Data practices built for responsible AI

We’re continuously investing in ways to help your teams achieve more with our platform and this change is no different. AI is already multiplying what teams can accomplish, from agents that simplify complex workflows to enterprise search and chat that quickly surface the right context when it matters.

By learning from richer, more diverse customer data and usage patterns, we can deliver enhanced AI capabilities that unlock greater value across your organization. While we use this type of data to improve your individual company or organization’s experience today, we’re updating how we use customer metadata and in-app data to improve our apps and AI experiences for all customers.

At the same time, we know you trust Atlassian with some of your most business‑critical work, and we take that responsibility seriously, especially when it comes to keeping your data safe. That’s why we are pairing this change with new in‑app settings to give you control and strengthening our existing safeguards with de-identification and aggregation of all contributed data. This approach enables us to unlock more powerful AI experiences for all customers while keeping your data protected.

Changes to how Atlassian will use metadata and in-app data to improve apps and experiences for all customers went into effect on August 17, 2026.

Initially, data contribution settings only apply to metadata and in-app data in Jira, Confluence, and Jira Service Management, including data in your Atlassian Platform apps (Rovo, Home, Teams, Projects, Assets, Goals, Analytics, and Administration) and certain Teamwork Graph connectors that you’ve set up for your organization. You can manage these settings in Atlassian Administration at any time. Atlassian will not use metadata or in-app data from apps where no data contribution settings are available.

We’ll notify you when settings become available for data within additional apps you own, so you can review them in Atlassian Administration.

Metadata refers to characteristics about your content and insights into content that is common across customers. Metadata includes two data types referred to as content attributes and common patterns.

• Content attributes are statistical characteristics, numeric fields, and derivatives of your in-app data.
  • Examples of content attributes may include the number of story points assigned to a Jira work item or the complexity of a Confluence page.
• Common patterns are phrases, keywords, and topics we extract from search queries and results, Rovo Chat (conversations, prompts, and responses), and custom configuration data that are frequently seen across many customers, while omitting rare data that may be unique to your organization.
  • Examples of common patterns may include common words, phrases, or Rovo Chat prompt topics that are frequently used by customers, such as “vacation policy” or “recap team activity.”

Before we use metadata to improve apps and experiences for all customers, we de-identify and aggregate it. We remove information that directly identifies individuals, such as names and email addresses, and we apply controls to prevent re-identification of the data.

Metadata helps us understand how customers typically use Atlassian apps so we can make them more intuitive and efficient. For example, with these insights we can improve search relevance and identify frequent tasks across customers. You can learn more about what metadata is, how it's protected, and how it's used in the documentation.

In-app data refers to content created by users within Atlassian apps. This can include things like:

• titles of and content within Confluence pages
• titles, description, and comments of Jira work items
• custom emoji names
• custom Jira or Confluence status names
• custom workflow names

All in-app data is de-identified and aggregated when being used to improve apps and experiences for all customers. We remove information that directly identifies individuals, such as name and email addresses, and we apply controls to prevent re-identification of the data.

When enabled through data contribution settings, Atlassian may use in‑app data to improve apps and experiences for all customers. For example, we may use in-app data to identify frequent tasks to surface better recommendations or next steps in workflows. You can learn more about what in-app data is, how we protect it, and how we use it in the documentation.

Data contribution settings are new controls available in Atlassian Administration that allow you to manage how Atlassian uses metadata and in-app data to improve apps and experiences for all customers. These settings are managed at the Atlassian organization level. If you manage multiple Atlassian organizations, you'll need to review and manage the data contribution settings for each one separately.

Initially, these settings will apply to metadata and in-app data in Jira, Confluence, and Jira Service Management, including data in your Atlassian Platform apps (Rovo, Home, Teams, Projects, Assets, Goals, Analytics, and Administration). We will also provide these settings for certain Teamwork Graph connectors that you set up for your organization. We will not use metadata or in-app data from apps that do not have data contribution settings available.

Data contribution default settings follow the highest active plan in each Atlassian cloud organization.

Some Atlassian cloud organizations are excluded from data contribution due to their compliance requirements, so default settings are not available.

Visit Atlassian Administration to see your organization’s specific data contribution settings.

Yes, if you’re an organization admin, you can change the data contribution setting for in-app data. Visit Atlassian Administration to see your organization’s specific data contribution settings.

Please note: some Atlassian organizations, including those with certain compliance requirements, may see different settings in Atlassian Administration.

If your Atlassian organization has an active Enterprise plan, you can opt-out of metadata contribution.

If your Atlassian organization’s highest active plan is Free, Standard, or Premium, metadata contribution is always on, and you’re not able to opt-out.

All metadata is de-identified and aggregated before it is used to improve apps and experiences for all customers. We remove information that directly identifies individuals, such as names and email addresses.

You can learn more about what metadata is, how it's protected, and how it's used in the documentation.

Please note: some organizations, including those with certain compliance requirements, may see different settings in Atlassian Administration.

When customers contribute metadata and/or in-app data to improve apps and experiences for all customers, we apply existing safeguards with additional privacy-preserving measures, including:

• All metadata and in-app data is de-identified and aggregated before it is used to improve apps and experiences for all customers. We remove information that directly identifies individuals, such as names and email addresses.
• We go one step further for common patterns, where we extract phrases, keywords, and topics only from search queries and results, Rovo Chat (conversations, prompts, and responses) , and custom configuration data frequently seen across customers. We omit data that is low in frequency and may be unique to your organization.
• We limit how Atlassian teams can access contributed in-app data, with robust monitoring practices. When improving features across customers, our teams only access in-app data you choose to contribute after it has been de-identified and aggregated.
• If you contribute metadata and/or in-app data, we may also extract common phrases, keywords, and topics from content attributes and in-app data frequently seen across customers. Similar to common patterns, we omit data that is low in frequency and may be unique to your organization, so that these insights are no longer associated with individual customers.

Additionally, every customer has the option to opt out of contributing in-app data.