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

Loay Abdelrazek, Leyli Karaçay, Marin Orlic

TL;DR

The paper tackles secure, fine-grained authorization for knowledge-base agents in Intent-based Management (IbM) as networks evolve toward 6G. It introduces a hybrid authorization framework that combines role-, attribute-, and predicate-based reasoning to dynamically generate agent-specific authorization profiles, enforcing least-privilege access to knowledge graphs in real time. A dynamic authorization ontology and declarative inference rules (via Turtle) enable the IMF reasoner to act as both PDP and PEP, applying per-request checks within a zero-trust, multi-vendor environment. This approach enhances security against compromised agents, supports runtime policy updates, and integrates seamlessly with the reasoning loop of knowledge-based agents, while underscoring the need for standardized authorization ontologies for interoperability across standards bodies. Overall, the work offers a scalable, policy-driven solution to secure autonomous IbM operations in next-generation networks.

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.

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

TL;DR

The paper tackles secure, fine-grained authorization for knowledge-base agents in Intent-based Management (IbM) as networks evolve toward 6G. It introduces a hybrid authorization framework that combines role-, attribute-, and predicate-based reasoning to dynamically generate agent-specific authorization profiles, enforcing least-privilege access to knowledge graphs in real time. A dynamic authorization ontology and declarative inference rules (via Turtle) enable the IMF reasoner to act as both PDP and PEP, applying per-request checks within a zero-trust, multi-vendor environment. This approach enhances security against compromised agents, supports runtime policy updates, and integrates seamlessly with the reasoning loop of knowledge-based agents, while underscoring the need for standardized authorization ontologies for interoperability across standards bodies. Overall, the work offers a scalable, policy-driven solution to secure autonomous IbM operations in next-generation networks.

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.
Paper Structure (13 sections, 4 equations, 5 figures)

This paper contains 13 sections, 4 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Knowledge-based Agent
  4. Intent Management Function
  5. Authorization Principles
  6. Related Work
  7. Threat model in Intent-based Management
  8. Agents Authorization Mechanism
  9. Hybrid Authorization Approach
  10. Authorization Flow
  11. Authorization Ontology
  12. Authorization Inference
  13. Conclusion

Figures (5)

  • Figure 1: Intent-based Management System Framework
  • Figure 2: A Hybrid Authorization Approach.
  • Figure 3: Authorization Flow
  • Figure 4: Creating an authorization profile graph for the agent serialized in Turtle
  • Figure 5: Graph representation of a grounding agent authorization profile