Optimal security and pricing strategies for AI cloud service providers: Balancing effort and price discounts across public, private, and hybrid AI cloud models

Abstract

In this study, we focus on cloud security and pricing as key factors that influence end users' choices. We develop an analytical model to examine how an artificial intelligence (AI) cloud service provider optimally sets security investments and price discounts, considering users' different views of public, private, and hybrid AI cloud services. Our results show how end users' characteristics and market dynamics affect these strategies and reveal the balance providers must strike between improving user experiences, capturing market share, and maximizing profits. We find that the provider's control over AI cloud security—along with security costs for both the provider and users, as well as users' potential security losses—plays a critical role in shaping effective strategies. In low-security-loss environments, end users gain more from choosing public AI cloud solutions. However, private AI cloud solutions become more favorable if the provider's security cost coefficient falls within certain limits. In the hybrid AI cloud scenario, the model becomes more complex. Under some conditions, security investment and price discounts act as complementary strategies; in others, they substitute for one another. We also analyze how these choices affect market share and profitability, and find that in some cases, security investments can outperform price discounts.