During my time at LevelBlue (formerly AT&T Cybersecurity), I worked on large-scale redesigns of our enterprise security platform. My focus included unifying multi-tenant workflows, introducing AI-assisted features, and improving investigations, playbooks, and accessibility through dark mode.

AT&T Cybersecurity → acquired by LevelBlue

Due to my NDA, I can only share process work and high-level flows here. Full Case Studies are available via the password protected button.

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Pain Points

  • Disjointed Workflows – Analysts had to switch between multiple tenant environments, slowing down investigations

  • Lack of AI Support – No AI assistant to help automate repetitive tasks or guide workflows

  • Inconsistent User Experience – Visual and structural inconsistencies made navigation complex and reduced efficiency

  • Accessibility Issues – The absence of dark mode and limited customization options affected usability for many analysts and customers

  • Tool Fragmentation – Analysts often relied on external tools because the platform could not fully support their workflows

Process

User Research
 I conducted interviews and usability tests to better understand analyst workflows and pain points. This revealed challenges with speed, too many tools, and difficult navigation.

Flows
 I created high-level, abstract flow diagrams to visualize how analysts moved across tenants and tools. These diagrams made the complexity of current workflows clear and guided opportunities for streamlining.

Surveys
 I distributed surveys to a broader analyst group to validate findings at scale. The survey results highlighted common frustrations with disjointed workflows and confirmed the need for unified tenant management and AI support.

Solution

  • Unified multi-tenant workflows into a single platform to simplify management and reduce analyst friction

  • Introduced an AI assistant to support users in multiple sections of the platform

  • Added comparison features so analysts could quickly identify patterns across events and investigations

  • Delivered dark mode for improved accessibility, usability, and visual consistency

  • Redesigned investigations and playbook flows to make complex tasks easier to follow and execute

  • Improved information architecture to create a more consistent and scalable structure across the platform

  • Enhanced search and filtering capabilities to help analysts find the right data faster

  • Validated designs through research and testing, including surveys, usability studies, and A/B experiments, to ensure solutions met analyst needs

Incorporated Designs

This flow shows an example of how I integrated AI to help users search for alarms. The AI generates results directly in the chat, along with a supporting table (table not shown here due to NDA).

This flow highlights my design work on the Playbooks App Actions section of the platform. It demonstrates how users can fill out and add actions (other playbook details are not shown due to NDA).

Reflection

This project taught me the importance of balancing user needs with enterprise complexity. Even though not all designs are live yet, my research and testing shaped the product roadmap and strengthened my skills in multi-tenant workflows, AI integration, and usability validation at scale. If I had more time, I would integrate analytics to measure ongoing impact.

Thank you for taking the time to view my work