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Personal data ends up in AI pipelines by accident more than by design. A support ticket contains a name and an address, a document carries a date of birth, a transcript includes a card number the customer read out. All of it flows into prompts, and from there into logs, caches and third-party systems. Handling personal data in an AI pipeline is mostly about knowing where it accumulates, because the copies nobody planned are the ones with no controls on them. Prompts and logs are the quiet problem Careful teams protect their database and then log every prompt in full

A great deal of money is spent applying language models to problems that a rule, a lookup table or a well-designed form would solve better, faster and for nothing. The result works, which is what makes it hard to challenge, and it carries a permanent cost the simpler version would not. Knowing when not to use AI is more valuable than any implementation skill, and the cases are recognisable in advance. When the rule is already known If the logic can be written down completely, write it down. A model asked to apply a known rule will apply it correctly