Sovereign AI for 6G: Towards the Future of AI-Native Networks

2025-09-08

AI Summary

To address data sovereignty, security compliance, and explainability risks arising from external AI models in the evolution toward AI-native 6G networks, this paper proposes “Sovereign AI”—the first end-to-end AI governance framework specifically designed for 6G. The framework integrates architectural design, operational management, and governance mechanisms to enable policy-aligned intelligent control and trustworthy federated learning. Leveraging the O-RAN architecture, Sovereign AI–driven xApps/rApps are deployed in near-real-time and non-real-time RICs, incorporating secure model updating and trusted infrastructure technologies. Experimental validation demonstrates that the framework enables efficient, interpretable, and sustainably evolving 6G network intelligence while ensuring national security requirements and regulatory compliance. This work establishes Sovereign AI as a foundational pillar for future intelligent networks, bridging AI innovation with sovereign control, accountability, and trustworthiness in 6G ecosystems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Intelligent Robots: Embodied AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The advent of Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and Large Telecom Models (LTM) significantly reshapes mobile networks, especially as the telecom industry transitions from 5G's cloud-centric to AI-native 6G architectures. This transition unlocks unprecedented capabilities in real-time automation, semantic networking, and autonomous service orchestration. However, it introduces critical risks related to data sovereignty, security, explainability, and regulatory compliance especially when AI models are trained, deployed, or governed externally. This paper introduces the concept of `Sovereign AI' as a strategic imperative for 6G, proposing architectural, operational, and governance frameworks that enable national or operator-level control over AI development, deployment, and life-cycle management. Focusing on O-RAN architecture, we explore how sovereign AI-based xApps and rApps can be deployed Near-RT and Non-RT RICs to ensure policy-aligned control, secure model updates, and federated learning across trusted infrastructure. We analyse global strategies, technical enablers, and challenges across safety, talent, and model governance. Our findings underscore that Sovereign AI is not just a regulatory necessity but a foundational pillar for secure, resilient, and ethically-aligned 6G networks.

Problem

• Addressing data sovereignty and security risks in AI-native 6G networks
• Ensuring regulatory compliance and explainability in externally governed AI models
• Enabling national or operator-level control over AI development and deployment

Innovation

• Sovereign AI framework for national control
• Deploying xApps and rApps in O-RAN
• Federated learning across trusted infrastructure