There's a dangerous assumption that exists throughout enterprises as it relates to AI: someone else is handling GPU security.

We've had this conversation dozens of times. CISOs at banks, healthcare systems, and Fortune 500s tell us they believe their cloud provider is securing the GPU layer. Then we talk to the cloud providers. They're equally clear: GPU workload security is the customer's responsibility.

Both sides are confident. Both sides are wrong about what the other is doing. And in between? A wide-open attack surface.

The Shared Responsibility Blind Spot

Every enterprise security leader understands shared responsibility for traditional infrastructure. You don't expect AWS or Azure to secure your application code. You run endpoint protection. You patch your drivers. You monitor your containers. You build secure VPCs and deploy WAFs and implement zero trust.

Nobody assumes the cloud provider handles CPU-level security for their workloads.

Yet somehow, there is a collective belief that GPUs are different.

The same organizations that wouldn't dream of running unmonitored containers or unpatched kernels are deploying AI workloads with zero visibility into GPU execution. The same teams that mandate EDR on every endpoint have no equivalent for their most expensive, most critical compute resources.

Why the Disconnect?

Three factors drive this blind spot:

Novelty: GPU-accelerated AI workloads are new to most enterprises, three years ago most enterprises couldn't imagine why they'd want a fleet of GPUs even in the cloud. Security tooling and processes haven't caught up. When there's no obvious solution, it's easier to assume someone else owns the problem.

Complexity: GPU security requires understanding CUDA kernels, driver behavior, VRAM allocation, and hardware-level execution. Most security teams don't have this expertise. Cloud providers seem more qualified.

Vendor messaging: Cloud providers market "secure AI infrastructure." That language creates an impression of comprehensive protection. The fine print tells a different story.

What Cloud Providers Actually Secure

To be clear: cloud providers do secure their infrastructure. Physical security. Hypervisor integrity (although even this is messier with GPUs than people assume). Network isolation between tenants. Hardware lifecycle management.

What they don't secure: what happens inside your GPU allocation once you're running workloads. Your CUDA kernels. Your model execution. Your VRAM contents. Your driver interactions.

That's yours. It always was.

The Real-World Consequence

This assumption gap creates measurable risk. We've seen:

• Financial services firms running inference workloads with no GPU-level monitoring
• Healthcare organizations processing PHI through AI systems with zero visibility into execution
• Enterprises assuming "secure enclave" marketing means their models are protected

When we show these teams what's actually visible—or invisible—to their current security stack, the reaction is consistent: we thought someone was watching this.

Closing the Gap

GPU security follows the same shared responsibility model as everything else in cloud. The provider secures the infrastructure. You secure your workloads.

The difference: for CPUs, you have mature tools and established processes. For GPUs, most organizations have neither.

That's the gap Stealthium closes. Runtime visibility into GPU execution. Detection of threats your existing tools can't see. The same security posture you demand for CPU workloads, extended to AI infrastructure.

Stop assuming. Start verifying.

Contact us to chat about what GPU security your cloud provider actually delivers—and how we can help with what's still on you.