SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center

Uday Vallabhaneni, Cassie L. Cagwin, David J. Wild|September 3, 2026arXiv

Key Takeaway

Offloading structured reasoning (like network topology) from LLMs to specialized models (graph encoders + RL policies) makes AI security agents faster, more reliable, and deployable at enterprise scale.

Summary

This paper presents Sentinel-RL, a system that helps LLM-based security analysts by splitting their work: a graph neural network handles the complex authentication network topology, while reinforcement learning constrains the agent's actions to valid security moves, and the LLM focuses on explaining decisions to humans.

agents safety applications

Key Terms

agentic-language-model policy-gradient graph-neural-network constrained-decoding critic-based-filtering