How to Build a Privacy Filter Between User Activity and AI Agent Memory

As AI agents expand beyond chat into browsers, operating systems, and workflow tools, they increasingly capture sensitive user data including clicks, typed text, open files, and tool calls. Developers are warned against defaulting to broad data capture, and instead advised to define the minimum event stream necessary to serve the user. A recommended privacy filter sits between raw user activity and agent memory, performing five key functions: event allowlisting, sensitive data detection, purpose binding, retention control, and retrieval control. Unlike standard application logs viewed only by engineers, agent memory can be read by models, combined with other data, and used to drive future decisions, raising the privacy stakes significantly. The guide emphasizes classifying events before attempting redaction, arguing that avoiding unnecessary data collection is safer than scrubbing sensitive details after the fact.