Data minimization

Every field written and every record retained is a liability with no expiry: it can be adopted as fact, retrieved into a model, replicated to a system nobody is watching, or pasted into a tool nobody vetted. Data minimization treats the record itself as the attack surface and shrinks it — collect only what the task needs, write only what must persist, and age out what no longer earns its place.

In a sociotechnical deployment this is not a one-time schema decision but a standing discipline on the write edges: what the model is allowed to commit to the record, what a worker documents from an AI draft, how long either survives. Bounding those flows lowers exposure everywhere downstream at once, because there is simply less identifiable material in motion — less to read and believe, less to retrieve back into the model, less to carry out of the building under time pressure.

The person who owns this is typically a data-protection officer: an accountable role scoped to the systems holding personal data, empowered to say what is written and kept and what is not. National-survey findings put the demand squarely here — concerns about client data privacy and security are the profession's most-reported barrier to AI use, and stronger privacy and confidentiality protection is the single most-requested improvement to the tools.

The discipline it must never collapse into: deleting records blind to buy the appearance of safety. Removing content by volume rather than by what it contains can strip the benign material that was diluting the harmful, and a documented PAN-run scenario shows exactly this backfire — the contaminated share of a record system rising after a content-blind purge. Minimize what is written and how long it is kept; correct what is already there by content, never by panic.