Uber’s engineering team describes a “Secure AI” framework intended to govern autonomous AI agents from setup through ongoing operation. The framework addresses a gap that traditional identity and authorization systems can’t cover: while such controls can verify who is allowed to access a resource, they do not determine whether an agent’s proposed actions are appropriate for the context.
Across the accounts, the problem is framed as expanded risk in agentic systems, where the threat surface includes the chain of events an agent triggers and which may be difficult for human or system operators to observe or stop. Reported concerns include semantic failure modes such as prompt injection, tool or memory poisoning, goal hijacking, behavioral drift, and actions driven by hallucinations.
Uber outlines three lifecycle layers—provisioning-time validation, runtime enforcement, and continuous assurance. It also describes how provisioning records agent identity and permissions and how runtime and assurance mechanisms are designed to validate action appropriateness. InfoQ focuses on summarizing Uber’s key idea: that action appropriateness depends on the user request, the agent’s purpose, permissions, and interaction context.
