Data Handling

Data handling means how a vendor collects, uses, stores, shares, protects, or deletes data when an AI feature is used. It includes prompts, files, outputs, logs, transcripts, user activity, and system metadata.

AI features often need data to be useful. But the data may include customer records, employee information, business plans, contracts, code, or support conversations. Leaders need to know where that data goes before they enable the feature.

Data handling is like sending documents to an outside service. You would want to know who receives the documents, what they do with them, how long they keep them, and whether they send them anywhere else.

A document collaboration tool may add AI summaries. That sounds harmless until you ask whether the AI feature can read confidential board papers, customer contracts, or employee data. The feature may be useful, but it needs boundaries.

Create a vendor data question set. Ask about input data, output data, logs, retention, training use, access controls, and deletion. Match the answer to the sensitivity of the work.

The common mistake is checking the main product privacy policy and assuming the AI feature works the same way. AI features may have separate settings, subprocessors, or data flows.