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Deontic Policies for Runtime Governance of Agentic AI Systems

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arXiv:2606. 19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance.

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Authors: Anupam Joshi, Tim Finin, Karuna Pande Joshi, Lalana Kagal
Released: 2026-06-20 (19d ago)
Topic: Related to current project
Relation to my work: Read next because Deontic Policies for Runtime Governance of Agentic AI Systems overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, under, soft, eval, line, control, full, trained. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.19464

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