Toward a Unified Security and Privacy Framework for AI-Native 6G Networks

2026-07-01

AI Summary

This study addresses the fragmented security and privacy landscape in AI-native 6G networks, which arises from their extreme heterogeneity and the absence of a unified cross-layer coordination mechanism. The work proposes the first comprehensive cross-layer threat taxonomy encompassing infrastructure, network architecture, AI systems, and privacy management, positioning standards interoperability as a core security enabler. Through integrated cross-layer analysis, threat modeling, standards mapping, and a systematic literature review, the paper synthesizes advances in communication, computation, sensing, and AI security to systematically identify threats and align them with corresponding countermeasures. By clarifying critical research gaps, this research lays a foundational framework for building secure, trustworthy, and interoperable AI-native 6G ecosystems.

Abstract

Sixth Generation (6G) communication networks are expected to evolve into AI-native, highly autonomous ecosystems that integrate communication, computing, sensing, and artificial intelligence. While these capabilities enable unprecedented connectivity and intelligent services, they also create a highly heterogeneous security and privacy landscape that cannot be addressed through isolated, technology-specific solutions. This paper presents a comprehensive survey of security and privacy in AI-native 6G networks from a cross-layer perspective. We first examine the fragmentation of existing security and privacy approaches across emerging technologies, network architectures, AI systems, and standardization efforts, motivating the need for a unified security and privacy framework. Building upon this framework, we develop a cross-layer threat taxonomy encompassing infrastructure, network and architectural, AI, privacy, and security management domains, and analyze representative threats across key AI-native 6G technologies. Furthermore, we map these threats to corresponding cross-layer countermeasures, including standards harmonization as a security function, and identify critical research gaps and future priorities for secure, interoperable, and trustworthy AI-native 6G ecosystems. Finally, we discuss future research directions toward realizing secure, privacy-preserving, resilient, and globally interoperable 6G networks. This survey provides researchers, practitioners, and standardization communities with a holistic foundation for the design, evaluation, and deployment of trustworthy AI-native 6G systems.

Problem

• AI-native 6G
• security
• privacy
• unified framework
• cross-layer

Innovation

• AI-native 6G
• unified security framework
• cross-layer threat taxonomy
• privacy-preserving
• standards harmonization