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How to operationalize AI governance with W&B Weave

Weights & Biases16 June 2026Watch on YouTube

Description

A single eval score is not an approval record. Here's how teams defend an AI release with evidence, not screenshots. Ensuring AI applications meet industry standards and government mandates often involves navigating scattered evidence and complex review processes. In this video, we demonstrate the open-source AI Governance Toolkit built on W&B Weave, which acts as a central system of record for both developers and compliance teams. We walk through a structured "review gate" process to show how you can organize evidence against frameworks like the MIT AI Risk Repository, NIST AI RMF, and the EU AI Act. Learn how to streamline your compliance-driven workflows and provide your team with the exact traces needed to fix failure modes before production. 📘 *New ebook:* https://utm.io/upTJ2 💻 *Open-source AI Governance Toolkit:* https://github.com/wandb/rai-toolkit ⏳ *Timestamps:* 0:00  The problem: scattered evidence and review gates 1:08  W&B Weave and the open-source AI Governance Toolkit 2:25  Intake: submitting an application profile 3:07  Scope: deriving the review plan and risk tier 4:08  Assess: the failure verdict and red-team results 5:48  Probe: manual testing and pinning findings 6:56  Decide: human judgment and the final decision 8:07  Slack automation and wrap-up

What you'll learn

  • W&B Weave and the AI Governance Toolkit help teams establish a central system for tracking compliance evidence instead of scattered screenshots.
  • A structured review process with phases (intake, scope, assess, probe, decide) organizes AI evaluations against frameworks like NIST AI RMF and EU AI Act.
  • Teams can document failure modes and risks with traces and red-team results to defend AI releases with hard evidence.

Frequently asked questions

What problem does the AI Governance Toolkit solve?
The toolkit solves the problem of scattered evidence and complex review processes by providing a central system of record where developers and compliance teams can work together on AI governance.
What phases does an AI application go through in the review process?
The application goes through five phases: intake (submission), scope (deriving review plan and risk tier), assess (failure verdict and red-team results), probe (manual testing and findings), and decide (human judgment and final decision).
Against which standards can you demonstrate compliance with this toolkit?
The toolkit helps teams demonstrate compliance against frameworks such as the MIT AI Risk Repository, NIST AI RMF, and the EU AI Act by organizing evidence in a structured way.
How does the toolkit help teams identify failure modes before production?
The toolkit provides traces and red-team results that teams can use for manual testing and documenting findings, so failure modes can be fixed before applications go into production.

Topics

ai governancecompliancerisk managementmlopspolicy