Authorization of Knowledge-base Agents in an Intent-based Management Function

2025-10-22

AI Summary

In 6G intent-driven networks, knowledge-base agents face challenges in achieving secure, fine-grained authorization under dynamic multi-tenant environments. To address this, we propose a context-aware and functionally grounded dynamic access control framework. Our approach innovatively couples agent roles, runtime contextual attributes, and functional semantics to establish a policy-driven, adaptive authorization mechanism. We extend Attribute-Based Access Control (ABAC) with knowledge graph–based modeling, design a context-aware policy engine, and integrate a functional analysis module—enabling real-time, least-privilege, fine-grained authorization. Compared to conventional RBAC, ABAC, and RelBAC models, our framework significantly improves authorization flexibility and security while preserving zero-trust principles. It also enhances the automation efficiency and environmental adaptability of intent-to-configuration translation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Security and Privacy: Authentication, authorization, and access control
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As networks move toward the next-generation 6G, Intent-based Management (IbM) systems are increasingly adopted to simplify and automate network management by translating high-level intents into low-level configurations. Within these systems, agents play a critical role in monitoring current state of the network, gathering data, and enforcing actions across the network to fulfill the intent. However, ensuring secure and fine-grained authorization of agents remains a significant challenge, especially in dynamic and multi-tenant environments. Traditional models such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC) and Relational-Based Access Control (RelBAC) often lack the flexibility to accommodate the evolving context and granularity required by intentbased operations. In this paper, we propose an enhanced authorization framework that integrates contextual and functional attributes with agent roles to achieve dynamic, policy-driven access control. By analyzing agent functionalities, our approach ensures that agents are granted only the minimal necessary privileges towards knowledge graphs.

Problem

• Securing fine-grained authorization for knowledge-base agents in intent-based management
• Addressing inflexibility of traditional access control models in dynamic network environments
• Ensuring minimal necessary privileges for agents operating on knowledge graphs

Innovation

• Integrates contextual attributes with agent roles
• Uses policy-driven access control for dynamic authorization
• Grants minimal necessary privileges on knowledge graphs