What is data center security?

Key data center security takeaways

• Operational continuity is a key metric for data center security. A security breach in your building management systems (OT) is now just as likely to cause a total outage as a network-level attack.
• The basement-to-cloud gap is your biggest security blind spot. Attackers are increasingly using lateral movement from secondary IT systems to sabotage physical cooling and power.
• AI infrastructure within a data center needs a performance-first security posture. You can’t let traditional security agents tax the compute cycles of high-density machine learning and AI clusters.
• As harvest now, decrypt later tactics accelerate, account for the long-term exposure of your encrypted data today to prepare your organization for impacts in the future.

Data center security is the unified management of exposures across every system that keeps your facility running — from OT, IoT, and cyber-physical systems at its foundation, to enterprise IT, cloud, identity, AI, and web applications. 

If you’re relying on siloed security tools and reactive threat detection and response systems, you’ll never have the end-to-end, proactive visibility you need to secure your data center. 

That’s because your old security perimeter of firewalls and access controls belongs to a different era, one when data centers were synonymous with traditional server rooms, not the high-density AI factories they are today. Modern data centers are industrial in scale, with physical, operational, and supply chain vulnerabilities those security tools never anticipated, but that attackers are eager to exploit.

Data center security threats now include anything that disrupts operations, from internal power distribution failures to physical infrastructure damage and supply chain compromises. 

Finding and mitigating that risk means getting visibility into every asset across your entire operational and digital landscape. 

Unified exposure management gives you a single view of your cyber risk across a complex data center attack surface that your team built piece by piece. It aligns how you defend your infrastructure with how attackers actually move through it: without boundaries.

In a data center, you manage a footprint where physical and digital assets are inseparable. 

A vulnerability in a core facility programmable logic controller (PLC) or a misconfiguration in internal power distribution systems is now a tier-one cyber risk. In a high-density environment, losing power to these systems, even for a microsecond, can result in the simultaneous failure of hundreds of servers.

If a threat actor sabotages your HVAC system, the impact on a 100 MW hyperscale center, which requires up to 28,500 tons of cooling, is immediate. Within three to eight minutes, CPUs will throttle performance, and within nine to 15 minutes, the cluster will shut down completely, causing catastrophic hardware damage. 

To secure your data facility, you must bridge the visibility gap between the basement (your power and cooling) and your entire compute stack, including the high-performance graphics processing units (GPUs), the proprietary AI models they run, and the identities that access them.

Want to better manage your data center security in a single exposure management platform? See how in Tenable One.

Converged attacks that bridge physical facilities and digital networks can outpace the capacity of most lean data center security teams. This vulnerability stems from a fundamental difference in language and siloed teams. Facility managers monitor mechanical health, while security operations center (SOC) analysts monitor bit-level anomalies. 

When a chiller sends a mechanical alert, a SOC analyst usually dismisses it as a maintenance issue. They simply don't have the context to recognize it as the first stage of a thermal shutdown attack.

Exposure management fixes this by pulling manual data correlation off your plate and automatically mapping the entire attack path. Tenable One, for instance, blends facility insights with IT vulnerabilities to give you the why behind mechanical anomalies and reveal the security intent that traditional monitoring misses.

The exposure score weights asset criticality, so your team makes decisions based on actual operational and threat context. For example, a cooling threat to a 100 MW GPU cluster should never stay in the same queue as a routine software patch on a peripheral server, and with Tenable One, it won't be. You get to skip the triage bottleneck and jump straight to the remediations that actually move the needle on risk.

Most facility teams and SOC teams don't talk to each other enough, and that gap is a security problem.

Instead of going head-to-head with a hardened enterprise firewall, today, attackers can use building management systems (BMSs) or uninterruptible power supplies (UPSs) as entry points and pivot through those neglected, networked controllers straight to your primary compute stack.

Once inside, they can adjust temperature and humidity controls to force a thermal shutdown, or pivot laterally and pull data from the servers those cooling systems are keeping alive. Most teams don't catch it because they're watching OT and IT through separate lenses.

Exposure management closes this visibility gap. When you bring data from OT, IT, and all other parts of your attack surface together into a single pane of glass, you can see how your facility controllers and your compute stack connect. Your SOC gets visibility into a facility-based attack path before an adversary can exploit it.

And, when you're quantifying risk across all environments at the same time, you stop making siloed guesses and start running a proactive defense that covers your entire operational chain.

Cooling and power: Data center life support systems

Critical dependencies also create significant risk within a data center. 

According to the Uptime Institute Annual Outage Analysis 2025, power remains the leading cause of impactful outages, while IT and networking issues now account for nearly a quarter of all significant failures.

In 2023, a cooling malfunction at a major Singapore data center halted 2.5 million banking transactions. Similarly, late in 2025, a 22-hour lithium-ion battery fire in South Korea resulted in the loss of 858TB of data. These events underscore the need for OT security as part of your exposure management program to monitor your UPS, power distribution units (PDUs), and very early smoke detection apparatus (VESDA). 

Converged IT/OT and physical access

Data center security is now about lateral movement between two previously isolated worlds, creating a converged IT/OT security challenge where a single phished credential on your business network can give threat actors a foothold to access physical security cameras, badge readers, or environment controls.

To stop threat actors from getting access, monitor for identity exposure to ensure threat actors can’t use compromised user credentials to unlock server rooms or deactivate physical alarms.

By conducting an attack path analysis from your external attack surface to your internal critical assets, you can see how an exposed web server might lead to a physical breach. You must view your data center as one continuous fabric of risk to proactively secure it.

Specialized physical threats

Scaling modular infrastructure in your data center to support high-density racks introduces physical vulnerabilities that don't always get digital oversight. Here are a few that catch teams off guard:

• A lot of data centers run battery energy storage systems (BESS) and on-site power generation for AI workloads. The power draw is enormous. The connection between the utility grid and your data center is a prime target for nation-state actors looking to cause regional disruption.
• If you're still using physical hard disks for secondary storage, fire suppression is a threat most CISOs never see coming. A high-decibel VESDA discharge triggers gas releases, like Inergen, FM200. While the gas won't damage circuits, the acoustic pressure from the discharge creates vibrations strong enough to physically shatter hard drive platters.
• Aging components are also a security liability. Lithium-ion batteries past their service life can be fire hazards. When those systems fail, hardware replacement costs and downtime typically hit harder than most data breaches.

Exposure management ingests information from BESS controllers and hardware lifecycle data. An exposure assessment platform (EAP) can flag aging power components or unpatched edge assets as high-risk entry points. Risk-based prioritization then dictates hardware replacement according to actual threat levels, not arbitrary maintenance schedules. 

When you sync these physical health metrics with your SOC, you stop attackers from exploiting the structural decay of your data center to get access to your digital assets.

In a high-density AI environment, the amount of inter-node communication can overwhelm traditional perimeter-based security. 

Standard firewalls handle north-south traffic. They can't keep up with massive, low-latency east-west bursts that distributed training requires — where thousands of GPUs must synchronize parameters in parallel without creating bottlenecks.

Exposure management solves this performance-security trade-off by mapping the specific communication pathways between these GPU clusters to detect lateral movement without interrupting the training flow. 

By correlating this high-speed network data with asset criticality, exposure management identifies unauthorized data shifts across the east-west fabric before an adversary can weaponize the cluster’s massive throughput.

Eliminating the performance tax on compute 

When you run high-performance AI training data and machine learning workloads within your data center, every compute cycle is a valuable asset. 

Traditional security agents often impose a performance tax that AI models cannot afford. This tax refers to the consumption of valuable compute cycles and the injection of latency into data pipelines by traditional security software. 

Because high-density AI and machine learning workloads and GPU clusters require every ounce of available power for training and inference, any drag on performance directly increases the time-to-train for large language models, turning security into a bottleneck. 

To avoid bottlenecks, go agentless with an attack surface management approach built on hybrid, adaptive assessment:

• Push security processing off the host CPU/GPU onto specialized data processing units (DPUs) to get deep visibility into the host and hypervisor without touching the compute cycles you need for model training.
• Run deep-packet inspection on east-west traffic, including VXLAN, at line rate. You can catch anomalies and lateral movement without injecting latency into your data pipelines.
• Use zero-trust segmentation so a vulnerability in a PDU can't become a foothold into a high-performance training cluster.
• Audit your cloud and AI software stack, including orchestration layers like Kubernetes and specialized model libraries, using snapshot-based scanning to find vulnerabilities and misconfigurations without placing an active load on a GPU cluster.

Read "Securing AI data centers" to learn more about this non-disruptive approach.

Securing your AI training data and machine learning lifecycle

Security for your data center should follow data from the ingestion pipelines in your storage layer to the inference endpoints serving your users, to prevent:

• Model poisoning: Unauthorized lateral access to high-speed storage tiers where training datasets reside.
• Prompt injection: Malicious inputs targeting inference service endpoints running on your production clusters.
• Shadow AI: Employees or developers spinning up unauthorized AI models that create dark corners where systems process sensitive data without corporate security oversight.

Continuous exposure management maps your entire data lineage and identifies misconfigurations in model-serving infrastructure. Correlating these vulnerabilities with physical facility risks ensures that a breach in your cooling system or an unpatched API cannot compromise the integrity of your systems.

Agentic AI is a significant risk for your data center. These AI systems make independent decisions to power polymorphic malware that rewrites its own code on the fly and automated reconnaissance agents that scan petabytes of traffic to find micro-vulnerabilities faster than any human can.

To counter AI risks in your data center, perform regular vulnerability assessments of your autonomous AI agents to ensure threat actors haven’t poisoned them with malicious instructions that alter security logic.

Use exposure management to mitigate this threat by analyzing behavior logs and permission sets of every autonomous agent within your compute fabric. By identifying over-privileged agents or unauthorized changes to an agent's instruction set, exposure management prevents attackers from turning your own automation against your infrastructure.

Identity-centric log-in attacks: Deepfakes and MFA bypass

Adversaries are also pivoting from “breaking in” to “logging in.” Today, they’ll just as easily exploit the human element of your data center by tricking someone into giving them credentials, as a known vulnerability. To defend this perimeter, you need identity-centric security.

Here are some attack methods that could impact your data center:

• Attackers use AI-generated voice and video to spoof identities.
  • Additionally, compromised camera feeds allow adversaries to read PIN numbers at entry points or capture operator faces for blackmail and social engineering.
  • Exposure management finds these anomalies by correlating unusual access requests with baseline behavioral telemetry and device reputation scores.
• Adversary-in-the-middle (AiTM) attacks and push-bombing.
  • These help extortionists to bypass multi-factor authentication (MFA) by stealing active session tokens.
  • An exposure management platform mitigates this risk by identifying vulnerable session configurations and flagging high-risk authentication patterns that indicate token theft or excessive push notifications.
• Active Directory attack path exploitation leverages compromised entry points.
  • Once threat actors get physical access via a badge-system hack, they can install hardware hacker modules to bypass firewalls, establish persistent remote access, and trigger catastrophic system denial incidents.
  • Exposure management maps these complex identity relationships, surfacing hidden paths from a low-level facility asset to your most sensitive domain controllers before an attacker can exploit them.
• Credential relay attacks exploit traditional MFA methods by intercepting and replaying authentication tokens in real-time.
  • This is so attackers can bypass security controls even when users have successfully authenticated through legitimate channels.
  • Exposure management disrupts these attacks by mapping specific service accounts and legacy protocols, like a new technology LAN manager (NTLM), which allows token relay. When you can find all the choke points where these accounts link to business-critical assets, you can take steps to turn off relay-prone configs before an adversary weaponizes them.

Harvest now, decrypt later

The harvest now, decrypt later (HNDL) strategy is a primary concern for data centers storing long-term sensitive data. Adversaries are siphoning encrypted data today with the intent to decrypt it once cryptographically relevant quantum computers are available. 

According to the CISA guidance on Product Categories for Technologies that Use Post-Quantum Cryptography Standards, you should prioritize migration of high-impact systems and national critical functions (NCFs) to post-quantum cryptography (PQC) to defend against long-term data harvesting.

Because data center lifecycles are long, the hardware you install today must be quantum-ready to avoid being obsolete in the next few years. 

Exposure management supports this migration with a continuous inventory of your cryptographic assets. It can pinpoint specific instances of legacy encryption, like Rivest-Shamir-Adleman (RSA) or elliptic curve cryptography (ECC), within your software stack and cloud storage tiers. 

By surfacing these vulnerable algorithms before they’re obsolete, you can systematically apply PQC standards to your highest-value data assets first.

Disconnected and siloed security tools create blind spots in your data center security strategy. A more unified approach, like exposure management, sees your entire converged environment as a single attack surface.

Instead of navigating multiple dashboards, Tenable One unifies data from across your enterprise into a single exposure score. Consolidating this data into a single view helps your security team intercept complex, cross-domain attack paths before they cause operational downtime.

Your security strategy for your data center must also satisfy rigorous data center security compliance standards like NERC CIP compliance for power systems and NIS2 for infrastructure resilience. 

Tenable One gives you continuous, automated visibility into every asset and exposure across your data center to continuously improve compliance.

Download the Security Leader’s Guide to Exposure Management to see how a unified exposure management platform can streamline regulatory reporting.

As more and more data centers are being built to power the evolving digital economy, across cloud, AI, quantum and more, some common questions keep arising. Let's answer a few of those here. 

What are the primary risks to data center security in 2026?

The biggest risks in 2026 are around converged IT/OT attacks, where an adversary moves laterally from a corporate network to sabotage physical components like cooling or power, or goes after your digital assets. Beyond that, agentic AI is changing how fast attacks can scale, and encrypted data harvesting is becoming a real concern as teams prepare for future quantum decryption.

How does AI affect data center security?

Security teams use AI-driven exposure management to predict attack paths before exploitation. Attackers use agentic AI to automate exploits faster than most teams can respond. On top of that, securing high-density machine learning workloads and data to train AI means your AI security strategy has to protect the hardware without cutting into compute performance.

Why is OT security critical for data centers?

OT manages data center life-support systems like HVAC and UPS. Because these systems are on a network, a cyberattack can trigger physical consequences like overheating or fire. That kind of failure typically causes more downtime than a traditional software breach, and it's a lot harder to recover from.

Ask for a demo to see how Tenable One can help you simplify your data center security strategy.