Visualizing Next-Gen Server Architecture
Client
FORTIFY 24x7
Year
2024
I'm partnering with KYMA to showcase how their AI-powered solutions optimize server performance. The challenge was to visualize complex server architecture concepts in an engaging way, helping IT professionals understand KYMA's innovative approach to CPU optimization.
The design system uses 3D geometric visualizations to illustrate server processes and performance metrics. Through interactive demonstrations and dynamic animations, users can see how KYMA's AI technology adapts and improves server architecture in real-time. This helps technical decision-makers clearly understand the platform's impact on their infrastructure.
Scope of Work
The Gap I Found
Fortify was generating significant intelligence across every service line—assessment outcomes, monitoring signals, and and incident timelines. All tracked. None of it reached the people who needed to act on it.
Three things stood out:
Technical outputs were staying technical. Security data wasn't being translated into business language. Decision-makers were receiving metrics, not intelligence.
Commercial and operational KPIs weren't aligned. The metrics that mattered for security operations were completely different from the ones driving client confidence and growth. No bridge existed between them.
Reporting was manual in a 24×7 environment. Insights were arriving late. Decisions were being made on yesterday's picture.
Research & Insights
I focused on how intelligence was flowing — and where it was stopping short of becoming useful.
Three patterns became clear:
Each audience needed something different. Clients needed proof of protection. Executives needed commercial performance. Marketing needed differentiation. All three were receiving the same raw output.
The translation layer had no owner. Data was being collected. The space between collection and communication was completely unstructured.
Speed was the real problem. In a business that never sleeps, a manual reporting process was already behind before it started.
The Strategy I Defined
I came in at the requirements stage—before the pipeline was built. My role was to define what the data needed to deliver for the business.
I mapped the questions that mattered across client reporting, internal performance, and marketing intelligence. Establish which outputs are needed to surface and in what format. Designed the reporting hierarchy so each audience received intelligence built for them—not interpretation left to them.
Delivered in collaboration with a data engineering specialist handling Azure Data Factory pipeline execution and database architecture.
Results
Requirements locked before technical execution — no rebuilds, no misalignment
KPI framework aligned operational and commercial reporting for the first time
Client-facing intelligence restructured around outcomes, not activity
Automated reporting reduced manual cycles across the reporting chain
Marketing gained a consistent view of service performance to inform positioning
Reflection
In cybersecurity, the product is trust. Every data point Fortify generates is proof of that trust being earned — but only if it's communicated clearly and consistently.
What I'd push further: connecting client reporting outputs directly into marketing content. The proof points were already there. They just weren't being used as a brand asset.
The Gap I Found
Fortify was generating significant intelligence across every service line—assessment outcomes, monitoring signals, and and incident timelines. All tracked. None of it reached the people who needed to act on it.
Three things stood out:
Technical outputs were staying technical. Security data wasn't being translated into business language. Decision-makers were receiving metrics, not intelligence.
Commercial and operational KPIs weren't aligned. The metrics that mattered for security operations were completely different from the ones driving client confidence and growth. No bridge existed between them.
Reporting was manual in a 24×7 environment. Insights were arriving late. Decisions were being made on yesterday's picture.
Research & Insights
I focused on how intelligence was flowing — and where it was stopping short of becoming useful.
Three patterns became clear:
Each audience needed something different. Clients needed proof of protection. Executives needed commercial performance. Marketing needed differentiation. All three were receiving the same raw output.
The translation layer had no owner. Data was being collected. The space between collection and communication was completely unstructured.
Speed was the real problem. In a business that never sleeps, a manual reporting process was already behind before it started.
The Strategy I Defined
I came in at the requirements stage—before the pipeline was built. My role was to define what the data needed to deliver for the business.
I mapped the questions that mattered across client reporting, internal performance, and marketing intelligence. Establish which outputs are needed to surface and in what format. Designed the reporting hierarchy so each audience received intelligence built for them—not interpretation left to them.
Delivered in collaboration with a data engineering specialist handling Azure Data Factory pipeline execution and database architecture.
Results
Requirements locked before technical execution — no rebuilds, no misalignment
KPI framework aligned operational and commercial reporting for the first time
Client-facing intelligence restructured around outcomes, not activity
Automated reporting reduced manual cycles across the reporting chain
Marketing gained a consistent view of service performance to inform positioning
Reflection
In cybersecurity, the product is trust. Every data point Fortify generates is proof of that trust being earned — but only if it's communicated clearly and consistently.
What I'd push further: connecting client reporting outputs directly into marketing content. The proof points were already there. They just weren't being used as a brand asset.
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