Taming Atlassian Audit Logs: Processing messy JSON to enable operational insights

Atlassian’s audit records are data-rich, but messy. In this data-driven deep dive, Eddy Gurney from NetScout shares what it took to get them into Graylog. He walks through four pipeline approaches and why each fell short, then shows how moving parsing to the edge with Filebeat unlocked Graylog. With clean, flattened events flowing in, alerts and dashboards turn “noise” into operational visibility. You’ll also see how Sidecars makes config rollout easy, plus what changes to make if you’re on Atlassian Cloud instead of Data Center. If you wrangle nested JSON, or have ever thought “I’ll just parse it in a pipeline”, this session is for you. 00:00 – Intro: The Atlassian Audit Log Challenge 00:24 – What a Real Bitbucket Audit Event Looks Like 01:00 – Why Atlassian JSON Audit Data Is So Messy 02:27 – Graylog Pipelines Basics (Rule Builder vs Code Editor) 03:33 – Pipeline Option 1: parse_json (Readable but Still Nested) 05:19 – Pipeline Option 2: flatten_json (Flat but Fragile Arrays) 06:56 – Cleaning JSON Strings with Regex (Still Hard to Query) 08:55 – Why Input JSON Extractors Don’t Solve the Problem 09:48 – Pipeline Option 4: JSONPath Extraction for Searchable Fields 12:58 – Measuring Pipeline Cost in Microseconds 15:00 – Moving Modeling to the Edge with Filebeat Script Processor 17:12 – Stable Fields, Dashboards, Alerts, and Sidecar Deployment 26:33 – Atlassian Cloud & Guard Audit Logs + Final Takeaway #atlassian #json #audit @Atlassian