Uber discloses a three-layer security framework for agents
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Uber has published its Secure AI agent security framework, sharing its design, implementation approach, and production deployment experience, with the goal of enabling engineering teams to embed protections from the start of building agent systems rather than adding them after launch. The framework has three layers: configuration-time validation, runtime enforcement, and continuous assurance, adding a semantic and behavioral control layer on top of traditional identity and authorization. Among these, AI Registry defines an agent's persona, intent, and constraints; AI Guard determines at runtime whether an action is consistent with the user request and session context, and uses a local small model to control latency. Uber says it published these trade-offs and lessons to provide engineering teams with reusable practice patterns.
AI overview based on source reports · Updated 47 min ago
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- Hacker News AISecuring AI Agents [at Uber]: Lessons from Real-World Deployment
Uber describes a defense-in-depth framework for securing AI agents across provisioning-time validation, runtime enforcement, and continuous assurance, built around an AI Registry, personas and explicit intents, tool and MCP validation, context-aware guardrails, and red teaming. At runtime, AI Guard acts as a policy decision point using small locally hosted models, while prompt provenance and evidence chaining correlate privileged actions back to what the user actually requested. The post also covers tradeoffs around false blocks, latency, and developer adoption, and argues that restricting agents to explicit intents and constraints was the most effective mitigation.
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