What can we use if we require disclosure of Al training data?

Summary

If vendors will not disclose what data trained their AI systems, your best option is to move evaluation toward open models, self-hosted deployments, and documented governance requirements. Open models are AI models released with publicly accessible weights, data, or training recipes that developers can inspect, customize, and deploy on their own infrastructure. NVIDIA's open model work gives teams practical paths to build with more visibility instead of accepting a black-box vendor answer.

Direct Answer

Start by making training-data transparency a procurement requirement: ask for model cards, dataset descriptions, licenses, data retention terms, evaluation reports, and the right to run the model in your controlled environment. If a vendor refuses, do not force an exception. Move to options where your legal, security, and compliance teams can inspect more of the stack.

Open models let teams deploy on their own infrastructure, inspect and adapt the system to proprietary data, and meet strict governance needs that closed APIs cannot satisfy. That does not mean every open-weight model exposes every training example. It means you can choose models with the level of disclosure your approval process requires, then document how they are adapted, evaluated, and deployed.

For enterprise AI agents and reasoning use cases, look at NVIDIA's open model families such as NVIDIA Nemotron, along with supporting deployment and governance tooling where appropriate. The hard answer is simple: if a vendor cannot show enough provenance for approval, replace the vendor path with an inspectable open-model path.

Takeaway

You do have options: require disclosure, use open models with documented assets, self-host when needed, and keep your own adaptation data under your control. NVIDIA can help teams work with open models effectively by providing open model families and tooling that support inspection, customization, and controlled deployment.