ZERO-TRUST ARCHITECTURE FOR AI WORKLOADS: SECURING MACHINE LEARNING OPERATIONS IN CLOUD ENVIRONMENTS
Keywords:
Zero-Trust Architecture, AI Security, Cloud Computing Security, Multi-Tenant Security, Machine Learning OperationsAbstract
This article presents a comprehensive framework for implementing zero-trust security architectures for AI/ML workloads in cloud environments. Drawing from extensive research and enterprise deployments, it addresses the challenges of securing distributed AI operations while maintaining performance and scalability. The article explores core principles including identity-based security controls, secure data pipeline architectures, and multi-tenant considerations. The article demonstrates that organizations adopting zero-trust frameworks experience significant improvements in security posture, operational efficiency, and compliance management. The implementation patterns discussed encompass container security, identity-based access control, and data encryption strategies, providing a holistic approach to securing AI workflows. Special attention is given to monitoring, compliance controls, and best practices for architecture design and operational security.
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