How do you know where your sensitive data is stored ?

@Utkarsh Chandel 

This is a very real concern, especially in Jira and Confluence environments where thousands of issues, pages, comments, and attachments are added every day.

One thing we often overlook is that once sensitive data is stored, the exposure has already started. It may be visible to project members, searchable, included in exports/backups, or passed to connected systems before anyone notices it.

For this reason, we look at DLP as an ongoing process rather than a one-time cleanup:

• Scan continuously or on a schedule to identify newly added and historical sensitive data.
• Detect PII, PHI, PCI data, passwords, API keys, tokens, and other custom patterns using predefined or custom regex rules.
• Remediate the finding by redacting, encrypting, masking, or deleting the sensitive information rather than simply reporting it.
• Scan page history and attachments as well, since sensitive information doesn't necessarily exist only in the current version.
• Maintain centralized reporting and audit logs so security teams can track what was detected and what action was taken.
• For Cloud environments, real-time scanning can help detect sensitive content as Jira issues or Confluence pages are created or updated.

This is the approach we've taken with the miniOrange DLP Sensitive Data Scanners for both Jira and Confluence:

• Jira Data Center: DLP – Sensitive Data Scanner & Cleaner for Jira
• Confluence Data Center: DLP – Sensitive Data Scanner for Confluence
• Jira Cloud: Data – PII Scanner (DLP) for Jira
• Confluence Cloud: Data – PII Scanner (DLP) for Confluence

The goal isn't just “find PII.” It's to shorten the time sensitive data remains exposed: Detect → Review → Remediate → Audit.

Curious how other teams are approaching this — do you run scheduled DLP scans, real-time detection, or a combination of both?

Hi @Utkarsh Chandel ,

Good question, and the honest answer for most orgs is "permissions and user awareness, until the first incident". A layered approach that actually works in practice:

1. Marketplace DLP apps. There is a decent selection now, and most are built on Forge, meaning the scanning runs inside Atlassian infrastructure and your data doesn't leave the instance to be scanned (which matters, a DLP tool that ships your content to a third party is itself a data transfer). Examples: PII Protection and DLP (available for both Jira and Confluence, detects 70+ data types including text in attachments and images), miniOrange Data - PII Scanner (policy-based auto-redaction and a violations dashboard), Compliance for Confluence (classification labels with access control tied to them), Data Protection Toolkit for Confluence (bulk redact/replace, user anonymization for DSAR/right-to-be-forgotten requests). They cost money but handle the boring parts: attachment parsing, scheduled scans, audit trails.

2. Custom scanning script. The DIY version: a script (any language, REST API) that walks issues via JQL in batches, pulls descriptions and comments, runs them against a set of regex masks - credit card numbers with a Luhn check, government ID formats, IBAN, email+phone combos, keywords like "password:" - and writes a report of issue keys with matches. Attachments need to be downloaded and text-extracted separately, since Jira search doesn't look inside them. The same script can purge in a second pass: edit the field to redact the match, or delete the comment/attachment.
Two caveats from experience: always run report-only first and review by a human before purging, false positives are plentiful; and remember that old values can survive in issue history and in exports/backups, so redaction in the UI is containment, not complete erasure.

3. AI-assisted classification. Regex misses context ("this looks like a diagnosis", "this paragraph contains a home address written in prose"), and LLMs are genuinely good at that. We route the suspicious candidates from the regex pass through a model that classifies "PII / not PII / needs review", which cuts the manual review pile dramatically. Same warning as with apps: sending your content to an external AI API to find sensitive data is itself a data transfer, so this only makes sense with a vendor you have a proper agreement with, or a locally hosted model. Otherwise your DLP scan becomes the leak.

I sawthat Atlassian Guard has some DLP capabilities, but haven't used it though.

The realistic workflow for a small team: run a scripted scan quarterly, and treat anything older than your retention policy as a purge candidate rather than trying to classify it perfectly.