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.
By Anupam Joshi, Tim Finin, Karuna Pande Joshi, Lalana Kagal
arXiv:2608. 14590v1 Announce Type: new Abstract: LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools.
By Pierre Dantas, Lucas Cordeiro, Ehsan Nowroozi, Tihanyi Norbert
arXiv:2609.06543v1 Announce Type: new
Abstract: Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows,...
By Prabhu Raghav, Balamurugan Pandi, Arul Vivek, Shek Mohammed, Sridhar S
arXiv:2510. 20692v3 Announce Type: replace-cross Abstract: Access control policies are reliability-critical configuration artifacts in cloud systems, yet administrators frequently struggle to verify that a policy permits exactly what they intend.
By Adarsh Vatsa, Bethel Hall, William Eiers
arXiv:2609.00015v1 Announce Type: new
Abstract: AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, control...
By Dongsheng Chen, Xiangyu Zhao, Xin Yao, Xuetao Wei
arXiv:2606. 29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies, rather than relying on predefined risk taxonomies.
By Jiacheng Zhang, Haoyu He, Sen Zhang, Shen Wang, Xiaolei Xu, Yuhao Sun, Meng Shen, Feng Liu