Networking Technology: Security - Securing AI Using Zero Trust Principles

Securing AI Using Zero Trust Principles Strategic Guidance for Defending AI Systems in a Rapidly Evolving Threat Landscape Artificial intelligence is reshaping industries, driving innovation in critical sectors such as healthcare, finance, energy, and government. Yet, as organizations integrate AI into business operations, they inherit new risks, many of which conventional security models fail to address. Adversaries are weaponizing AI to automate reconnaissance, bypass defenses, and exploit vulnerable systems. The solution is not more trust, but less. Zero Trust offers a foundational paradigm shift: no identity, device, system, or interaction is inherently trusted. Security must be continuously enforced, context-aware, and resilient by design. This book demonstrates how Zero Trust, when strategically applied to AI environments, enables organizations to secure data pipelines, mitigate emergent threats, and maintain control over evolving digital ecosystems. Key insights include AI Through a Security Lens: Demystifies machine learning, generative AI, and large language models with a focus on operational and business impact. Zero Trust Foundations: Provides a historical and architectural overview of Zero Trust, including Cisco’s Five Zero Trust Categories. Security by Design for AI: Offers guidance on protecting AI development workflows, from data ingestion and model training to inference and deployment. Threat Mitigation Strategies: Addresses adversarial AI, data poisoning, shadow AI, and insider misuse through identity enforcement, segmentation, and telemetry. Strategic Execution: Maps Zero Trust principles to regulatory frameworks including NIST AI RMF, EU AI Act, DORA, and ISO 27001, and provides actionable templates for running successful Zero Trust Segmentation…