiv Asthana on X · view source
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