Secure Deployment of Generative AI in Cloud Environments

Abstract

Generative Artificial Intelligence (GenAI) models have become widely adopted through cloud computing platforms such as AWS, Microsoft Azure, and Google Cloud. Models such as ChatGPT and Gemini are transforming industries ranging from education and healthcare to enterprises and public services. Cloud environments provide scalability, cost efficiency, and ease of deployment; however, they also introduce complex privacy and security challenges. GenAI models are susceptible to sophisticated attacks such as prompt injection, model inversion, unauthorized access through insecure APIs, and data leakage. This paper examines security and privacy risks in cloud-hosted GenAI systems across data, training, deployment, and interface stages. It reviews mitigations like AI firewalls, differential privacy, and secure enclaves, and explores secure and trustworthy GenAI deployments.