Policy-as-Code for AI: Designing Testable Controls, Lineage and Evidence

As AI permeates business, the assurance challenge shifts from “Do we have a policy?” to “Are the controls actually executed at the point of use, and can we prove it?” This session presents a practical blueprint for translating privacy, security and ethics requirements into policy-as-code so controls are embedded in data and AI pipelines, operate consistently at scale and produce audit-ready artifacts by default. The session will break the problem into four auditable layers.

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• Archived Webinar
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• English