Comparing a traditional facility to a modern AI-powered campus can feel like comparing a reliable family SUV to a Formula One race car. Both are designed to perform, but one operates under more demanding conditions, carries a significantly higher price tag, and leaves far less room for error.

A modern AI campus often features thousands of GPUs, high-density racks, advanced liquid cooling, dedicated substations, and large-scale power infrastructure. Industry estimates say these facilities can cost two to four times more per megawatt to build and operate than traditional hyperscale environments.

Today’s AI facilities are not just larger versions of yesterday’s data centers; they are, in many ways, fundamentally different assets.

While the economics and technical demands have changed dramatically, many data center operators still approach physical security – and the technology used for it – through a lens of enhancing perimeter protection, access control, and compliance.

Sure, those capabilities remain essential, but they are no longer sufficient on their own. As organizations race to build AI capacity, two important questions are rising:

• Have physical security approaches adequately evolved to match the value and operational sensitivity of modern AI infrastructure?
• How can advancements in video surveillance and integrated sensor technologies deliver enterprise value for both security and broader operational resilience?

The physical layer is under more stress than ever

Historically, data center environments were designed around modest rack densities.

To that end, proactive data center operators are now combining video surveillance, access control, radar, LiDAR, thermal imaging, and environmental monitoring to improve situational awareness and response times.

These systems deliver their greatest value when they contribute to a broader picture of resilience and work together as part of a unified technology ecosystem. Ideal systems are typically built on an open-architecture approach that facilitates multi-vendor interoperability, reduces vendor lock-in, and allows easy unification and evaluation of security and operational data without proprietary silos.

Today, organizations are expected to maintain continuous surveillance of entry points, mantraps, data halls, and critical infrastructure, while correlating video with access events, using AI to detect security risks, enabling camera tamper alerts, and retaining footage, commonly 30-90 days, with PCI DSS requiring at least three months for sensitive areas.

In high-value facilities, security depends on both comprehensive coverage of critical pathways and integrated systems that transform video into verifiable operational and investigative intelligence.

Looking ahead

Densities that once averaged five to 15kW per rack are growing in scale to 50kW, 75kW, or more than 100kW per rack, with some architectures scaling towards 1MW.

Suffice to say, AI deployments are now pushing facilities into new territory, concentrating significantly more computing power, operational risk, and financial value into a smaller footprint.

The impact of higher densities is substantial. It increases power demand, heat load, and dependence on cooling systems and electrical redundancy, resulting in a higher need for real-time operational visibility.

A cooling disruption that once may have affected a limited number of workloads can slow down an entire AI cluster. A power anomaly that was previously manageable can have far-reaching business consequences when it supports large-scale training or inference workloads. A physical-security event is no longer solely isolated to the security team.

In an AI environment, each of these issues can impact an entire organization faster and more significantly than ever before.

Proactive vision intelligence leads to security and resilience systems

Early detection of threats or equipment problems is essential for the physical security and operational side of the business. Because, in many cases, these issues begin very subtly:

• An improperly secured or poorly monitored electrical yard or substation
• Trespassers probing fence lines or secondary entrances outside the immediate perimeter
• Tailgating or piggybacking at controlled doors (still one of the most common physical access vectors)
• Early indicators of equipment operational issues, including fluid leaks, overheating equipment areas, smoke conditions, or activity around power and cooling infrastructure.

Organizations have traditionally viewed these challenges through separate functional lenses.

Security teams focus on protecting people and property, facilities teams focus on equipment uptime and environmental conditions, and operations teams focus on efficiency and capacity.

That said, operators are increasingly seeing the value of having a more connected approach that aligns these stakeholders.

The goal is no longer only detecting incidents after they occur. It is about generating real-time understanding of how multiple indicators can signal developing risk at the earliest warning signs.

The data center industry has spent the last several years focused on building enough infrastructure to support AI growth. According to JLL’s 2026 outlook, nearly 100GW of new data center capacity is expected to come online between 2026 and 2030, effectively doubling global capacity, pushing it toward 200GW by 2030.

The bigger story is not only how many facilities are being built, but how much value is being concentrated inside them. Whether protecting a family SUV or a Formula One race car, the goal is the same. Keep critical assets operating at peak performance.

As the value of the assets increase and the consequences of disruptions grow, security strategies must evolve as well.

Together, we can accomplish what data center operators need now, creating integrated, intelligence-driven systems that strengthen security and operational resilience.

Explore how Hanwha Vision helps data centers enhance security, accelerate response times, and improve operational visibility.