Identity that feels human

persona

A unicorn startup for identity infrastructure

Persona is an identity infrastructure platform that helps businesses verify users and manage identity-related workflows. It works with companies like OpenAI, Anthropic, and LinkedIn, and is trusted by 3 of the Fortune 5.

overview

Making Persona’s Event Timeline easier to scan and investigate

The Event Timeline gives customers and internal teams a chronological history of activity, helping them investigate cases, debug issues, and understand how something reached its state.

Every identity leaves behind a trail of events

These are a few examples of actions in Persona that can trigger events:

problem

A tool meant to clarify history had become too inconsistent to quickly scan and understand

As Persona grew, new event types were added with different layouts, metadata, and levels of detail. These inconsistent structures made the timeline difficult to scan, forcing users to read events individually.

research

I audited the timeline to understand what was slowing users down

I mapped existing event types and compared how each displayed titles, timestamps, actors, links, object references, and supporting details.

Challenge 1: Key information was hard to scan

Users often had to read an entire event to find basic information like what happened, when it happened, or what object it belonged to.

Challenge 2: The same information appeared differently across events

Titles, timestamps, actors, and object references changed location, or disappeared entirely, depending on the event type. Without a predictable hierarchy, every event required a slightly different reading pattern.

strategy

A shared hierarchy made events easier to scan

To make the timeline more consistent without limiting different event types, I set three principles for the redesign:

mvp solution

I unified event cards under one consistent hierarchy

I created a consistent hierarchy across event components. Here are a few examples of how titles, timestamps, metadata, links, and supporting details were standardized.

The system scaled across different event types

I mapped the structure across major changes, minor changes, metadata, long-form content, and edge cases. Below is a before-and-after comparison of case field(s) updated.

post-mvp

The MVP improved the timeline, but I saw a larger opportunity

Standardizing the cards made events more consistent and easier to scan. But the timeline was still a long vertical feed, which limited how quickly users could navigate larger histories.

Other enterprise tools surfaced event history as structured data

I looked at products like GitHub and other audit/debugging tools and noticed they used structured tables to handle dense event history.

exploration

Exploring different ways to structure the timeline

I tested several formats for organizing event history, including horizontal timelines, swimlanes, workflow trees, and tables.

final solution

A table offered the clearest way to scan, compare, and navigate large volumes of event data.

A table surfaced repeated attributes in consistent columns, making events easier to scan and compare while creating room for sorting and filtering.

A structured timeline also opened the door to filtering

Once event attributes were consistently surfaced, users could narrow the timeline by event type, actor, object, or workflow instead of scanning the full history.

reflections

I improved the timeline for today while designing toward a more scalable future

Here are a few takeaways I’m carrying forward from this project: