If Legal requires written confirmation that no customer...

When asked by legal or compliance teams to certify absolute absence of PII in a pre-training corpus, an engineer must recognize the technical impossibility of strict enumeration. Because preprocessing and tokenization break down and transform document boundaries, document identity is lost in the model corpus, rendering exact record-by-record verification unfeasible.

Instead of absolute certification, provide a statistical measurement approach. Use sample-size arithmetic to calculate a mathematical confidence bound over the dataset. Furthermore, clarify that residual risk in trained neural networks manifests as conditional extraction under adversarial prompting strategies rather than static tabular exposure. If data unlearning or deletion is later requested, point out that unlearning requires exact sequence enumeration, which remains untraceable in the raw tokenized pre-training state.