Alright, who else here has pulled a raw log export from Anomali and just stared at it? 😅 The amount of noise and redundant system events in there makes it a real pain to spot the actual user behavior patterns I need for UX analysis.
I kept doing this manually for my weekly reports, and it was eating up so much time. So I finally sat down and wrote a Python script to automate the cleanup. It's nothing fancy, but it's been a total game-changer for my workflow. Basically, it takes the CSV export, filters out all the predefined internal/system events (you can customize this list), standardizes some messy timestamp formatting, and groups low-frequency events into an "Other" category so the data is actually readable in charts.
The cool part is it outputs a cleaned CSV and a quick summary text file with counts. Now I can go straight into Figma with clean data to map out user flows and pinpoint where our UI might be causing confusion. It’s made my user testing prep so much faster.
If anyone wants to try it out or suggest improvements, I'm happy to share the script. Just let me know what you'd want it to do. Anyone else building little tools to bridge the gap between security data and UX work?
Oh wow, that's exactly what I need! I've been dreading those manual log reviews for our adoption reports. The idea of grouping low-frequency events into "Other" is really smart - my charts always get so cluttered.
Could you walk me through how you set up the filtering for internal events? I'm worried I might accidentally filter out something important for my use case, which is tracking feature discovery for new users.