Finding credit card numbers in Jira attachments before PCI-DSS does
If your organisation handles card payments, there’s a rule you already know: cardholder data is not supposed to live in a general-purpose system like Jira. And yet it does. A customer pastes a screenshot of a payment page into a support ticket. An invoice with a full card number gets attached to a billing issue. A spreadsheet reconciling failed transactions includes the PANs. None of it is deliberate, and all of it is a PCI-DSS finding waiting to happen. If you need to find credit card numbers in Jira attachments, the good news is that card numbers are one of the most detectable data types there is — and you can validate them to keep false positives low.
Why card data in Jira is a serious problem
PCI-DSS expects you to know everywhere cardholder data is stored and to minimise it ruthlessly. A primary account number sitting in a Jira attachment expands your cardholder data environment into a tool that almost certainly isn’t scoped, monitored, or controlled for it. The exposure is concrete: anyone with access to the issue can download the file, the data persists long after the ticket closes, and during an assessment you have no way to attest that it isn’t there. “We don’t scan attachments for card numbers” is not a position you want to defend to an assessor.
The detection gap
Card numbers hide in exactly the places ordinary tools can’t see. Jira’s search indexes fields and comments, not file contents, so a PAN inside a spreadsheet, invoice PDF, or payment screenshot never appears in a query. Malware scanning treats those as clean files. And because card data frequently arrives as a screenshot of a checkout page or a scanned invoice, text-based DLP misses it unless something reads the image — which means OCR.
A pattern you can actually trust
Card numbers are well suited to regex because they follow a predictable shape: 13 to 16 digits, often grouped in fours, sometimes with major-brand prefixes (4 for Visa, 51–55 for Mastercard, and so on). A starting pattern such as \b(?:\d[ -]*?){13,16}\b catches candidate numbers across spacing and dashes. The reason card detection can be made genuinely reliable is the Luhn checksum: real card numbers pass a specific mathematical check, so where your workflow allows, validating candidates against Luhn dramatically cuts false positives from order numbers and IDs that merely look card-like. Attachment Scanner for Jira lets you define the regex and JQL scope; treat Luhn-style validation as the refinement that separates “looks like a card” from “is almost certainly a card” during review.
Scoping and running the scan
Point the scan at the projects most likely to carry payment data — billing, finance, customer support — with JQL, and the app reads every supported attachment: Office documents and CSVs, text-layer and scanned PDFs, plain text, and images, using OCR for screenshots and scanned invoices. A document-only scan covers reconciliation spreadsheets and text exports for free; a full scan adds the screenshots and scans where card data most often appears. Tight scoping keeps the review manageable and the OCR credits predictable.
Review, then remediate fast
Cardholder data is high-severity, so speed matters. Each match shows the issue key, file name, extraction type, matched text, and context — enough to distinguish a live PAN in a customer invoice from a documented test card in a QA ticket. When you confirm real card data, bulk-select the matches and delete those attachments immediately; deletion is an explicit, admin-confirmed action recorded in the audit log, and nothing is removed automatically. That audit trail is also exactly what an assessor will want to see: evidence that you actively look for and remove cardholder data, not just a policy that says you shouldn’t store it.
Privacy, honest limits, and getting started
Scanning for card data means handling sensitive content, so the privacy model matters: OCR runs on dedicated EU/EEA GPU hardware with no public AI service, attachments are processed in memory and discarded, and only matched snippets are stored in Atlassian’s Forge storage, isolated per site — never whole files. Be clear-eyed about scope: this finds and helps you remove cardholder data already in Jira; it is on-demand rather than continuous, and Jira Cloud only for now, so pair it with prevention and with PCI controls elsewhere. No tool makes you PCI-compliant on its own — but being able to find and prove the removal of card numbers in attachments is a meaningful control. You can try it free for 30 days from the Atlassian Marketplace.
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