The blueprint architecture for securing the AI data center

AI data center security cannot be an afterthought.

by · TechRadar

Opinion By Aviv Abramovich published 28 April 2026

Building the AI infrastructure is only part of the puzzle. Enterprises need to protect it. (Image credit: Getty Images)

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As enterprises turn traditional data centers into AI factories powered by LLMs, they’re focused on unlocking new revenue streams, competitive differentiation, and operational efficiencies. But they’re also exposing themselves to unprecedented risk.

Enterprises are no longer just leasing AI. They are producing it. According to Markets and Markets, the global AI data center market is expected to grow from ~$236B in 2025 to ~$934B by 2030 at a CAGR of 31.6%, with enterprises being the fastest-growing end-user segment.

Aviv Abramovich

VP of Product Management at Check Point.

Why are organizations building their own AI?

The main drivers leading enterprises to build their own on-premises AI data centers are the need to meet compliance and sovereign AI mandates, avoid prohibitive cloud provider costs and concerns over risk to their data and intellectual property.

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For heavily regulated industries, such as financial services and healthcare, model training requires clear audit trails and explainability. With that in mind, as AI workloads continue to rise, it becomes more financial beneficial to own the IT infrastructure, especially with the cumulative cost of cloud GPU compute often exceeding the investment in dedicated infrastructure.

For heavily regulated industries, such as financial services and healthcare, keeping model training and inference becomes a necessity. And as AI workloads scale, it becomes more financially viable to own, with the cumulative cost of cloud GPU compute often exceeding the investment in dedicated infrastructure.

New AI data centers, new needs

Organizations developing their own AI data centers contend with multiple new challenges. Whether their “AI factories” are designed for internal consumption, public use, or as a service they sell, there are several steps of the blueprint to follow.

A starting point is to transform on-premises data centers into those that can support AI training and inference through purpose-built GPU clusters, distributed inference services, and high-throughput networking.

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