paperJuly 2026Unreviewed

Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls

Saroj Gopali, Bipin Chhetri, Deepika Giri, Sima Siami-Namini, Akbar Siami Namin

Abstract

Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT environments. While Large Language Models (LLMs) have strong semantic reasoning abilities to assist in decision support, their hallucinatory nature presents unacceptable safety liabilities for closed-loop control. This paper introduces a neuro-agentic control framework, a novel architecture that couples an LLM-based p

Cite

@misc{gopali2026neuroagentic,
  title = {{Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls}},
  author = {Saroj Gopali and Bipin Chhetri and Deepika Giri and Sima Siami-Namini and Akbar Siami Namin},
  year = {2026},
  month = jul,
  eprint = {2607.09076},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.09076}
}