A writing task rarely needs every detail in the source record. Before sending material to an AI tool, decide which facts the output actually requires and prepare an input that contains those facts.
This guide describes a practical preparation process. It does not certify a provider, establish a legal basis for sharing data or promise that replacing names makes a record anonymous. Follow the applicable organization policy and current provider terms for the tool you use.
OWASP recommends limiting sensitive-data access to what the user or process needs and restricting connected data sources. The preparation steps below apply that principle to a writing task. OWASP sensitive-information guidance.
Start with the output
Suppose you need a reusable equipment-return reminder. The draft needs a return deadline, an approved contact channel and the process the recipient should follow. It usually does not need an employee's salary, personal address, disciplinary notes or account credentials.
Write the purpose in one sentence: “Create a generic reminder that a reviewer will personalize and send later.” That helps distinguish template facts from recipient-specific details.
Transform the input before sharing it
Use a wholly fictional example while developing the template:
| Fictional source detail | Input for the writing task | Reason |
|---|---|---|
| Person: Example Employee 42 | [recipient] | The model needs the role of the field, not the identity |
| Return date: 2026-09-18 | [return date] | The finished template can be personalized later |
| Asset serial: SAMPLE-DEVICE-042 | [asset reference] | Keep the placeholder only if the process requires it |
| Internal contact | [approved support channel] | Use an approved destination during review |
| Unrelated employment notes | Omit | They do not support this task |
| Credential value | Omit entirely | A writing task does not need credentials |
A placeholder is a drafting aid, not anonymization proof. A unique combination of dates, role, location and circumstances may still identify someone even after their name is removed. If the real combination is unnecessary, use a generic scenario.
Keep the personalization step local to the approved process
Ask the model for structure and wording using placeholders. Review the draft, then insert the authorized recipient details in the system your organization uses for sending. Verify that every required placeholder has been filled and that no extra recipient or destination was introduced.
A useful prompt is:
“Draft a neutral equipment-return reminder using these placeholders. Preserve the approved process. Do not invent fees, deadlines or escalation steps. Leave missing details in brackets for the reviewer.”
This separates writing assistance from the consequential action of sending. The reviewer still needs the right permission and a verified destination.
Inspect more than the visible prompt
Check attachments, quoted email chains, screenshots, filenames, document properties and connected-tool context. A short prompt can still send a large amount of background material through an integration.
For the chosen service, check what is retained, who can access it, whether other tools can receive it and what deletion controls actually cover. Avoid a blanket claim based on a setting name or a local model process. The complete workflow determines what leaves the machine.
If an attachment's contents are not needed, do not include it. If an integration cannot expose or limit its context sufficiently for your task, use a simpler approved workflow or synthetic input.
Review the output for leakage and invention
Check that the answer does not include removed details, introduce a real-looking identity, invent a policy or add a contact address. Also look for accidental preservation of sensitive facts through paraphrase.
Test with a synthetic record that includes an irrelevant field. The correct behavior is to omit that field from the draft. This is a useful evaluation case, but it does not prove that a provider never retains submitted data.
Record the minimum useful evidence
Keep the sanitized input, approved template and review outcome where your process permits. Do not copy the sensitive original into a second log merely to document that it was removed. Set the retention and cleanup steps according to the actual tools involved, and verify supported deletion actions without claiming they erase every backup or downstream copy.
Add the preparation step to the AI Control Layer workbook. For shared evaluation inputs, use the synthetic case pack and keep its answer key out of the prompt.