Close telemetry gaps
Improve the evidence available to analysts during an investigation.
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Case study 02 · SOC workflow optimization
Reducing incident response time by 63%
A healthcare SOC was taking roughly 24 hours to respond to incidents. I helped close telemetry gaps, improve asset classification, and connect NDR data to the team’s investigation workflow.
The goal
Turn more security data into faster decisions
The platform was producing value, but missing telemetry and inconsistent asset classification slowed investigations.
The customer needed a tighter path from network evidence to analyst action. The goal was to reduce friction in the SOC workflow and create a stronger foundation for future AI-assisted detection.
Fix asset context
Make classification more consistent so alerts could be interpreted faster.
Fit the workflow
Connect NDR data to the way the SOC actually investigated incidents.
The strategy
Improve the operating system around the platform
The response-time problem crossed product usage, data quality, and team workflow. I worked across those layers so analysts could move from signal to action with less friction.
Mapped the sources of delay
Focused on the points that slowed investigations: incomplete telemetry, inconsistent asset context, and a gap between NDR evidence and established SOC processes.
Improved data and classification
Worked to close telemetry gaps and correct asset classification so analysts had more reliable context when triaging and investigating alerts.
Connected data to the investigation path
Integrated NDR insights into the SOC’s working process, turning platform data into a more direct input for incident response.
Measured the operational outcome
Tracked the change in mean time to respond over six months, keeping the work tied to analyst performance rather than feature adoption alone.
The result
Fifteen hours removed from the response cycle
Mean time to respond fell from 24 hours to 9 hours in six months—a 63% improvement.
The improved telemetry, asset context, and workflow also prepared the SOC for AI-assisted detection by strengthening the underlying data and operating discipline.
Faster mean time to respond
Removed from the average response cycle
From baseline to measured result
What this case demonstrates
Customer success can change an operational metric when adoption work reaches beyond feature usage. The leverage came from connecting platform data, asset context, and the team’s real investigation process.
Customer name and sensitive implementation details are withheld. Results are drawn from my portfolio performance.