How do you design consent and data-retention policy for data that feeds ML training?
Consent and retention decide whether you can lawfully train on data and how long you may hold it. The signal is purpose limitation, granular consent, enforceable TTLs, and a plan for deleting data already baked into a model. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Consent and retention decide whether you can lawfully train on data and how long you may hold it. The signal is purpose limitation, granular consent, enforceable TTLs, and a plan for deleting data already baked into a model. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
