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DATA CONTROLS

Your workflows.
Clearly defined permissions.

Operating productivity tools, training custom models, and licensing data to others have different purposes. We agree on the scope of each before using your private data for that purpose.

Discuss your requirements
YOUR CHOICE

Your private AI project does not authorize external data licensing.

01 / THREE DISTINCT PURPOSES

Permission for one use
does not imply permission for all.

01

Operating your productivity tools

We define what data your tools need to process for the agreed workflow. Using a productivity tool does not by itself authorize separate model training or external data licensing.

SERVICE TERMS
02

Preparing data and training your models

Your project agreement defines which private data may be organized and processed, which models may be trained, and how the resulting datasets and models may be used within your organization.

SEPARATELY AGREED
03

Licensing data to others

External delivery is limited to approved data products and explicitly authorized uses. This applies to frontier labs and other enterprise AI teams alike.

EXPLICIT AUTHORIZATION

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.

  1. Set collection boundaries.

    Agree on eligible sources, workflows, fields, and exclusions before processing private data for the project.

  2. Transform the content.

    Remove or transform sensitive details and reconstruct tasks or environments where needed to preserve the learning signal.

  3. Review the proposed deliverable.

    Check the processed dataset or environment against the agreed sensitivity and quality criteria before external delivery.

  4. 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.

Preparing private data, training custom models, and building internal tools do not automatically authorize external data licensing. Any proposed external use needs its own explicit authorization.

Talk through your data requirements