Enterprise GPU Cloud Security Requirements Checklist

Enterprise GPU cloud security requires isolation, encryption, identity and access management, audit logging, network segmentation, and AI-specific data path protections — verified with evidence because regulatory obligations demand proof, not promises. For the full audit methodology, see auditing AI infrastructure providers. For the compliance framework, see enterprise AI compliance.

The Enterprise Security Checklist

Isolation: single-tenant dedicated infrastructure, or multi-tenant with verified logical isolation including GPU memory clearing. For the isolation mechanism, see how solo capacity stops data leakage. Encryption: at rest and in transit across all surfaces — storage, network, GPU memory — with key residency matching data residency. IAM: least-privilege access, MFA, role-based controls, and documented staff access boundaries. Audit logging: all access and data movement logged, tamper-resistant, covering AI-specific events (checkpoint saves, inference requests, model exports). Network segmentation: isolated fabric with east-west controls between tenants. AI-specific data path: checkpoints, inference logs, vector databases, and GPU memory governed with the same controls as traditional data surfaces. For the full verification framework, see evaluating secure AI providers.

FAQ

What security does enterprise GPU cloud need?

Isolation, encryption, IAM, audit logging, network segmentation, and AI-specific data path protections. For each, verify with evidence — not claims. See the checklist above and auditing AI providers.

Summary

Enterprise GPU security requires six controls verified with evidence. For the full security framework, see auditing AI infrastructure providers.