The skeletons in your Data Center closet

[#1]

Picture an auditor asking, very politely, whether you can promise there is no personal data or stray credentials sitting in your Jira tickets or Confluence pages right now. You start to say "of course not," and then you remember that 2021 support ticket where someone pasted a customer's full card number into a comment. The room gets quiet.

If you run Jira or Confluence Data Center, you have probably had that thought. Sensitive data piles up the way a junk drawer does, one reasonable decision at a time. A detail dropped into a ticket to speed things along. A token sitting in the corner of a screenshot. A page that quietly quotes production data. Multiply that by a few years and a few hundred people, and there you are.

What tends to hide in a mature instance

• Content you cannot see: PII, card numbers, passwords, and API keys tucked into descriptions, comments, and page bodies. The sneaky part is page history, where a secret can live on in an old revision long after you scrub the current version.
• Attachment bloat: years of screenshots and exports nobody has opened, some huge, some sensitive.
• Ghost users: people who left but still have accounts, plus near-duplicate groups. Every one is a door, and on per-user licensing, a line on a bill.

A sane cleanup, in five moves

1. Decide what you are hunting for and write it as patterns (regex is your friend).
2. Look/Scan everywhere, history included.
3. Choose case by case: redact, encrypt, or delete. 
4. Keep the receipts so you can prove what you did.
5. Keep the data at home. Do not ship your sensitive content to some far-away cloud to be scanned.

Do all that by hand across a large instance and you are in for slow, error-prone weekends. That gap is why our app exists.

Quick heads-up: I work on the team at miniOrange, a Marketplace Partner that builds security apps for Atlassian products. We built a tool for this but the advice above is useful either way.

Where our app fits

At miniOrange, we built the DLP - Sensitive Data (PII, GDPR) Scanner and Cleaner for Jira and Confluence Data Center to handle all three problems above from one screen.

Comprehensive scanning- Scans tickets, pages, comments, and page history against ready-made patterns plus your own custom regex

Full revision coverage- Checks every revision, not just the live page, so secrets cleaned up months ago don't quietly survive in an old version

Flexible remediation- Lets you redact, encrypt, or delete each finding yourself

Attachment auditing- Audits attachments by size and type, surfacing forgotten exports and sensitive screenshots that would otherwise sit unnoticed for years

For the ghost users, it cleans up stale accounts and near-duplicate groups in bulk, closing the doors former employees left open and trimming the lines on your per-user bill.

Findings map to GDPR, HIPAA, PCI DSS, and SOC 2, so you have the receipts when the auditor asks. And the scanning runs inside your own environment, so nothing gets sent out to be processed. Your data stays home.

One honest caveat: no scanner catches everything. Regex only finds the patterns you describe. Treat a tool like this as a way to shrink the haystack and keep it small, and pair it with the habit of not pasting secrets into tickets in the first place.

Over to you

How are you handling this today, and what is the strangest thing you have ever found buried in an old Confluence page? Drop it below.

Both listings are on the Atlassian Marketplace:

• Confluence Data Center: https://marketplace.atlassian.com/apps/544091142/dlp-sensitive-data-pii-gdpr-scanner-for-confluence?hosting=datacenter
• Jira Data Center: https://marketplace.atlassian.com/apps/1979326073/dlp-sensitive-data-pii-gdpr-scanner-cleaner-for-jira?hosting=datacenter

[Emma Phillips]

We manage a fairly large Jira/Confluence environment, and doing a manual review of old tickets, comments, revisions, and attachments isn't realistic.

We're looking at automated scanning, but we don't want the remediation process to simply delete entire tickets or pages also selection based and with complete audit around it.

Being able to identify the sensitive portion and remove only that data would be much more useful.

What approaches are you using for targeted PII detection and selective redaction?

[Karl from Ricksoft]

The "decide what you're hunting for, scan everywhere including history, keep the receipts" framework is a solid checklist . the page-history point especially, since that's the part most manual cleanups miss entirely.

One thing worth separating out: this tool (and any DLP scanner like it) is fundamentally reactive. it finds and cleans up sensitive data that already leaked into free-text fields. It doesn't stop the next person from pasting a card number into a comment tomorrow.

For teams also planning a Data Center to Cloud migration, that's where we focus: Secure Custom Fields for Jira (Cloud only, so a different layer/timeline than this DLP scanner) lets admins move sensitive fields into AES-256 encrypted, permission-scoped custom fields going forward, so the data has a proper home instead of ending up in a free-text comment in the first place. Complementary problem to the one above, not a replacement for the DC audit you'd still want to run pre-migration.

(Disclosure: I work on Secure Custom Fields for Jira at Ricksoft, so take the product mention with that in mind)

Try Secure Custom Fields for Jira free