Observe

A black box for every accelerator.

The sensor records how each workload accesses the GPU and keeps the history.

  • Device opens, memory maps, IPC handles and unified memory
  • Each event tied to container, pod, namespace and team
  • A tenancy map: which workload holds which accelerator
  • Searchable during an investigation, streamed to your SIEM
Detect

The access that should not happen.

  • One workload reaching into another's live memory
  • Compute taken from a neighbor's allocation
  • Escape attempts from containers on GPU hosts
  • Risky configuration: driver and toolkit versions, sharing modes, exposed services
Enforce

Contain one workload, not the node.

  • Policies per workload: devices, memory and shared handles
  • Revoke one container's access to the accelerator
  • Neighbors on the card keep running
  • Every action logged and signed for the incident review
Prove

Evidence your auditor can check.

  • Active isolation tests, with negative controls that separate a live read from leftovers
  • Signed reports: each claim comes with the command that reproduces it
  • The posture you configured, compared with what actually runs
  • Material for PCI DSS A1.1.4, DORA and SOC 2 reviews

The console.

Every accelerator, every workload, and the events no other tool records. Shown with an example customer.

How it starts.

Days, not quarters. You see the boundary in your own environment before you decide anything.

01

Install

We install the sensor on a group of your AI workloads.

02

Observe

You see every access at the accelerator boundary, tied to each workload.

03

Report

You get the isolation results and a signed report.

Questions about GPU runtime security.

What teams ask before they put SLYCORE on their accelerators.

What is GPU runtime security?

It watches what workloads do on GPUs and AI accelerators while they run: which workload opens the device, maps memory or shares a handle, and whether any of it crosses into another workload. SLYCORE adds that layer at the boundary between the operating system and the accelerator.

Don't GPU partitioning and sharing modes already keep workloads apart?

They divide the hardware, but they do not show what actually happens across the boundary. Under a common GPU sharing mode, one container can trigger a fault that stops every workload sharing the accelerator. SLYCORE verifies isolation at runtime instead of assuming it.

How is SLYCORE different from EDR or cloud security tools?

EDR, cloud security posture tools and cloud audit logs stop at the host. They do not see device opens, memory maps or shared handles on the accelerator. SLYCORE works below them and sends its events into the SIEM you already run.

Do we need to change our models or applications?

No. The sensor is installed on the hosts that run your AI workloads. Models and applications stay as they are.

Is SLYCORE for GPU clouds or for enterprise AI teams?

Both. GPU clouds use SLYCORE to prove tenant isolation to customers and auditors, including PCI DSS A1.1.4 separation testing. Enterprise AI teams use it to control which teams, pipelines and agents reach which models on their own cards.

How do we get started?

We install the SLYCORE sensor on a group of your AI workloads and show you what happens between them, in your own environment. Request a demo and we will set it up with you.

See the boundary in your own environment.

We start with the sensor on a group of your AI workloads.