Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance

TL;DR

This paper introduces an AI agent framework using LLMs and RAG to automate security compliance in 6G RANs, demonstrating initial case study results and discussing future challenges.

Contribution

It proposes a novel AI agent-based framework for autonomous security compliance enforcement in 6G RANs, integrating LLMs with retrieval-augmented generation.

Findings

Agent can assess configuration files for compliance with standards.

Agent can generate explanations and propose automated remediation.

Highlights key challenges like hallucinations and vendor inconsistencies.

Abstract

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.