# Guardians of the Dataverse: Securing the AI Supply and Data Chain

Source: https://www.testifysec.com/blog/guardians-dataverse/

Author: Frederick Kautz

Published: 2024-06-15

Drawing on NIST SP 800-204D to extend software supply-chain security to AI/ML and big-data pipelines. Examines how CNCF and OpenSSF projects can inform AI workflow design.

From the event archive

This is an event archive. Product names, affiliations, and demonstrations reflect the recording, not the current release. The overview is editorial context, not a transcript or a product support commitment.

- [Current TestifySec docs →](https://www.testifysec.com/docs)
- [Trust architecture →](https://www.testifysec.com/docs/cilock/trust)

## Watch the recording

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[Open the recording at its source ↗](https://www.youtube.com/watch?v=5KvyVP1JYNg)

Published by CNCF \[Cloud Native Computing Foundation\]. The source is authoritative for the recording title, date, and participants.

## Questions to take into the discussion

- Provenance for data, code, and models.
- Applying supply-chain concepts to AI workflows.
- Trust boundaries around training and data processing.

Use these as an editorial guide while watching or exploring the topic. They are not quotations or a verified transcript of the session.

## Apply the ideas to a current workflow

For an implementation, start with the [shared documentation](https://www.testifysec.com/docs). CI/lock captures the work, Pushgate checks the push, and the platform manages gates and technical control evidence. Check the [support matrix](https://www.testifysec.com/docs/concepts/support-matrix) for the release and environment you intend to use.

A signed statement can establish attribution and detect alteration under a configured trust model. It does not establish that the producer reported honestly or that a whole compliance framework is satisfied. The [trust architecture](https://www.testifysec.com/docs/cilock/trust) explains those boundaries.