Sure, it’s a tightrope. You can run the model on encrypted data, or add differential privacy so it never memorises specifics. The trick is to keep the learning signal strong while masking the raw inputs. If you over‑blur, the model learns nothing; if you under‑blur, privacy slips. It’s a constant back‑and‑forth, but doable if you keep a watchdog on the data flows. Keep an eye on the gradients—those are the leak vector you’ll want to scrub.