Providing model access
Processing needed to provide the requested model service is defined by the applicable service terms. Model access alone does not authorize workflow contribution for training or external licensing.
DATA CONTROLS
Your workflows.
Clearly defined permissions.
Model access, private data production, and external licensing have different purposes. We agree on the scope of each before using your data for that purpose.
Discuss your requirements01 / THREE DISTINCT PURPOSES
Permission for one use
does not imply permission for all.
02 / FROM RAW DATA TO APPROVED PRODUCTS
Handle sensitive context.
Review the result.
Names are only part of the picture. Source code, customer content, business rules, and surrounding context may also be sensitive.
Set collection boundaries.
Agree on eligible workflows, fields, and exclusions before collection for data production.
Transform the content.
Remove or transform sensitive details and reconstruct tasks or environments where needed to preserve the learning signal.
Review the proposed deliverable.
Check the processed dataset or environment against the agreed sensitivity and quality criteria before external delivery.
Limit delivery to the approved use.
Define the data product, permitted recipients or recipient categories, and allowed training or evaluation purposes.
03 / PROJECT AGREEMENTS
Make the boundaries
specific to your project.
Raw data and processed data products need distinct handling rules.
We agree on who can access raw material, what can be retained, and how retention, deletion, and changes to permissions are handled. These terms are defined for each engagement.
Creating data for your own AI does not automatically authorize external licensing. Any proposed external use needs its own explicit authorization.
Talk through your data requirements