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Managing open-weight model risk in enterprise deployments
microsoft.comarticle

Managing open-weight model risk in enterprise deployments

Microsoft blog post introducing "Risk Management for Open-Weight Models in Enterprise", a joint Microsoft–NVIDIA paper. It proposes a shared risk taxonomy (Cybersecurity, Data Protection, Content, Human-AI Interaction, Bias) grounded in ISO standards and the NIST AI RMF. The paper maps evaluations to risks as evidence and argues that risk depends on the model, use case, deployment and safeguards, not on weight openness alone. It also covers how responsibility for controls shifts when enterprises self-host models, and points to a joint repository of evaluations. The first paper focuses on the model layer; runtime, harness and application layers are planned for later.

TL;DR Microsoft and NVIDIA publish a five-category risk taxonomy and evaluation-mapping approach for managing open-weight model risk in enterprises, aligned to NIST AI RMF and ISO.
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Organisations named here

  • ISO

Technologies named here

  • Open-weight AI models in enterprise deployments
  • Open-weight models used through managed platforms
  • Joint Microsoft-NVIDIA repository
  • NIST AI Risk Management Framework
  • International Organization for Standardization standards
  • NVIDIA
  • NIST AI RMF
  • Microsoft Foundry

Techniques named here

  • Risk taxonomy with five categories
  • Automated red teaming
  • Assessing risk by deployment context