How to Evaluate a PII or Secret Scanner for Confluence and Jira

The listings all sound the same

At least five apps now compete for the same shelf space in the Atlassian Marketplace, each promising to scan Confluence for sensitive data. Read their listings back to back and the language converges — “detects sensitive data,” “protects your organization,” “achieve compliance.” None of that tells you whether a given product will actually catch the credential your team pastes into a comment next Tuesday. We pulled what’s actually documented, publicly, on each listing into one table.

The current landscape, by the numbers

Snapshot as of August 2026, pulled directly from each app’s own Atlassian Marketplace listing. Install and review counts change continuously; treat the qualitative columns (data types, surface, architecture) as more durable than the numbers.

Two things stand out immediately. First, every single app in this category currently shows zero published reviews — that’s a category-wide pattern, not a signal about any one product specifically, and it means install count is currently the only volume signal available; nobody here has social proof yet. Second, “data types claimed” and “content surface” are stated with wildly different precision — some listings name an exact number and format, others don’t address the question at all, which is itself informative about how rigorously each vendor has documented its own product.

Six questions the table can’t answer for you

1. Which content surfaces does it actually scan? The table shows what’s stated publicly, but “pages” in one listing might mean something different from “pages” in another — ask for the exact list: page bodies, page comments, Jira issue descriptions, Jira comments, attachments. Our review of Atlassian’s own native DLP tool found this exact ambiguity matters in practice — its documented scanning covers page and blog post bodies and titles, not comments, and not most of Jira.

2. Does it name a real benchmark for its accuracy claims? Gamma’s listing states “99.5% accuracy” with no named corpus or methodology behind it. That’s not a criticism specific to Gamma — it’s the same standard peer-reviewed research on secret-detection tools applies to every vendor: an accuracy number with no stated benchmark isn’t independently checkable, and a scanner with a quiet alert queue may simply be missing real secrets rather than finding fewer of them.

3. What detection method is actually running? Detection techniques range from keyword matching through entropy scoring to ML classification, each with different failure modes. None of the five listings above disclose their underlying detection method in detail. Ask directly, and ask whether it’s a maintained, auditable engine you can test against your own examples before buying, or an undisclosed proprietary model.

4. Where does your content actually go to get scanned? Three of the five listings above don’t state their architecture at all. Apps built on Atlassian Forge run inside Atlassian’s own infrastructure; Connect-framework apps typically send content to infrastructure the vendor hosts. Neither is automatically disqualifying, but a listing that doesn’t answer this at all is a listing you have to ask directly, not infer.

5. Does it redact, or just alert? An alert-only tool tells you a secret was pasted; it doesn’t remove it. Ask whether redaction happens automatically and in place, whether it preserves formatting, and whether there’s a timestamped audit trail of what was found.

6. Is real coverage gated behind a separate paid tier? Some platforms’ native scanning ships as an add-on with its own subscription, separate from your existing plan. Know before you buy whether the specific capability you need is in the quoted price or an upsell once you’re already invested in the workflow.

Where Secret Sentinel lands on its own table — including the gap

Secret Sentinel covers pages and comments, and Jira issues and comments — a content-surface claim few competitors in the table state explicitly. Its detection methodology is documented and built on the open-source secretlint engine rather than an undisclosed model. It runs entirely on Forge, with no external server processing content.

It’s also fair to name where it doesn’t lead: Data - PII Scanner documents attachment scanning — PDF and Word files — which Secret Sentinel’s current documentation doesn’t claim. If your risk specifically involves sensitive data inside uploaded files rather than page and issue text, that’s a real, relevant difference to weigh, not something to paper over. Run the six questions above — and the table — against any product you’re evaluating, including this one, before it goes anywhere near production content.