Uber details a defense-in-depth security framework for governing AI agent behavior at runtime

Uber's engineering team published details of "Uber Secure AI," a defense-in-depth framework governing AI agent behavior across three layers: provisioning-time validation, runtime enforcement, and continuous assurance. Components include an AI Registry, the Argus automated analysis platform, AI Guard as a policy decision point, and Agent Guard runtime hooks. The design uses small, locally hosted specialized models to meet strict latency budgets rather than general-purpose LLMs. The author's central lesson: security, accuracy, latency, cost, autonomy, and developer experience cannot be optimized independently.

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