Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance
For telecom network operators, this work addresses the bottleneck of manual security compliance in complex RANs, but it is an early-stage proposal with no quantitative results.
The paper proposes an LLM-based agentic AI framework with RAG to automate security compliance in 6G RANs, demonstrating in a case study that it can assess configurations, generate justifications, and propose remediation.
Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks (RANs) opens up numerous opportunities for applying these systems. Securing the RAN is a key area, particularly through automating the security compliance process, as traditional methods often struggle to keep pace with evolving specifications and real-time changes. In this article, we propose a framework that leverages LLM-based AI agents integrated with a retrieval-augmented generation (RAG) pipeline to enable intelligent and autonomous enforcement of security compliance. An initial case study demonstrates how an agent can assess configuration files for compliance with O-RAN Alliance and 3GPP standards, generate explainable justifications, and propose automated remediation if needed. We also highlight key challenges such as model hallucinations and vendor inconsistencies, along with considerations like agent security, transparency, and system trust. Finally, we outline future directions, emphasizing the need for telecom-specific LLMs and standardized evaluation frameworks.