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Dainius Ražinskas

Cybersecurity Expert · Product Engineer at NordVPN

A cybersecurity professional dedicated to helping internet users avoid evolving digital threats through simple and easy-to-use tools.

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Dainius Ražinskas at a Nord Security event

“The biggest threat to your online safety usually isn’t a hacker or virus, but a moment of distraction. Good security tools are designed to close that gap.”

About me

Dainius Ražinskas built NordVPN’s threat protection department from scratch — hiring the first engineers and assembling teams that learned threat detection by building it, and shipping it.

From the start, he stood up Threat Intelligence alongside the department, because the product needed people running the machine-learning models and curating threat feeds from day one. Since 2020, he has taken Threat Protection from a first prototype to the company’s next-gen antivirus (previously Threat Protection Pro™), combining web protection, malicious content blocking, phishing detection, and file scanning.

He came to it the long way: system administration, software development, then complex enterprise projects — SOCs, data centers, mainframe migrations — and from there into product management at startups. He drew on all of that experience to then build a security product from nothing at NordVPN. He holds ten granted patents across five patent families, covering malware detection, federated learning, browser-side execution prevention, and machine-learning threat analysis.

Early career

System administration, software development & enterprise delivery

Patents

Ten granted patents across five patent families

2020 — present

Threat Protection & Threat Intelligence, NordVPN

Expertise

Areas of expertise

While Dainius has spent his last few professional years in cybersecurity, he has developed many other skills along the way.

Building security products from zero

Founding a department and a product at the same time: hiring the first engineers, forming the teams, and taking a next-gen antivirus from concept to launch.

Threat protection strategy

Owning what the product defends against and how — network-level defenses, on-device scanning, phishing and scam prevention, post-download file analysis — and deciding where each belongs.

Applied machine learning in security

Directing how ML is used against phishing, scams, and malicious behaviour, including federated learning and model training approaches. Ten granted patents across five patent families in this space.