Enhancing Secure Intent-Based Networking with an Agentic AI: The EU Project MARE Approach

TL;DR

This paper proposes an enhanced, security-focused intent-based networking framework integrating agentic AI and LLMs, aiming to improve multi-domain and multi-vendor network security and management.

Contribution

It introduces a hierarchical multi-agent architecture for intent-based systems that incorporates LLMs and external security knowledge bases, extending prior MARE project work.

Findings

Proposes a multi-agent, multi-vendor architecture for secure IBN.

Integrates LLMs for intent processing and external security knowledge bases.

Extends security architecture beyond the original MARE project.

Abstract

In the EU project MARE, a novel plane was proposed and used in combination with intent-based networking (IBN), allowing the operator to focus on what, rather than on how. Recently, LLMs have been successfully employed to translate the high-level intents into low-level actions. The open challenge is to understand how IBN can be effectively enhanced with LLM and the emerging agentic AI for security purposes. Enhancing IBN with an agentic AI paradigm introduces significant challenges that existing solutions do not fully address. This paper proposes an enhanced IBN framework with a strong security focus toward agentic AI. We address the architectural and security requirements for a multi-agent intent-based system (IBS) architecture, including a multi-domain IBN. We propose a hierarchical multi-agent and multi-vendor architecture that can also be applied more broadly in 6G architectures and beyond, beyond the security architecture proposed in MARE. The architecture incorporates an interactive intent-processing pipeline using LLMs, and it also allows the IBS to connect to external security knowledge bases, such as MITRE ATT\&CK, MITRE FiGHT, and NIST.