OpenAI Privacy Filter Runs On-Device

This is the right shape of privacy tooling: cheap enough to sit in front of any cloud call, but smart enough to beat regexes. The on-device demo matters more than the benchmark number, because deployment is what turns privacy from a promise into an architecture choice. OpenAI says the model is small enough to run locally, so sensitive drafts, chat logs, and OCR output can be screened before upload. ExecuTorch plus React Native makes the deployment story legible for mobile teams, not just PyTorch researchers. Privacy filtering is a preflight problem, so putting it on-device reduces exposure before data reaches logging, search, or LLM pipelines. Around 600 MB RAM is workable for modern phones, but it still sets a practical floor for broader mobile adoption. If recall holds up in real apps, this could become a standard guardrail layer for AI products that handle user text.