Isolation is assumed. It is rarely verified.
Workloads that share an accelerator are assumed to be kept apart. Whether they are depends on how the card is shared, and few ever check.
See the findingRuntime security at the accelerator layer
Runtime security for GPUs and AI accelerators
Secure AI where it actually runs.
SLYCORE makes the GPUs and accelerators your AI runs on observable, verifiable and controllable, starting at the boundary where every workload reaches the device.
Where SLYCORE works
The point where a workload reaches the accelerator.
SLYCORE sees how every workload uses the accelerator, and ties each action to the workload behind it.
Runtime security for GPUs and AI accelerators
Secure AI where it actually runs.
SLYCORE makes the GPUs and accelerators your AI runs on observable, verifiable and controllable, starting at the boundary where every workload reaches the device.
Where SLYCORE works
The point where a workload reaches the accelerator.
SLYCORE sees how every workload uses the accelerator, and ties each action to the workload behind it.
What can you actually see when your AI runs?
GPU metrics show how busy a card is. Not which workload reached into a neighbor. Endpoint and cloud security stop at the host.
Escapes from the container
Flaws in GPU container tooling have let a workload break out to the host and reach the workloads next to it.
Compute taken from a neighbor
A workload can claim accelerator cores it was never allocated, and every host tool sees normal activity.
Activity your tools never see
What happens between workloads on the accelerator happens below endpoint and cloud security.
One sensor for the accelerator layer.
Runs alongside your AI workloads. No host changes, no changes to models or applications.
Protect production AI
See and stop a workload that reaches past its boundary, while it happens.
Prove isolation
Evidence for customers and auditors that workloads sharing a GPU stay apart.
Contain one workload
Isolate the offender. Everything else on the card keeps running.
Account for every GPU
Know which workload holds which accelerator, and who can reach whom.
Cross-workload accessresearch-sandbox → fraud-scoring
Make the accelerator layer observable, verifiable and controllable.
For AI teams and the GPU clouds they run on.
Whether you share accelerators with customers or keep them in-house, the question is the same: who and what can reach the workloads running on them?
Your cards may be dedicated. Your access is not.
Access per team and workload
Teams, pipelines and AI agents run on the same cluster. See which identity reached which model and memory.
Deploy rights are model access
Anyone who can launch a container can sit next to your models. See every container that touches the device, and who launched it.
Evidence for the auditor
The isolation you configured, compared with what actually ran on the hardware.
Into the SOC you already run
Boundary events reach your SIEM with the workload, pod and team attached.
Sell isolation you can prove.
Cross-tenant access, caught
One workload reaching into a neighbor's memory or compute, seen at the boundary.
One workload contained
The offender is isolated. The node keeps running and the tenant next door is never touched.
Share capacity with evidence
Show every customer that their neighbors on the card cannot reach them.
Separation testing, documented
Evidence for PCI DSS A1.1.4 checks of the separation between customer environments.
Start at the boundary.
We install the SLYCORE sensor on a group of your AI workloads and show you what happens between them, in your own environment.
One sensor
- Runs as part of your workloads
- Sees every interaction with the accelerator
- No changes to models or applications
Your infrastructure
- Cloud GPU instances and on-prem clusters
- Kubernetes and bare metal
- Run on more than one accelerator family
Your tools
- Events streamed to your SIEM
- Signed reports for auditors and customers
- Each event tied to workload, pod and team
Findings from the boundary.
What shared accelerators actually allow, stated plainly.
Research
One tenant can affect every workload sharing its accelerator.
Depending on the platform, it can crash its neighbors or read their live data, and host security tools record nothing. It matters to anyone who shares GPU capacity.
See the findingAI is only as secure as the layer it runs on.
See what is happening on your accelerators, and stop what should not be.