Why names matter — and why they’re hard

For compliance, names are often the thread that ties a record to a person. A spreadsheet of order numbers is low-risk until a column of customer names makes every row identifiable. A scanned contract is just paper until the signatory’s name turns it into personal data. So being able to locate names matters for data-subject requests, for minimisation, and for knowing what you actually hold.

The difficulty is that names don’t follow a rule a machine can match cleanly. There’s no equivalent of the “@” in an email or the length of a card number. Capitalised words appear constantly in normal text. Names cross cultures and formats. A purely pattern-based search for “any name” will both over-match (flagging ordinary words) and under-match (missing names it doesn’t expect). It’s important to say that plainly: Attachment Scanner detects patterns you define, it is not a natural-language entity recogniser, and there is no built-in “name detector” that understands which words are people.

The approach that genuinely works: known-name search

The strongest, most reliable use of name detection is the targeted one: you already know the name you’re looking for. This is the everyday reality of a data-subject access or deletion request — someone asks what data you hold about them. Here, pattern matching is exactly the right tool. You define the specific name (or a few spelling variants) as your pattern, scope the scan with JQL, and the app reads every supported attachment — including images and scanned PDFs via OCR — and reports every file where that name appears, with context. For finding one person across thousands of attachments, this is fast, precise, and defensible.

It’s a different and far more dependable job than “find every name in our instance,” and it’s where this capability earns its place.

Contextual patterns for semi-structured documents

The second approach that works well is to anchor on the labels around names rather than the names themselves. Many documents are predictable: a form with a “Name:” field, a support export with a “Customer:” line, a letter that opens with “Dear ___.” You can write a regex that matches the label and captures what follows — for example, the words after Name: or Patient: or Bearer:. This turns an unstructured problem into a structured one, and it’s reliable precisely because you’re matching the form, not guessing at the name.

The same logic applies to spreadsheets and CSVs with a clear name column, where the position and surrounding headers give you a dependable anchor.

How a scan comes together

In practice you build a template — a name or label pattern, a JQL scope, and a scan mode — and run it on demand. A full scan reads images and all PDF types with OCR, which you need for scanned forms and screenshots; a document-only scan covers Office and text files for free. Results arrive in a dashboard where each match shows the issue key (click through to Jira), the file name, whether the text came from OCR or direct extraction, the matched text, and the surrounding context. With names especially, that context column is essential — it’s how you confirm “Mark” is a person and not the verb before you act on anything.

Setting expectations honestly

Open-ended name discovery will always involve some noise. Expect false positives (ordinary capitalised words, place names, product names) and accept that some unusual names will slip through. That’s not a flaw in any one tool; it’s the nature of names. The practical implication: treat broad name scans as a prioritised worklist for human review, not a finished inventory, and lean on the targeted known-name approach whenever you can, because that’s where the results are trustworthy. Being clear about this up front is more useful than a promise no pattern matcher can keep.

Remediation, privacy, and limits

When you confirm that a file holds names that shouldn’t be there, you can bulk-select and delete those attachments — an explicit, admin-confirmed action recorded in the audit log, never automatic. On privacy, OCR runs on dedicated EU/EEA GPU hardware with no public AI service involved; attachments are processed in memory and discarded, and only matched snippets are stored in Atlassian’s Forge storage, isolated per site. Worth noting for this topic specifically: the app reads attachment contents, not the reporter or assignee identity fields, so it doesn’t build a profile of who touched an issue.

The usual limits apply: on-demand scanning rather than continuous monitoring, and Jira Cloud only for now. Within those bounds — and with realistic expectations about open-ended matching — name detection is most valuable as the engine behind a clean, auditable answer to “show me everything you hold about this person.” You can try Attachment Scanner for Jira free for 30 days from the Atlassian Marketplace.