The growing AI landscape is giving enterprises more choice in how they build, customize, and deploy increasingly capable models. Open-weight models are an important part of that landscape. They give enterprises access to model weights and greater flexibility to inspect, evaluate, modify, and run models within their own environments.
While that flexibility can be valuable, it also affects how enterprises evaluate models, where they implement safeguards, and who is responsible for maintaining those safeguards over time.
As enterprises choose which models to deploy, the relevant question is broader than whether a model is open-weight or closed-weight. Enterprises need to understand what they are using the model for, what could go wrong in that context, and what evidence and controls they need to manage risks.
Today, Microsoft and NVIDIA are publishing Risk Management for Open-Weight Models in Enterprise, the first in a planned series of joint resources intended to help enterprises address those questions. The paper introduces a shared risk taxonomy and an approach to managing model risks grounded in established standards and frameworks, including International Organization for Standardization (ISO) standards and the National Institute of Standards and Technology (NIST) AI Risk Management Framework.
The paper reflects a core idea: weight openness alone does not determine an AI system’s risk. Risk depends on the model, use case, deployment, and safeguards.
Understanding risk in the deployment context
Open- and closed-weight models can share many of the same underlying risks, but how those risks materialize depends on the deployment. A model used to help an employee draft a document presents different considerations than the same model used in a customer-facing workflow, just as a standalone inference endpoint differs from a system connected to external data or tools.