Jira Data Protection Apps Compared: What Each One Actually Does

If personal data is already sitting in your Jira, you need a scanning-and-remediation tool. If you want to stop it being entered in the first place, you need field-level controls. If you need policy enforcement across your whole Atlassian org, that is Atlassian Guard, and it does not remove data that is already there. Most regulated teams end up combining two of the three. This page compares the options on the criteria that actually differ, including where each one is the better choice.

Every tool below is a real product with real strengths. The differences are less about quality than about which of three jobs each one was built to do.

The three categories, and why the distinction matters

1. Scan and remediate existing data. These tools search what is already stored in Jira, issue summaries and descriptions, comments, custom fields, attachments, and issue history, and then do something about what they find. This is the category you need for a GDPR right-to-erasure request or a data subject access request, because the data already exists and must be removed.

2. Protect data at entry. Field-level apps mask or encrypt values as they are typed, so sensitive data never lands in the clear. Excellent going forward. They do nothing about the twelve months of history behind them.

3. Govern the whole org. Atlassian Guard Premium applies policy, monitors activity and detects violations across all Atlassian cloud products. It is a governance layer, not a cleanup tool.

The most common buying mistake is choosing category 2 or 3 when the actual problem is category 1. Field masking and policy alerts both leave existing data exactly where it is.

Comparison

	Data Protection Toolkit	Soteri Security for Jira	Polymetis PII Protection & DLP	Atlassian Guard Premium
Primary job	Find, redact and anonymize existing data	Detect secrets and PII	Detect PII and trigger actions	Org-wide policy and monitoring
Built-in detection patterns	62	Not published	70+ types claimed	Not published
Country-specific national ID formats	32 formats across 27 countries	Not published	Not published	No
Country-specific phone formats	20	Not published	Not published	No
Custom RegEx rules	Yes, Actonic writes, tests and hands them over free of charge	Yes	Yes	Limited
Secrets and API keys	Via RegEx rules	Yes	Yes	Limited
Bulk redaction / replacement	Yes	Manual remediation	Actions such as close or notify	No
User anonymization	Yes	Not offered	Not offered	No
Right to erasure / DSAR	Yes	Not offered	Not offered	Not addressed
Consent management	Yes	No	No	No
Attachment scanning	Yes	Yes (Advanced edition)	Yes, including images	Limited
Issue history	Yes	Yes	Not stated	No
Scheduled scans	Yes	Yes	Yes	Continuous
Hosting	Cloud and Data Center	Cloud and Data Center	Cloud only	Cloud only
Cloud Fortified	No	Yes	Yes	First-party

Competitor capabilities checked against their Atlassian Marketplace listings in August 2026; “not published” means the vendor does not state a figure, not that the capability is absent. Data Protection Toolkit figures are from its shipping pattern library. Vendors change frequently, verify anything decision-critical on the listing itself.

Where each tool is the right answer

Soteri Security for Jira: best for credential scanning with no setup

Soteri’s core strength is credentials: passwords, API keys, tokens and credit card numbers, alongside PII. It scans attachments in its Advanced edition and covers historical issue versions, works on both Cloud and Data Center, and is Cloud Fortified.

Choose it over Data Protection Toolkit if your main worry is developers pasting API keys into tickets. Soteri ships credential signatures that work out of the box. Data Protection Toolkit also finds secrets and API keys, any format you can express as a RegEx rule, and, unlike Soteri, can then redact or remove them in bulk rather than only surfacing them for manual handling. The trade is prebuilt signatures out of the box versus rules you configure once and remediation that actually cleans up. Soteri also does not offer user anonymization or DSAR workflows.

Polymetis PII Protection and DLP: best for breadth of detection out of the box

Polymetis advertises over 70 types of PII and sensitive data including text inside images and documents, and can trigger automated responses such as closing an issue or notifying someone. It is Cloud Fortified.

Choose it over Data Protection Toolkit if you want the widest set of prebuilt detectors with minimal configuration and your requirement stops at detection and alerting. Two things to check first: it is Cloud only, so it is not an option on Data Center; and its published capabilities centre on detection and response actions rather than redacting or anonymizing what it finds.

Atlassian Guard Premium: best for org-wide governance

Guard is Atlassian’s own product and operates at a level no Marketplace app can reach: identity, policy and activity monitoring across every Atlassian cloud product at once. If your requirement is written as “we need visibility and policy control across the organisation,” Guard is the answer.

Choose it over Data Protection Toolkit if the problem is governance rather than cleanup. But be clear about the boundary: Guard tells you sensitive data exists. It does not remove it. It does not handle right-to-erasure or DSAR requests, does not anonymize users, and is Cloud only. Teams under GDPR or HIPAA typically need both, Guard for ongoing governance, a remediation tool for the cleanup and the erasure requests.

Field-level apps: best for preventing new exposure

Apps such as Secure Fields and Protected Fields mask or restrict values at the point of entry. That is genuinely the right tool for stopping sensitive data entering a specific custom field going forward, and if that is your only requirement you do not need a scanner at all. They do not address data already stored, including everything sitting in issue history.

Data Protection Toolkit: best when data is already in Jira

Data Protection Toolkit is built for category 1. It scans issues, comments, custom fields, attachments and issue history against built-in templates or your own RegEx rules, then redacts, replaces or anonymizes in bulk and on a schedule. Because the rule engine accepts custom RegEx alongside the built-in templates, the same scan covers credentials, API keys and tokens as well as personal data, and remediates all of it in one pass. It handles user and project anonymization for right-to-erasure and DSAR requests, and includes consent forms with acceptance tracking so you can prove consent rather than assume it. It runs on both Cloud and Data Center.

On Atlassian Cloud, scanning and calculation happen in the user’s browser: no personal data is stored or passed to third parties, and only anonymized meta and configuration data is retained.

When Data Protection Toolkit is the wrong choice

Being straightforward about this saves everyone time:

• You want secret signatures with zero configuration. Data Protection Toolkit detects credentials and API keys through custom RegEx rules and can remove them once found, but a dedicated secret scanner ships more prebuilt signatures out of the box. If you want detection working on day one with no rule-writing, that is the faster path.
• You only need to stop new data. If nothing sensitive is stored yet and you simply want one field locked down, a field-level app is simpler and cheaper.
• You need governance across all Atlassian products. That is Guard’s job, and no Marketplace app substitutes for it.

Frequently asked questions

Does Jira have built-in data masking?

No. Jira offers issue security levels and field configurations, which control who can see a field, the data is still stored. There is no native pattern-based masking or redaction, and no native way to search for personal data by pattern across issues and attachments.

How do I find personal data across a whole Jira instance?

Native Jira search matches text, not patterns, and cannot search inside attachments. Finding personal data reliably needs a tool that scans against pattern templates, email addresses, phone numbers, national ID formats, credit cards, across issues, comments, custom fields, attachments and issue history.

How do I handle a GDPR right-to-be-forgotten request in Jira?

Account-level anonymization alone is usually not sufficient. It anonymizes the user’s account references but leaves personal data that user entered into summaries, descriptions, comments, custom fields and attachments, and does not touch issue history. A complete response requires finding and removing that remaining data.

Is this a DLP tool, and how is it different from network DLP?

Traditional DLP watches traffic leaving your network. DLP inside Jira works on content that is already stored, scanning and remediating it in place. The two solve different problems and are not substitutes.

Does it work on Data Center as well as Cloud?

Yes. Note that the Cloud edition implements a subset of the Data Center modules, because of Cloud API limitations.

Where is my data processed?

On Atlassian Cloud, the app fetches data from the Cloud and performs all calculation directly in the user’s browser. No personal data is stored or passed to third parties; only meta, configuration data and object IDs is transferred. This differs from external SaaS DLP platforms, which connect via API and process your content on their own infrastructure.

Choosing in one line

If the data is already in Jira and has to come out, you need a remediation tool. If it is not there yet and you want to keep it out, you need field-level controls. If you need to see and govern everything at once, you need Guard. Work out which sentence describes your problem and the shortlist writes itself.

See Data Protection Toolkit on the Atlassian Marketplace or read the full feature breakdown.

Formerly known as Data Protection and Security Toolkit for Jira, and previously as GDPR (DSGVO) and Security for Jira. Published on the Atlassian Marketplace by Actonic Products GmbH.