Securing AI Pipelines with Zero Trust Architecture

Abstract

The integration of Artificial Intelligence (AI) into enterprise systems has introduced new complexities and vulnerabilities, particularly within AI pipelines encompassing data ingestion, model training and deployment. Traditional security models, which often rely on perimeter defenses, are increasingly inadequate in addressing the dynamic and distributed nature of modern AI systems. Zero Trust Architecture (ZTA), emphasizing continuous verification and least-privilege access, presents a robust framework for mitigating risks associated with AI pipelines. Zero Trust has emerged as a modern security paradigm that rejects any assumption of implicit trust, emphasizing continuous verification, least privilege, and rigorous segmentation.While many discussions of Zero Trust focus on technical implementations, this paper explores how Zero Trust principles can serve as a governance model in cybersecurity risk management. It  emphasizes how implementing ZTA is essential for securing AI pipelines in 2025, ensuring data integrity, model protection, and overall system resilience.​We highlight the evolution from “castle-and-moat” defenses to continuous verification frameworks, discuss how Zero Trust principles align with broader governance requirements, and propose strategies for integrating Zero Trust into organizational policies and procedures. By treating Zero Trust as a governance approach rather than a mere technical deployment, organizations can better align security investments, enforce consistency across business units, and instill a culture of minimal trust that fortifies resilience against evolving cyber threats.