Responsible AI Tooling

Responsible AI tooling refers to software platforms and frameworks that help organizations build, test, and deploy AI systems in ways that reduce harm and maintain transparency. These tools typically address bias detection, fairness assessment, model explainability, and governance tracking, allowing teams to identify and document potential risks before systems reach users. For compliance officers and general counsels, this category matters because it shifts AI risk management from reactive (handling problems after deployment) to proactive, creating audit trails and evidence that your organization made reasonable efforts to manage known harms and meet emerging regulatory expectations.

1 item

Corporate PolicyGlobal2026-09-26

Microsoft's run-assert-eval Cuts Agent Violations From 30% to 5.9%, With Audit Proof

Microsoft has released run-assert-eval, a developer tool that chains threat modeling, policy evaluation, and runtime enforcement into a single automated loop for AI agent governance. In a billing-support agent demonstration, the tool reduced cross-account data disclosure violations from 30% to 5.9%. The result is a quantified, before-and-after evidence package that compliance teams can use to demonstrate control effectiveness to auditors.

Microsoft agentic AI runtime enforcement AI agent governance responsible AI tooling policy-as-code pre-deployment testing