Separate AI training terms from data-sharing decisions
A model-training statement does not decide whether an employee may send a document to a service. Review disclosure, processing, access and retention separately, using evidence that applies to the actual product and account.
For Privacy reviewers, security teams and application sponsors
A team cites no-training terms to justify a file upload
A project owner wants to send internal interview notes to an assistant. They have found a provider statement about training and assume that it resolves the entire sensitive-data review.
What you are working with
- The exact product, account arrangement and relevant provider statement.
- The information needed for the task and its owner.
- Evidence about applicable processing, retention, access and connected services.
A safer approach
- Record the training commitment within its actual scope.
- Review the proposed information transfer as a separate decision.
- Minimize the input and use only a reviewed submission route.
Expected outcome: The approval explains why this task and input are acceptable, while accurately preserving the limits of each provider commitment.
Work through the procedure
Read the training statement in context
Locate current primary documentation or contractual evidence for the precise product and account. Record any applicable settings or conditions. Avoid transferring a statement from an enterprise service to a personal account, or from an API product to a similarly branded assistant.
Map the proposed disclosure
Identify what leaves the employee's control, why the task needs it and which parties or connected services are involved. Ask the data owner whether the input is appropriate. A commitment about model improvement does not itself provide organizational permission to share client or internal information.
Review the other data-handling questions
Assess processing and storage location, retention, access, connected permissions and deletion options using the applicable evidence. Keep unanswered questions visible. Do not infer that data stays on the device, is never retained or cannot be accessed simply because training use is restricted.
Apply the reviewed input boundary
Minimize the prompt or document and select the approved account and workflow. Test any chosen technical restriction using synthetic content. Record training terms as one part of the decision, then tell employees which data they may submit and where to ask about uncertain cases.
What to check before proceeding
1. The commitment applies to this arrangement
- Ready when
- The evidence names the actual product and account conditions in use.
- If the check fails
- Verify applicability before presenting the commitment as protection.
2. The data owner approves the submission
- Ready when
- Permission covers the reviewed input and business purpose.
- If the check fails
- Use a minimized alternative or pause the proposed disclosure.
3. Other data-handling questions remain separate
- Ready when
- The assessment records processing, retention and access evidence without inferring them from training terms.
- If the check fails
- Return the unresolved questions to the vendor reviewer.
Common mistakes to avoid
- Explaining no training as no transmission, no storage or no possible access.
- Using a security product's regional processing statement to make assumptions about the separate AI provider's data handling.
Evaluate this workflow with Aona
Where Aona can help
On supported workflows, Aona's prompt and file policies can help control the information submitted to an assistant. That is a distinct layer from the provider's commitments about how received information may be used.
What to confirm
Aona does not set a third-party provider's training or retention terms. Review Aona's own data flow and the destination provider's handling separately; neither substitutes for the organization's decision about the input.
Turning this policy into an operational rollout?
Discuss the teams, devices and AI tools in scope, who will own the policy, and which deployment and evidence requirements need to be met before rollout.