As artificial intelligence (AI) accelerates across industries from financial modeling and autonomous vehicles to medical imaging and logistics optimization, one issue consistently flies under the radar: Physical security. 

While tech leaders debate models, training data and compute resources, AI’s dependency on real-world infrastructure, including racks of GPUs, cabling pathways, edge nodes and cooling systems, rarely receives the same attention.  

That’s a problem. 

Global AI infrastructure spending is projected to exceed $400 billion by 2027, with a significant portion of this investment fueling new data center construction and high-density retrofits. 

Nowhere is this transformation more visible than in San Jose and across Northern California, where AI-focused buildouts are rapidly reshaping the data center landscape.  

Yet amid this rapid growth, the physical layer remains one of the least addressed and most vulnerable parts of the stack. 

Legacy Security Postures in Rapidly Changing Spaces 

AI infrastructure is dynamic by nature. Equipment turns over frequently. Vendors rotate in and out. Floor plans are modified to accommodate higher density and increased power requirements. 

Yet physical security assessments often rely on checklists built for a different era, designed around environments that were slower-moving, more stable and far less complex. 

That approach no longer fits. 

Changes that used to unfold over months are now happening in a matter of weeks. When physical security practices aren’t updated just as quickly, they miss critical details — temporary access points, unsecured staging areas, or makeshift server spaces that fall outside standard protections. 

And it’s not just the infrastructure that’s shifting, it’s the people coming in and out of these environments, too. 

AI buildouts attract a diverse mix of personnel, including facilities staff, robotics engineers, IT teams, contractors and third-party service providers. Each of them needs access to certain spaces, but many organizations still default to shared credentials, universal badge systems, or one-size-fits-all access rights. 

The result is a lack of granular control and very little auditability. 

Human error already accounts for nearly 40% of data center outages (Uptime Institute). In AI environments, where complexity increases and more personnel interact with the hardware, inadequate access management becomes a growing liability. 

Even in well-funded AI environments, perimeter security tends to get most of the attention — camera coverage, access control at entry points, lobby sign-ins. However, once inside, assumptions take over. 

GPU clusters, high-value servers and critical networking gear are often stored in interior zones that lack adequate physical controls. Some don’t even have rack-level monitoring or logging. 

That means someone could rewire a connection, install rogue hardware, or tamper with equipment, with no record of who, when, or why. 

It’s a similar story in utility spaces. 

AI processing loads push heat output to the extreme. Modern processors, such as the NVIDIA H100 or its equivalents, can draw up to 1,200 watts under full load. Multiply that across racks and rows, and the mechanical infrastructure — PDUs, HVAC, cooling systems — becomes as vital as the servers themselves. 

But cooling rooms, electrical closets and support areas are frequently overlooked. They’re marked “low-risk” or “back-of-house,” despite being central to uptime. 

It might look like just another back room, but if it’s unsecured, it could bring down everything. 

Security Integration is the Way to Go 

In many AI facilities, safety and security still operate in separate lanes. One team handles emergency protocols and fire suppression; another manages access control and intrusion detection. On paper, that might seem efficient.  

But in the middle of a real incident, it’s a recipe for confusion. 

The reality is, AI doesn’t just run on cloud platforms and compute power. It runs in real buildings, with real equipment and real people responsible for keeping it all online. 

If we want these systems to be resilient, we need to treat the physical layer with the same attention we give to code, data and digital infrastructure.  

That means tighter coordination, better visibility, and a mindset that sees cyber and physical security as two sides of the same coin. 

Because in the end, it’s not just about protecting technology, it’s about protecting the environments that make AI possible.