Data Security at Superna
I led the redesign of our flagship product, Data Security Edition, owning research and design while partnering closely with designers, product managers and engineers. The redesign became a key differentiator in competitive deals, supporting customer retention and future growth.
1.6 hrs
per day given back to security teams
57%
reduction in false positive investigation time
92%
reduction in time to comprehend a threat
Timeline
April 2023 to February 2026
Team
Context
We were losing customers to better designed data security products.
In 2023, our sales team repeatedly heard the same feedback: the interface felt dated. Existing customers were also missing critical threats due to interface friction. If this continued, we'd lose our position as a leader in the enterprise security market.
Discovery
I worked with our PMs to interview storage administrators and IT managers to understand how they investigated threats, made decisions under pressure, and handed off work across teams.
Our PMs facilitated cross-functional workshops. I synthesized those findings into personas, journey maps, and affinity diagrams that aligned the team around where the investigation experience was breaking down and where our efforts would have the biggest impact.
Definition
The experience felt...
Dated
The visual design looked dated and reduced trust in the product capabilities.
Disorienting
Recovery and investigation work required switching between too many windows.
Disconnected
Users had to piece together information about a single threat across multiple windows.
Overwhelming
High mental load reduced attention available for higher-risk threats.
Design Challenge #1
Investigating a single threat meant juggling 7 windows at once.
The previous interface scattered threat information across 7 different windows. Users had to click through multiple layers and mentally piece together the story.
First Iteration
Consolidating Information
Security teams responded positively, but revealed new problems:
Feedback
- “What's the recommended workflow?”
- “Recovery manager gives me the most details when investigating.”
- “I almost missed that there were no snapshots available. That's the first thing I need to know.”
Second Iteration
Reducing Noise
Security teams provided more feedback to further improve the design:
Feedback
- “This is more organized, but I'm seeing details I don't need unless I'm doing a deep investigation.”
- “The rest is not required until I decide to dig in.”
Synthesis
Users didn't want to fully commit to the details page for something that will most likely not be a threat. This led to the slide-out panel pattern. It allowed users to preview key information and take action if needed. They only need to dig deeper when it looks suspicious. This insight became the basis for the slide-out pattern.
Outcome
The slide-out became the core pattern for surfacing contextual information across the platform.
The slide-out created a middle stage before committing to deeper investigation that didn't exist before. It introduced progressive disclosure, which became key to how we thought about information across the platform. Other teams adopted it in other contexts, like the Alarms and Licensing pages.
Design Challenge #2
Users did not understand why a threat was flagged as abnormal.
We used cryptic labels, like Threat_Detector_07, to tell users why we flagged behaviour as abnormal. Users had to make an educated guess as to what happened.
Proposed Solution
Simplifying language
The simplest fix was to replace the cryptic labels with plain-language descriptions of what each detector caught.
Stakeholder Concern
“Describing each detector could give competitors an idea of how to replicate our system.”
Before
- Message doesn’t tell users what happened
- Forces users to figure it out on their own
- Users have to memorize the detector numbers
Improved
- Message is clear and specific when explaining what happened
- Users can understand without leaving the window.
User Advocacy
From many customer conversations, I learned that these labels were either ignored entirely or only understood by long-time users who had memorized them through experience. I used those research findings to build alignment across teams and replace the cryptic detector labels with plain-language descriptions.
Outcome
Cut the time to understand a
threat by 92%.
Users had the context they needed to understand a threat without digging for it. This saved users at least 4 minutes when investigating every threat. It removed a hidden barrier, because they no longer had to memorize internal labels.
Process Innovation
Our Figma prototypes couldn't keep up with our product's complexity.
Tasks like file browsing and threat investigation required realistic interactions that Figma prototypes couldn't recreate. We were also maintaining three separate prototypes for three different audiences. That became unsustainable for a two-person design team.
AI Prototyping
To solve this, I changed our prototyping workflow with the help of AI. I started on my own, treating it as an experiment, turning Figma screens into functioning code. This allowed me to research the best approach before involving the team.
I rebuilt our front end in React and styled prebuilt components using Tailwind CSS to match our existing product implementations. After validating the approach, I brought it to my team, and we opened a shared repository to continue iterating on the approach.
Prototype Library
One prototype, three audiences
I created a versioning feature that allowed us to use the prototype to serve different audiences. A single prototype could show three different states of the product:
- Now: Used with developers to show how a feature should look and behave in production.
- Next: Shared during customer calls for UAT and beta feedback on upcoming features.
- Future: Used in strategic discussions to show where the product was heading.
Future Exploration
Experimental features
I tested high-fidelity concepts for new features with 8+ customers. Customers began asking much more specific questions than what I got in Figma demos. I could put different directions in front of users and get meaningful feedback.
Outcome
We got better, more meaningful feedback from our AI prototypes.
Prototypes felt closer to the real product, and the feedback I got from them was better and more actionable. Customers could respond to workflows that felt real, giving us earlier and more actionable feedback. This meant we could test upcoming and experimental features with fewer rounds of validation.
Successful Partnership
Building a flexible design system with Dell
We needed to modernize while meeting strict Dell partnership requirements. Rather than maintaining two separate design languages, the design team collaborated with Dell's design team to create one interface supporting both brands with minimal changes.
The Collaboration
Dell's team joined us in calls to review components, layout, typography, colour, and dark and light mode variations. This was an iterative process to arrive at a system that met both needs.
Documenting Patterns
Hyperion design system
I documented core components like buttons, dropdowns, and slide-out panels as system patterns that could flex between Superna and Dell's brand requirements. Each component and colour was built once and designed to work for both.
Outcome
One design system modernized the product while preserving the Dell partnership.
The modernized look gave the product a visual our customers felt had been missing, but more importantly, it instilled trust in our product that was previously missing.
Impact
The redesign became a key factor in customer retention and a differentiator in competitive deals. Security teams saw significant reductions in time spent investigating threats.
It shipped because the team was quick and scrappy when it came to designing, validating with users, and making trade-offs when necessary. I balanced design quality with engineering and business constraints.
Reflections
The AI prototyping process changed how I think about the space between design, engineering and product management. Getting concepts into code earlier made all of our processes faster. When I started at Superna, we utilized the Double Diamond approach and after AI was integrated into our processes, we fundamentally changed how we validated ideas. This is an area that I’m still understanding and exploring.
Working within a two-person design team on a complex platform improved my ability to prioritize. I learned to move fast through iterations rather than always waiting for the perfect design.