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PrivacyFlow: Information-Flow Defense for LLM Agent Privacy Leakage
- Engineer — Learn: Novel framework for tracing data provenance and simulating disclosure consequences in multi-step LLM agent workflows; relevant to engineers designing or evaluating agentic systems that handle personal data, but requires no change to running systems today.
- SOC/IR — Skip
- Leader — Learn: Research quantifies the privacy-utility-autonomy tradeoff in autonomous AI agents — useful framing for shaping AI governance policy before agentic deployments scale, but no near-term regulatory deadline or vendor exposure requiring action.
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