How to Find Hidden PII and Passwords in Jira Attachments
Most teams put real effort into securing the parts of Jira they can read: issue fields, comments, permission schemes. But the riskiest data rarely sits in a field. It lives inside the attachments — a password pasted into a screenshot, a customer record on a scanned PDF, an API key in a config file someone dropped onto a ticket “just to get it working.”
This is the blind spot. Jira’s native search and most data-loss-prevention (DLP) tools read text. The moment sensitive information is trapped inside an image or a scanned document, that text-based scanning goes blind. This guide explains why the gap exists and gives you a practical, repeatable way to find and remediate sensitive data in Jira attachments — including the files no keyword search will ever surface.
Why Jira can’t see what’s inside its own attachments
It’s worth being precise about what Jira does and does not do, because the gap is easy to misjudge.
- JQL and native search index fields, not file contents. You can search issue summaries, descriptions, comments and custom fields. You cannot search for a password that lives inside a PNG, because the index never reads the pixels.
- Permission schemes control access, not content. Restricting who can open an issue does nothing about what sensitive data the attachment on that issue actually holds — or who already downloaded it.
- Atlassian’s built-in malware scanning is not content discovery. Atlassian now scans uploaded files for malware across Jira, Confluence and Trello, which is valuable — but it answers “is this file dangerous?”, not “does this file contain a credit-card number or an SSN?”. Those are completely different questions.
- Most DLP tooling reads text, comments and plain documents. That covers a lot — until the data is in a screenshot or a scanned form, where it’s just an image to a text scanner.
That last point is the crux. To read text out of an image or scanned PDF you need OCR (optical character recognition). Without it, your attachment “coverage” has a hole exactly where the highest-risk content tends to land. A password in a screenshot and a passport number on a scanned form are, to a text scanner, just pixels.
What sensitive data actually ends up in attachments
Across support desks, internal projects and service portals, the same patterns appear again and again. If you only audit one thing, audit for these:
- Credentials — passwords pasted into screenshots, “temporary” logins shared on tickets, API keys and tokens in config snippets or .env files.
- Personal data (PII) — names, emails, phone numbers, national IDs, dates of birth, addresses, often inside customer-uploaded PDFs or support screenshots.
- Financial data (PCI) — full card numbers in spreadsheets, invoices and chat exports.
- Health data (PHI) — patient records and forms uploaded to service desks, frequently as scanned documents.
- Internal secrets — connection strings, private keys, infrastructure diagrams with hostnames and credentials baked in.
None of this is a sign of a careless team. It’s the natural by-product of thousands of people moving fast and reaching for the quickest way to share context. The data accumulates quietly — and it usually surfaces at the worst possible time: during an audit, a customer security review, or a breach investigation.
Why this is a compliance problem, not just hygiene. Regulations such as GDPR, HIPAA, PCI-DSS and CCPA expect you to know what sensitive data you hold and where it lives, and to be able to honour deletion and access requests. You cannot delete, restrict or report on a password or a customer record you don’t know exists. Undiscovered PII in attachments is, in practice, undiscovered risk on your balance sheet.
A practical method for finding sensitive data in Jira attachments
Whether you do this manually, with scripts, or with a dedicated app, the same six-step method applies. Treat it as a checklist.
1. Define what “sensitive” means for you
Don’t try to find “everything.” Decide which data types actually matter for your sector and obligations, then express each one as a pattern. Pattern matching (regex) is how you turn a vague worry into something a machine can detect. A few starting points:
| Data type | Example pattern (starting point) |
|---|---|
| Email address | [A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,} |
| Credit-card-like number | \b(?:\d[ -]*?){13,16}\b |
| US Social Security Number | \b\d{3}-\d{2}-\d{4}\b |
| AWS access key | \bAKIA[0-9A-Z]{16}\b |
| Password label | (?i)\b(pass(word)?|pwd|secret)\b\s*[:=] |
Treat these as starting points, not finished rules. Real patterns need tuning (Luhn validation for card numbers, locale-specific ID formats) and testing against your own data to balance catches against false positives.
2. Scope the scan before you run it
Scanning your entire instance on day one produces noise, not insight. Narrow it down with JQL so the results are actionable:
- Start with the highest-risk projects — service desks and customer-facing portals first.
- Bound it by time, e.g. the last quarter, to make the result set reviewable.
- Focus on issues that actually carry attachments.
A scope expressed in JQL might look like:
project = SUPPORT AND created >= -90d AND attachments IS NOT EMPTY ORDER BY created DESC
3. Cover every file type — including images and scanned PDFs
This is the step people skip, and it’s the one that matters most. Make sure your approach reads:
- Text-extractable files — Office documents, CSVs, text files, digital PDFs.
- Images and screenshots — PNG, JPG and similar, via OCR.
- Scanned PDFs — documents that look like text but are really pictures of text, again via OCR.
If your method can’t read images and scanned PDFs, accept that your coverage stops exactly where the riskiest data hides. That’s the gap an OCR-capable scanner is built to close.
4. Review findings with context — don’t auto-delete
A raw list of “possible matches” is dangerous to act on blindly. For every hit you want to see the issue key, the attachment, the matched text and the surrounding context, so a human can confirm it’s a real exposure and not a false positive before anything is deleted or restricted. Equally important: surface the files that were skipped or threw warnings, so you know what your scan didn’t cover. Hidden gaps are how false confidence creeps in.
5. Remediate, then prove it
Once a finding is confirmed, your options are usually some combination of: delete the attachment, replace it with a redacted version, tighten the issue’s permissions, and notify the person who uploaded it. Keep an audit log of what was found and what you did about it — that record is exactly what an auditor or a data-subject request will ask for later.
6. Make it repeat
A one-off scan tells you about today. Sensitive data keeps arriving tomorrow. Schedule periodic scans (weekly or monthly) on your highest-risk scopes so new exposures are caught while they’re still cheap to fix, rather than discovered during an incident.
Doing it manually vs. using a tool
You can get a long way with the method above and a bit of scripting against the Jira REST API. It’s a reasonable choice for a one-time audit on a small instance. The honest trade-offs:
- OCR is the hard part. Reading text out of images and scanned PDFs reliably — and doing it without shipping your attachments off to a public AI service — is non-trivial to build and maintain yourself.
- Privacy of the scan itself matters. Whatever reads your attachments is, by definition, handling your most sensitive content. You want it processing files in memory only, not persisting them, and ideally processing within a jurisdiction you control.
- Repeatability and reporting add up. Scheduling, deduplication, context-rich results and audit logs are a lot of plumbing to own long-term.
If you’d rather not build that, dedicated marketplace apps cover the same workflow. One example built specifically around the OCR gap is Actonic’s Attachment Scanner – OCR, PII & Password Detection for Jira: you define a regex or text pattern plus a JQL scope, and it reads every supported attachment — including screenshots and scanned PDFs via an OCR vision model — then reports each match with file context. Attachment content is held in memory only and never persisted, with EU-based processing; you can read more on the product page. Whatever you choose, judge it against the checklist above rather than the feature list.
Five habits that stop sensitive data reaching attachments in the first place
Finding data after the fact is reactive. Pair your scanning with prevention:
- Give people a safe channel for secrets. A password manager or secrets vault removes the excuse to paste credentials into a ticket.
- Educate on screenshots. Most password-in-a-screenshot incidents are habit, not malice. A short reminder changes behaviour.
- Restrict attachment access by default. Tighter permissions limit the blast radius when something does slip through.
- Set retention rules. Old attachments are pure risk with little value. Clear them on a schedule.
- Scan continuously, not once. Treat attachment scanning as an ongoing control, the same way you treat malware scanning.
Frequently asked questions
Does Jira scan attachments for sensitive data?
No. Atlassian scans new uploads for malware, but it does not scan attachment content for PII, passwords or other sensitive data. That requires a dedicated approach or app.
Can JQL search inside Jira attachments?
No. JQL and Jira’s native search index issue fields and metadata, not the contents of attached files. You cannot find a password that exists only inside an image or scanned PDF using JQL alone.
How do I find a password hidden in a screenshot in Jira?
You need OCR to read the text out of the image, combined with a pattern that recognises credentials. Keyword search will never find it, because there is no searchable text — only pixels.
Is PII in Jira attachments a GDPR problem?
It can be. GDPR expects you to know what personal data you hold and where, and to be able to delete it on request. Personal data sitting undiscovered in attachments undermines both obligations, which is why discovery is the first step toward compliance.
What’s the difference between malware scanning and PII scanning?
Malware scanning asks “is this file dangerous to open?” PII/content scanning asks “does this file contain sensitive information that shouldn’t be here?” They protect against different risks, and you generally want both.
The takeaway
Securing the readable parts of Jira is necessary but not sufficient. The data most likely to hurt you in an audit or a breach is the data you can’t see — credentials in screenshots, PII on scanned forms, secrets in dropped config files. Closing that gap comes down to a repeatable method: define your patterns, scope the scan, cover every file type including images, review with context, remediate, and repeat. Do that, and “what’s in our attachments?” stops being a question you dread and becomes one you can answer.
Further reading: The hidden risk of PII stored in Jira attachments.
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