Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

2025-05-26

AI Summary

Foundation models (FMs) deployed in IoT edge environments face escalating security and privacy risks due to heterogeneous, distributed architectures and untrusted execution contexts. Method: This paper proposes Zero-Trust Foundation Models (ZTFM), a novel paradigm that natively integrates continuous verification, least-privilege enforcement, data confidentiality, and behavioral analytics across the FM lifecycle. ZTFM systematically unifies federated learning, blockchain-based identity management, micro-segmentation, trusted execution environments (TEEs), adversarial training, and secure aggregation. Contribution/Results: We present the first comprehensive theoretical framework for ZTFM, enabling dynamic trust calibration and self-defense capabilities during model training, deployment, and inference. The framework supports decentralized trustworthy AI, explicitly identifies and models ZTFM-specific threats, and significantly enhances security assurance, verifiability, and privacy preservation in resource-constrained, heterogeneous IoT systems.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

Problem

• Integrating zero-trust security into IoT foundation models lifecycle
• Transforming trust-based IoT architectures into resilient ecosystems
• Addressing scalability and security in decentralized IoT intelligence

Innovation

• Integrates zero-trust principles into foundation models
• Uses federated learning and blockchain for security
• Incorporates anomaly detection and adversarial training