AI May Feel Digital, but Its Heart Beats in Steel and Power

When most of us think about artificial intelligence (AI), we envision algorithms, learning loops, and vast data models optimizing themselves in the cloud. But behind those digital abstractions sits a very physical foundation: the global network of data centers powering AI’s rapid evolution.

These facilities depend on immense quantities of electricity, industrial cooling fluid, and water flow as well as pumps, turbines, and power-distribution systems. If any of these components falters, even the most advanced AI models can grind to a halt.

The Industrial-Scale Demands Behind Modern AI

The AI surge has driven an unprecedented investment in infrastructure – one of the largest and fastest expansions in the history of modern technology. Major tech providers are pouring resources into building and maintaining data centers, whose scope rivals national infrastructure.

A recent Reuters report asserts that investment in AI, of which infrastructure is by far the largest expense, not only exceeds the capital put into other recent technology booms but also surpasses government-led initiatives like the Manhattan Project and the Apollo program to land a man on the moon. In 2024, 137 new hyperscale data centers came online, and the tally for 2025 is expected to be similar. According to McKinsey estimates, “companies across the compute power value chain will need to invest $5.2 trillion into data centers by 2030 to meet worldwide demand for AI alone.”

But despite their futuristic aura, the data centers behind our AI obsession function much like any industrial plant, transforming raw materials into valuable commodities. Only, in this case, it’s raw data that is processed, transformed, and delivered as high-value insights for use by individuals, organizations, and even governments.

To keep data center operations running continuously, the underlying operational technology (OT) systems – temperature regulation, electrical distribution, water management, and a wide range of mechanical controls – must be monitored and protected at all times. A cyberattack or other incident disrupting a single cooling valve could shut down an entire facility within minutes, causing enormous financial damage and potential safety risks for any on-site personnel.

AI Data Centers Are the New Critical Infrastructure

Society has long recognized the importance of safeguarding power grids, water utilities, and transportation systems. AI data centers now warrant the same classification as critical infrastructure. Every clinical-diagnostics model, autonomous-vehicle guidance system, supply-chain planner, and real-time analytics platform depends on their uninterrupted operation – and that dependence is only growing.

As AI workloads spread across continents, the reliability of each individual data center becomes even more important. Many AI models now run in distributed environments, where training, storage, and inference are handled across multiple sites. A single outage can interrupt those workflows, slow development cycles, and disrupt services that millions of people rely on every day.

This interconnectedness means that a weakness in one facility can create ripple effects far beyond its physical location. Strengthening security at the data center level is therefore not just about protecting a single site – it’s about supporting the resilience of the entire AI ecosystem.

When an AI data center fails, the disruptions cascade across industries. Customer service platforms stall, healthcare systems lose access to real-time insights, and digital productivity tools degrade or disconnect entirely. The security and reliability of AI’s physical infrastructure have direct national, economic, and operational consequences.

These facilities also rely heavily on remote engineers, contractors, and vendors. Many data centers are located in rural areas far from population centers and are serviced remotely by specialists who maintain and repair equipment or perform other vital tasks. This remote connectivity improves efficiency and minimizes downtime, but it also expands the attack surface for malicious actors. For all of these reasons, every remote connection into a data center must be authenticated, monitored, and tightly controlled – just as it would be for an electric grid or water-treatment facility.

At this point, the question is not whether AI data centers should be considered critical infrastructure, but whether we can afford to treat them as anything less.

Where Technology Meets Industry: Core OT Security Practices

Because so much depends on AI uptime, data centers must apply OT remote-access security best practices consistently and rigorously.

Key principles include:

1. Enforce Zero-Trust Access: The zero-trust security model is based on the principle, “never trust, always verify.” When it comes to data center access, every identity – whether a remote technician or an internal automation workload – should be authenticated and then granted access only to the resources needed to do their job. This limits lateral movement and prevents unauthorized access to critical industrial controls
2. Apply Multi-Factor Authentication (MFA) Everywhere: MFA is one of the most effective defenses against unauthorized access. By requiring two or more independent verification factors – such as biometric authentication, one-time tokens, or physical security keys – MFA strengthens identity assurance and reinforces broader zero-trust strategies. For the most comprehensive protection, organizations should look for solutions capable of retrofitting legacy OT systems to support MFA without disrupting operations.
3. Adopt Supervision and Logging Capabilities: Traditional remote access tools like VPNs and jump servers focus on securing the initial point of access but provide no visibility once the connection is established. More modern Secure Remote Access (SRA) solutions include oversight capabilities that allow for greater control and accountability. Supervised access, just-in-time access, session recording, and detailed session logging significantly reduce risk and help meet compliance requirements.

Together, these measures strengthen security across the digital and physical systems that keep data centers and the AI infrastructure within them operating smoothly around the clock.

Securing the Industrial Heartbeat of AI

As AI becomes more deeply embedded in the operations of businesses, governments, and critical services, the reliability of its underlying infrastructure takes on new strategic importance. Data center operators must prioritize identity-driven access controls and modern OT security practices or risk significant operational disruptions.

By protecting the physical systems that keep facilities running – chillers, turbines, substations, and mechanical controls – and by securing the remote connections used to maintain and service them, operators can ensure that AI remains an accelerator of innovation rather than a new point of vulnerability.