PII Risk by Actor(5.4.8)

When personal data appears in Jira, someone put it there. This visual identifies which actors most frequently create or update issues whose text matches a detection pattern, so that targeted awareness training or workflow guidance can be directed at the right people.

This is not a disciplinary ranking. Most people who include personal data in Jira do so accidentally, often by copy-pasting from emails or support tickets. The goal is to identify where training will have the most impact.

What you can conclude

• An actor with a high email-arm count is the strongest candidate for targeted data hygiene guidance, because that arm is the reliable one.
• An actor whose bar is almost entirely the digit arm is usually pasting build numbers, order references or account ids. Open a couple of their issues before treating the count as an exposure.
• If detections are evenly distributed across many actors, a broad team-wide session is more appropriate than individual targeting.
• An actor with declining counts over time has responded to prior guidance.

How this chart works

Horizontal stacked bar chart ranking actors by count of events they triggered whose text matched a detection pattern, with each bar split into the email-pattern arm and the phone-shaped-digit arm. The two arms overlap: an event matching both is counted in both, so the stacked total can exceed the actor's distinct detection count. Use the date and project filters to focus on specific teams or periods.

Where a GDPR review turns to training and process, this is the view that informs it.