{
  "id": 59,
  "url": "https://arxiv.org/abs/2607.09586v1",
  "title": "TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems",
  "summary": "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 qu",
  "authors": "Hannah M. Liu, Rhea Saxena, Shiv Asthana",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T16:36:44.000Z",
  "fetched_at": "2026-07-14T14:14:15.665Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/59",
  "original_url": "https://arxiv.org/abs/2607.09586v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}