WHITE PAPER Protecting the AI-ready data center AI doesn’t happen in one place. Workloads are constantly on the move, touching myriad systems both inside the data center and beyond. Data shifts across multiple storage arrays and compute nodes, over networks, to the edge and back again. Every movement and resting point is a potential opportunity for compromise and, as AI systems grow more capable, so can the threats that target them. Modern AI workloads demand massive data throughput, distributed processing, and complex model architectures, all of which create new surfaces for adversaries to probe. From training data poisoning to model inversion attacks, adversaries don’t exploit weaknesses just in software—but also hardware and infrastructure. dynamic, spanning dozens of different environments— and rendering many older assumptions obsolete. Data center infrastructure is itself an attack surface. The problem is that many enterprises still rely primarily on locking down the perimeter. With AI, however, workloads are increasingly distributed and This paper looks at the strategies IT leaders can use to tackle the unique security challenges that come with modernizing a data center for AI workloads. We often talk about AI as a workload—or workloads. But for adversaries, it’s something more—a new attack surface to exploit. On the other hand, while AI introduces new infrastructure risks and attack vectors, it also offers new ways to guard against them. From anomaly detection to policy enforcement, AI can itself help identify and respond to threats.

AI hasn't just redefined data center performance requirements—it has reshaped how infrastructure needs to be secured. While traditional data architecture focused on securing applications, users, and the perimeter, AI workloads have introduced new complexities: They don't just run on the infrastructure—they change it. AI workloads are dynamic, distributed, and highly data-intensive, making them inherently more vulnerable to novel and emerging attack vectors. As AI becomes more deeply embedded into everyday business operations, adversaries have started to target not just endpoints and networks,...

Perimeter defenses and encryption of data at rest are still important parts of any comprehensive security strategy, but they are no longer sufficient alone. With AI workloads constantly ingesting, analyzing, and generating huge amounts of data across diverse infrastructure layers, data center operators must think beyond static protections. After all, every stage of the data journey—whether in motion, at rest, or in active use—represents a potential opportunity for compromise. The number of potential single points of failure is immense. This is especially true for memory channels and I/O layers,...

AI workloads are fast-moving, multi-layered, and highly automated. Moreover, the technology itself is advancing at a pace that's unmatched throughout the history of computing. These characteristics have created an environment where security features need to be built in by design and default across every layer of the system. Until recently—and especially during the early days of AI experimentation and adoption-enterprises often made do with fragmented, ad-hoc security measures. But these can create blind spots and lead to operational complexity by introducing inconsistencies in how policies are...

Choosing the right AI infrastructure partner Modernizing the data center for AI isn’t just about scaling compute—it’s about reengineering trust across every layer of the tech stack. From adopting zero-trust principles to safeguarding data in motion, to embedding observability and enforcing continuous attestation, protecting AI workloads requires a planned architecturewide approach. As AI continues to advance, becoming more capable and more embedded into business operations, enterprises that treat security features as a dynamic, infrastructureaware capability will be far better positioned to innovate...