AI DLP for spreadsheets: inspect hidden content

Evaluate spreadsheet AI DLP by checking which workbook parts it can inspect before the file leaves the approved boundary. Include hidden sheets, hidden rows and columns, formula text, cached results and any unsupported objects in the coverage record. Require an explicit outcome when extraction is incomplete.

A workbook's visible cells show only one view of the file. An upload can also contain stored values and structures that a person reviewing the open sheet has not seen. The security decision needs to account for the actual material being transferred.

Separate the workbook from its preview

Microsoft's Open XML guidance on hidden worksheets describes both Hidden and VeryHidden sheet states. Its hidden-row and column example shows that those visibility settings can be retrieved from the document structure.

These features support ordinary spreadsheet organisation. Their security relevance follows from the upload boundary: data remains in the workbook even when that view hides it. Ask the DLP supplier whether extraction includes each state and which evidence identifies an omitted section.

Define the route being evaluated. Uploading an original workbook, pasting a selected range and exporting one sheet as CSV produce different inputs. Test the route employees actually use, preserving the file type and relevant application version in the result.

Inspect formula text and stored results separately

Microsoft's formula documentation explains that SpreadsheetML stores formula text in one element and the cached value from the last calculation in another. Formulas can refer to cells in other sheets or workbooks.

Ask whether the extractor reads formula expressions, stored values or both. A displayed total and a formula containing a text constant provide different evidence. Record what happens when the cached result is absent or differs from a newly calculated value.

Keep the inspection exercise passive. Use harmless, prebuilt fixtures and avoid executing macros, refreshing external connections or contacting a linked workbook merely to inspect it. Any active calculation or retrieval needs a separate authorised design and containment review.

This guide focuses on extraction coverage. It leaves spreadsheet sanitisation fidelity to a separate test: changing a cell can also change calculations, references and the usefulness of the document.

Keep provider preprocessing in its proper scope

OpenAI's file-input documentation says its Responses API uses spreadsheet-specific augmentation, parsing up to the first 1,000 rows per sheet and adding summary and header metadata. It also says non-PDF embedded images and charts are excluded from model context by that file-input processing.

Those statements describe a particular API's preprocessing. They leave whole-file transfer, storage and other processing paths to their own contracts. A model answering from a reduced representation supplies no evidence that the original workbook was safe to upload.

Ask the security supplier for its own row, sheet, size, time and object limits. Record whether hitting a limit produces an incomplete-inspection result. A successful parse of one supported section must retain the remaining coverage gaps in the final decision.

Build a small, diagnostic fixture set

Create fictional workbooks whose expected contents are known independently of the product being evaluated. Give each sensitive-looking test value a unique, nonfunctional marker and keep a manifest of its location. Use no real credentials, customer records or employee information.

Start with these cases:

1. The same marker in a visible cell, hidden row and hidden column, each in a separate file.
2. A marker on a hidden sheet and another on a very hidden sheet.
3. Distinct markers in formula text and its stored result, with the expected handling documented.
4. A marker just inside and just beyond the supplier's declared extraction limit.
5. A benign workbook that should complete the legitimate task without intervention.

Add comments, embedded objects, images, encrypted files and other workbook features according to the proposed deployment. Require the supplier to classify each as inspected, unsupported or failed. An unsupported case can be a documented boundary with an agreed safe route.

Keep the fixtures otherwise simple. Distinct markers let a missed section be traced without guessing whether one repeated value was detected somewhere else. Preserve the exact source file hash and independently verify that the marker was saved in the intended location.

Verify the decision before delivery

For each fixture, collect the extraction status, relevant finding category, policy outcome and observed delivery state. Use a controlled receiving endpoint where the evaluated route permits it. Establish whether the original file, a transformed file or no file reached that endpoint.

Make failure handling part of the same exercise. Test a timed-out parser, a damaged workbook and an unavailable inspection component. Check the user-facing recovery path as well as the security result, so legitimate work has a clear way to continue within policy.

Our AI DLP comparison guide places these checks within a wider buying decision. This is a proposed evaluation method based on Microsoft and OpenAI documentation; no product's spreadsheet coverage or prevention rate was tested here.

Promptective's desktop packages and public self-serve browser extension are available for workplace AI protection. Include your actual file-sharing routes in the discussion. Review your AI data-handling requirements with Promptective.