Overcoming Security Challenges in Protecting Shared Generative AI Environments
Ensuring Safe AI on Multi-Tenancy. In a recent report, over 65 percent of organizations reported having implemented generative AI in their business processes. However, upon closer inspection, the majority of these applications are either early stage or in conceptual design phases mostly due to optimism bias around the company’s capabilities to deploy them successfully. The gap between concept and production comes from several challenges: data integration issues, legacy system limitations, use case ROI considerations, and Security barriers. In this article, we’ll focus on one critical security aspect – resources in multiple tenants.
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Ensuring Safe AI on Multi-Tenancy
Introduction
Let us begin by outlining the situation in the field: many organizations are riding the generative AI wave. In a recent report, over 65 percent of organizations reported having implemented generative AI in their business processes. However, upon closer inspection, the majority of these said applications are either early stage or in conceptual design phases mostly due to optimism bias around the company’s capabilities to deploy them successfully.
The gap between concept and production comes from several challenges: data integration issues, legacy system limitations, use case ROI considerations, and Security barriers. In this article, we’ll focus on one critical security aspect – resources in multiple tenants.
