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