# Secure and Private GPU Infrastructure

Our core value is data security and privacy.

That's why we have invested in our own private and secure GPU cluster, designed with robust controls to mitigate the risks associated with shared GPU environments.

### <mark style="color:purple;">Key Mitigation Strategies</mark>

<mark style="color:blue;">Physical Partitioning</mark>

Our GPUs are physically partitioned to prevent any cross-process leakage or unauthorised access between different models or clients. This eliminates the risk of attackers stealing sensitive data like model weights or reconstructing model outputs.

<mark style="color:blue;">No Multi-Tenant Access</mark>

We strictly prohibit multi-tenant access to our GPUs. Each client's models and data are isolated in their own secure partition, inaccessible by any other parties.

<mark style="color:blue;">Proactive Patching</mark>

We work closely with our GPU vendors to ensure we rapidly deploy the latest security patches and firmware updates across our entire cluster. Our dedicated security team continuously monitors for any newly discovered vulnerabilities.

<mark style="color:blue;">Secure Coding Practices</mark>

All of the code we develop to train and deploy models on our GPU cluster adheres to rigorous secure coding standards. Our developers are highly trained in identifying and preventing any potential exploit vectors.

<mark style="color:blue;">Ongoing Security Research</mark>

We actively participate in AI security research to stay on the cutting edge of identifying and mitigating GPU and model vulnerabilities. Our team collaborates with leading experts to develop new security measures.

By maintaining our own private and secure GPU infrastructure, with state-of-the-art controls and active risk mitigation, Continuum Labs ensures that our clients' valuable models and data remain fully protected at all times.&#x20;