The decision to use an AI tool often becomes a decision about information before the first answer appears. A pasted document, an uploaded spreadsheet or a connected folder can reveal far more than the immediate task requires. A useful approach begins by identifying the minimum material needed, the service receiving it and the people responsible for approving that use.
The task and the document are different things
The NIST generative AI risk profile identifies privacy risks involving leakage, unauthorised disclosure and inferences about individuals. That framing extends the question beyond whether someone intentionally uploads a secret: information can also be exposed through combinations of details.
A hypothetical team asking for help improving a customer-email template may only need a fictional example and the desired tone. It does not automatically need to provide a real customer thread with contact information, internal comments and previous attachments. Defining the task first makes it easier to decide what can be left out.
Replacing a name is not a complete assessment of a document. An unusual job title, a specific event or a combination of dates may still identify a person. Equally, invented sample data should be clearly marked as fictional so that it is not later mistaken for a real record.
Know which service receives the material
A familiar interface does not establish how a particular account is configured. Before using work information, check the organisation’s approved service, relevant account settings and applicable agreement. Questions about retention, access, training use and connected services need answers for the actual product and account concerned.
This article offers a way to organise those questions, rather than a determination of legal compliance. Where personal or confidential information is involved, the organisation’s responsible privacy, security or legal team should assess the proposed processing in its own context.
The OECD AI Principles include privacy among the rights and values relevant to trustworthy AI. A practical interpretation for an internal workflow is to document why a category of information is needed and who has authorised the destination, rather than relying on a general enthusiasm for experimentation.
Connections can change the scope
A single pasted paragraph and an ongoing connection to a shared folder are different arrangements. The latter calls for a clear account of which files are reachable, whether future additions become available and how access can be withdrawn. The answer should come from the service’s documented behaviour and the organisation’s configuration.
ENISA identifies security and privacy challenges around AI systems. For the sample workflow described here, an original review exercise is to draw the route of information: source file, AI service, output location and final recipient. Each step raises a concrete access question.
This map can also expose assumptions. A colleague may think the tool receives one selected file while another believes it can search the entire workspace. Resolving that difference before use is more useful than discovering it after an unexpected result.
The output needs an information check too
A generated summary can repeat details that the final audience should not receive. Review the output for unnecessary identifying information as well as factual accuracy. A shorter answer is not automatically a less sensitive one if it concentrates the most revealing details.
Before a pilot expands, the team should be able to describe what enters the service, where the output goes and how mistakes are reported. A small, approved sample is often enough to test whether the tool can help with the writing task. Broader information access is a separate decision that needs its own justification.
