In one sentence govern by "data lineage + deletion propagation," and don't read "deleted from the primary DB" as "deleted" — that's the most common false security here.
In one sentence how much you can see and delete is decided by the product and its backend. The harder lesson comes from NYT v. OpenAI (2025) govern the product's memory as one more copy of your data, subject to both the product and the law — don't read "I flipped the switch off" as "it's all deleted."
In one sentence the same model parameters can be produced from a different dataset / a different gradient sequence, so a model owner can pass off a fabricated "I unlearned it" proof while actually keeping the record. Alongside it, TOFU (Maini et al., COLM 2024) turns "forget quality" into a measurable benchmark and finds no off-the-shelf method convincingly passes the "forget quality vs. utility" tradeoff. Conclusion first: audit the algorithm / process, not the final weights; "deleted" must be provable, or it's compliance theater.
In one sentence once memorization is in the weights, deleting the source doesn't make it vanish). Machine unlearning aims to make the model "behave as if it never saw that record." Two hard parts: ① how to actually forget (exact unlearning ≈ equivalent retraining, expensive; approximate unlearning is fast but unguaranteed); ② how to prove it forgot (verifiable deletion) — the piece engineering most lacks.