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arXiv · 2604.20134

AgentSOC: A Multi-Layer Agentic AI Framework for Security Operations Automation

Joyjit Roy·Samaresh Kumar Singh
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Abstract

Security Operations Centers (SOCs) increasingly encounter difficulties in correlating heterogeneous alerts, interpreting multi-stage attack progressions, and selecting safe and effective response actions. This study introduces AgentSOC, a multi-layered agentic AI framework that enhances SOC automation by integrating perception, anticipatory reasoning, and risk-based action planning. The proposed architecture consolidates several layers of abstraction to provide a single operational loop to support normalizing alerts, enriching context, generating hypotheses, validating structural feasibility, and executing policy-compliant responses. Conceptually evaluated within a large enterprise environment, AgentSOC improves triage consistency, anticipates attackers' intentions, and provides recommended containment options that are both operationally feasible and well-balanced between security efficacy and operational impact. The results suggest that hybrid agentic reasoning has the potential to serve as a foundation for developing adaptive, safer SOC automation in large enterprises. Additionally, a minimal Proof-Of-Concept (POC) demonstration using LANL authentication data demonstrated the feasibility of the proposed architecture.

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Joyjit Roy, Samaresh Kumar Singh. 2026-04-22. AgentSOC: A Multi-Layer Agentic AI Framework for Security Operations Automation. https://doi.org/10.1109/icaic67076.2026.11395783

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