Data-heavy dashboard redesign for the OPENVAS Security Intelligence Platform

Impact at a glance

• Introduced new dashboard layouts, improving user segmentation and laying the base for a permission-based product architecture
• Designed and delivered 4+ new data visualization widgets and redefined the existing ones, enabling better analytical capabilities
• Implemented a standarized widget template, reducing UI inconsistencies and creating a foundation for future dashboard features
• Defined a navigation concept to switch between dashboards (<3 clicks between views), reducing workflow friction and improving efficiency

My role

I was responsible for the design direction of the project, from early exploration to final interface design. I worked closely with a product owner and a front-end developer to define the information architecture, iterate quickly on design decisions, and establish a scalable foundation for future feature improvements.

To inform the design, I conducted benchmarking of similar security platforms, audited the legacy platform for UX improvements and leveraged existing personas and internal expert knowledge to translate complex requirements into an intuitive dashboard workflow.

Context and challenge

OPENVAS is a family of IT-security products used by organizations to detect and remediate vulnerabilities across complex IT infrastructures. While the dashboard had already been migrated to a new UI framework, its underlying structure was still based on a legacy model.

Critical analytic metrics were missing, trend data was fragmented across views, and there was no clear differentiation between user roles. As a result, users struggled to quickly understand their overall security posture and prioritize actions effectively.

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Design decisions and outcomes

The redesigned dashboard delivers value across three key dimensions:

1 – Improved clarity for decision making

Historical trends and new widget formats with improved visual hierarchy help users quickly understand their security posture. Key metrics, trends, and risk indicators are easier to interpret, reducing cognitive load and supporting faster decisions.

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2 – Support for diverse user needs

Configurable widgets and different dashboard views allow users to focus on what matters most to their role, from high-level risk overviews for managers to detailed insights for security analysts.

The first release deliberately balanced flexibility with simplicity, creating a solid foundation for future personalization without overcomplicating initial workflows.

3 – Strategic alignment with long-term product direction

A standardized widget structure reduced inconsistency and prepared the platform for scalable dashboard functionality, including user-generated views in later phases. This approach improved design and development speed and lowered the effort required to introduce new data visualizations.

Clear hand-off documentation and a responsive design ensured that the dashboard is future-proof and easier to maintain as the platform evolves.

Integrating new technologies to improve the development process

This project was also an opportunity to experiment with AI-supported workflows beyond traditional design tools.

Alongside Figma for design and prototyping, I used Figma Make during early exploration to quickly generate alternative layout directions and test structural ideas before refining them manually. During live sessions with the product owner, we used Lovable to iterate on ideas together in real time, testing hypotheses faster than in traditional review cycles.

On the development side, we explored a Cursor–Figma MCP integration test to support the implementation of new dashboard widgets. While still experimental, this approach aimed to reduce translation friction between design and code. This process helped us better understand where AI tools can meaningfully accelerate iteration.

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What’s next: enabling user-generated dashboards

As design began two sprints ahead of development, I explored the evolution of this feature by:

• Designing a dashboard management dialog
• Adding a structured widget selection flow
• Creating an empty state concept that supports first-time setup

Future steps include expanding personalization options, and continuously validating analytics patterns with real usage data.

This project reinforced the importance of designing for complex, expert-driven products without oversimplifying the domain. Working with limited research resources highlighted the value of leveraging internal knowledge, validating early with experts, and staying closely aligned with development constraints.

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