Securing Cloud AI Workloads: Protecting Generative AI Models from Adversarial Attacks

Abstract

Generative artificial intelligence models have brought about advancements in fields like healthcare and finance, as well as in autonomous systems; however, they also encounter notable security vulnerabilities, primarily when operating in cloud environments. These AI models can be targeted by attacks that involve altering input data to deceive the system into generating harmful or incorrect results. This study delves into the security issues that AI systems face in cloud setups, explicitly focusing on the dangers posed by adversarial manipulation of data integrity and the challenges of utilizing shared resources within multi-user environments. The text covers methods for defending AI models, like training and defensive distillation, to make them more robust against attacks. It also delves into security measures for the cloud, such as encrypted communications and robust authentication systems to safeguard data integrity. Furthermore, the importance of AI explainability and transparency in uncovering vulnerabilities and building trust is highlighted. The outcomes of security breaches emphasize the importance of having AI systems to avoid impacts on decision-making and broader ethical and societal concerns. The document also discusses research areas such as quantum algorithms and decentralized security structures to tackle evolving risks and safeguard the future of secure AI applications that generate content.