Data handling and privacy

Published

April 11, 2025

How does ​ValidMind handle the processing of PII?

​ValidMind does not send any personally identifiable information (PII) through our ValidMind Library Python API.1

Access to the ValidMind Platform is facilitated through AWS PrivateLink, which provides private connectivity between ​ValidMind and your on-premises networks without exposing your traffic to the public internet.2

What model artifacts are automatically imported into ​ValidMind?

​ValidMind stores the following artifacts in the documentation via our ValidMind Library Python API:

  • Dataset and model metadata which allow generating documentation snippets programmatically (example: stored definition for “common logistic regression limitations” when a logistic regression model has been passed to the ​ValidMind test suite execution)
  • Quality and performance metrics collected from the dataset and model
  • Outputs from executed test suites
  • Images, plots, and visuals generated as part of extracting metrics and running tests

How is data retained within ​ValidMind?

  • ​ValidMind is a multi-tenant or single-tenant solution hosted on cloud providers.
  • With multi-tenant deployments, infrastructure is shared but with strict data isolation protocols that ensure that no tenant can access another’s sensitive information.
For organizations requiring the highest degree of data security, ​ValidMind offers a Virtual Private ValidMind (VPV)3 option to host the solution in a dedicated single-tenant cloud instance on the ​ValidMind cloud account.

Furthermore, ​ValidMind’s data retention policy complies with the SOC 2 security standard.

What about the confidentiality or size of data sent to ​ValidMind?

  • ​ValidMind does not send datasets outside the client’s environment.
  • The ValidMind Library executes test suites and functions locally in your environment and is not limited by dataset size.

Is activity on models, documentation, etc. logged?

  • Yes, the ValidMind Platform4 provides an audit trail functionality, enabling you to track or audit all the events associated with a specific model.
  • You can review a full record of comments, workflow status changes, and any other updates made to the model, including modifications to documentation or test results.

How does ​ValidMind manage updates to models?

  1. ​ValidMind allows model developers to re-run documentation functions with the ValidMind Library5 to capture changes in the model, such as changes in the number of features or hyperparameters.
  2. After a model developer has made a change in their development environment, such as to a Jupyter Notebook,6 they can execute the relevant ​ValidMind documentation function to update the corresponding documentation section.
  3. ​ValidMind will then automatically recreate the relevant figures and tables and update them in the online documentation.

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