How We Built a Team of Top Cybersecurity Talent

When I committed to making Netenrich a data-first, intelligence-first security company in 2018, I did something that made some people question my judgment: I decided that the team I needed would not look like a typical security company.

A typical security company is staffed primarily by traditional security practitioners - threat analysts, incident responders, SOC engineers, penetration testers. But they were fundamentally insufficient for the infrastructure I wanted to build.

Traditional security metrics value headcount over system capability. But when an adversary scales their operations using automated AI attack paths, simply adding more analysts to a legacy triage queue is a losing strategy.

Building a system that genuinely applied data science to security, machine learning on behavioral telemetry, NLP on threat intelligence, graph analytics on relationship data, LLMs and agents on top of everything, required a genuinely different composition. We had to redefine what top cybersecurity talent actually means.

1. Security Practitioners Who Think in Data

Security practitioners who thought analytically. Not just analysts who had learned to use data tools, but people who could translate security problems into data science problems and evaluate whether data science solutions were actually solving the right things. These people are rare. They combine deep security domain knowledge with quantitative curiosity, and they are often more valuable than ten conventional analysts because they see things conventional analysis cannot.

2. Data Scientists Obsessed with Domain Context

Data scientists with genuine curiosity about security as a domain. Not people applying familiar techniques to a new application area, but people who became genuinely interested in the adversary, the defender, and the specific analytical challenges that security presents. Data science that ignores domain context produces technically sophisticated answers to the wrong questions.

3. Data Engineers Built for Petabyte Scale

Data engineers who understood petabyte-scale systems. The data foundation of the Resolution Intelligence Cloud — ingesting telemetry from hundreds of enterprise environments, normalizing it into a unified data model, enriching it continuously, making it available for analytical workloads at scale - required serious infrastructure engineering. This category of cybersecurity talent is consistently underinvested in across traditional security organizations, yet it is entirely foundational to everything else.

4. SaaS Practitioners Who Have Shipped at Scale

SaaS practitioners who had shipped production enterprise products. Knowing what production quality actually means, reliability, observability, customer support, the thousand things that separate a working prototype from a system enterprises depend on for mission-critical operations.

The Talent Gravity: Mission Over Compensation

Finding people who fit these profiles and who were willing to join a bootstrapped company building something ambitious took clarity of mission above all else. I could not offer them the compensation packages of well-funded competitors or the brand recognition of established vendors. What I could offer was a genuine problem worth solving and the conviction that we were approaching it the right way.

What I found is that intellectually ambitious people — the ones you actually want, are attracted to clarity of mission more than to brand or compensation. They want to work on something that matters, with people who understand why it matters. The thesis was clear: build the data foundation for genuine security intelligence, apply the full data science toolkit to it, and create something that gets smarter every day it operates.

The Cross-Disciplinary Dividend

The team that came together has been building and refining this for six years. Many of them have become something rare in any organization: deeply cross-disciplinary. Our security practitioners understand the ML models. Our data scientists understand the adversary. The fluency that has developed through years of working on the same hard problem is what produces the quality of what we build.

I am grateful for every one of them. Building a cause rather than just a company — creating something people genuinely want to be part of, is the most important thing a founder can do. The technology, the market outcomes, and the elite cybersecurity talent always follow the team.

*Part of my ongoing series on data science and the future of security operations.*

 
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