"Secure AI Infrastructure Design for Encrypted Models"

Abstract

The advancement of artificial intelligence (AI) has driven the need for secure AI infrastructure to protect sensitive data and proprietary models. This paper presents a comprehensive design for secure AI infrastructure that focuses on encrypted models, ensuring data confidentiality and integrity throughout the AI lifecycle. Our approach integrates advanced encryption techniques, secure multi-party computation, and homomorphic encryption to safeguard model training, deployment, and inference processes. We outline the architecture, key components, and security protocols necessary for building a resilient AI system capable of withstanding various cyber threats. Additionally, we address performance considerations and the trade-offs between security and efficiency. The proposed design is validated through a series of experiments demonstrating its effectiveness in protecting AI models without significantly impacting their performance. This work contributes to the field by providing a robust framework for developing secure AI systems, paving the way for safer and more reliable AI applications across various industries.