TyroneGowerImproving asset discovery
Boosting user engagement by 50%
Project Overview
Brand Trust uses DNS, WHOIS and SSL metadata from a domain to create Identities. Those identities can then be used to discover domains our client potentially own, that haven't yet been found, or have been parked or forgotten and left unsecured (shadow IT)
The Problem
Users were either unaware of this feature or uncertain about how it worked, leading to low engagement and underutilization
The Solution
We repositioned the feature in a more visible and high-traffic area while integrating AI-driven guidance to educate users on the Why, How, and Next Steps based on the discovered data
Increase in Engagement
Engagement and user enablement increased by 55%, demonstrating improved awareness and usability
Research & Discovery
Ideation and Brainstorming
I facilitated a brainstorming session with key stakeholders to align on critical aspects of the feature, including its goals, existing pain points, potential technical challenges in AI integration and development, gaps in our knowledge base, and additional data that could enhance user support.
The objective was to ensure a shared understanding among all stakeholders and establish clear takeaways to drive the next steps effectively.
Defining the Problem: How Might We Statements
Building on the insights gathered from the workshop, I synthesized key discussions into a set of 'How Might We' (HMW) statements, framing the core challenges that our solution needed to address. These statements served as a strategic foundation, ensuring that our design approach was user-centered, aligned with business objectives, and focused on measurable success. By clearly defining these problem statements, we were able to guide ideation, prioritize features, and create a solution that effectively tackled the identified issues.
User Flow
The Design Process
Quick Sketches: Exploring Initial Concepts
To kickstart the design process, I began with quick sketches to visualize potential layouts and key features. This low-fidelity approach allowed me to rapidly explore different design possibilities, ensuring flexibility in experimenting with structure, user flow, and functionality. By sketching out rough ideas early on, I was able to identify opportunities, iterate efficiently, and lay the groundwork for more refined wireframes and prototypes.
Wireframing: Refining Ideas into Structured Concepts
Building on the initial sketches, I transitioned into wireframing to refine and structure the most promising ideas. This step allowed me to define the layout, hierarchy, and interactions in greater detail while ensuring alignment with user needs and business objectives. By creating wireframes, I was able to visualize user flows, identify potential usability challenges early on, and establish a strong foundation for prototyping and further design iterations.
AI and Recommendations Modal
I redesigned the experience of how users receive AI-driven recommendations when scanning WHOIS metadata for domain identities. Previously, the interface only provided a minimal suggestion, which didn’t give users enough context about why the recommendation was made or how it could benefit them.
To solve this, I designed a more informative popover that clearly explains:
- What was discovered: The registrant name linked to the scanned domain
- Why it matters: The AI model validates the identity and provides context about the organization, helping users understand its relevance
- What action users can take: By enabling the identified entity, users can automatically discover additional related assets or potential shadow domains that may require monitoring
This enhanced design makes the recommendation feel more credible, actionable, and aligned with Brand Trust’s goal of giving users clarity and control over their domain security. It also balances AI transparency (showing what the model found and why it’s important) with user empowerment (allowing them to act or dismiss).
The end result is a recommendation component that not only improves trust in AI outputs but also directly supports the user’s workflow by making complex security insights understandable and actionable.
Initial Identity modal
Updated Identity Modal
Exploration: Identity Details Page
I explored the concept of introducing a dedicated details page for each identity. However, through this exploration, we determined that there wasn't enough relevant data to justify a full-page layout, leading us to reconsider the approach for presenting identity-related information more effectively.
The Final Design
The final solution effectively addresses all the questions outlined in the How Might We (HMW) statements. By repositioning identities to a more logical and visible location, users can now easily access and engage with the feature. A summary of key data points allows users to filter and refine the information efficiently. Additionally, AI-driven recommendations analyze metadata to highlight why an identity could be considered an asset. Finally, we introduced a dedicated column displaying the number of lookalikes discovered when an identity is enabled, providing users with deeper insights and actionable data.
