The blueprint architecture for securing the AI data center

Pro The blueprint architecture for securing the AI data center Opinion By Aviv Abramovich published 28 April 2026 AI data center security cannot be an afterthought. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Building the AI infrastructure is only part of the puzzle. Enterprises need to protect it. (Image credit: Getty Images) Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter 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 AbramovichSocial Links NavigationVP 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.Article continues below You may like Securing AI infrastructure is critical – here's how to do it Confronting AI’s data privacy paradox Inference pushes AI out of the data center 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 needsOrganizations 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. window.sliceComponents = window.sliceComponents || {}; externalsScriptLoaded.then(() => { window.reliablePageLoad.then(() => { var componentContainer = document.querySelector("#slice-container-newsletterForm-articleInbodyContent-xLdLG39MvEcLpeuo243DrE"); if (componentContainer) { var data = {"layout":"inbodyContent","header":"Are you a pro? Subscribe to our newsletter","tagline":"Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed!","formFooterText":"By submitting your information you agree to the <a href=\"https:\/\/futureplc.com\/terms-conditions\/\" target=\"_blank\">Terms &…

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