Uber CISO: AI agents need an insider threat program
Uber Chief Information Security Officer Philip Martin says AI agents need the same kind of insider threat program companies already run for employees, because frontier models may be directed to act outside expected constraints. In an interview on Anthropic's Claude blog, he said his team once assembled threat-modeling context by hand and now uses Claude Fable to gather it from code, design documents and ticket history. Uber recently open-sourced AI Detection and Response (ADR) to collect real-time telemetry and take real-time action inside the agent harnesses it deploys; Martin said it complements Anthropic's Enterprise Frontier Safeguards, which let safeguards apply while Uber data stays inaccessible to Anthropic without permission.
Martin argues the stakes rise as models take on more complex, autonomous work, making a model-level insider threat program essential to rolling out these systems safely.
Uber runs one of the world's largest real-time marketplaces, with over 40 million trips and delivery orders a day. Philip Martin, Uber's Chief Information Security Officer (CISO), leads the team that protects Uber's data and the riders, drivers, and couriers who use the platform in the real world. His team started working with Claude on threat modeling before Claude Code existed, assembling context by hand. Now, with Claude Fable, Claude gathers that context itself from code, design docs, and ticket history.
Martin thinks AI agents need the same kind of insider threat program companies already run for their employees. We spoke with him about that idea, why he thinks so much about accidental misuse, and how Uber's open-source monitoring tools work alongside Enterprise Frontier Safeguards.
How are frontier models changing your security team’s work?
The really big difference between prior models and today’s frontier models is their ability to incorporate additional context over long time horizons, and as a result, generate really specific outputs. When we're doing security reviews or threat modeling for a given service application, we can let the models consume a lot more context about the service, whether that's the code itself, design documents, issues in Jira, or historical context on what the service is supposed to do and what it interacts with.
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