As organizations race to deploy AI agents, training pipelines, and autonomous workflows, a critical foundation is playing catch up: identity. Without a unified, cryptographically backed identity framework, one that extends seamlessly across humans, AI agents, CI/CD pipelines, and arbitrary workloads, every layer of the AI lifecycle is exposed to unauditable, ungovernable risk.

This talk argues that cryptographic identity is not merely a security best practice but the essential building block for trustworthy AI. From model training environments where data provenance and access control determine integrity, to production systems where autonomous agents act on behalf of users and organizations, the ability to authenticate, authorize, and audit every actor in the chain is what separates experimentation from enterprise-grade AI. We will explore how a unified identity plane eliminates the fragmented, secret-laden approaches that dominate today's infrastructure, and why organizations that solve identity first will be the ones that unlock AI's full potential: securely, at scale, and with confidence.