AI Infrastructure Security Requirements for Enterprise Deployment

AI infrastructure security requires six controls — isolation, encryption, identity and access management, audit logging, network segmentation, and AI-specific data path protections — each verified with evidence because enterprise deployments must satisfy regulators, auditors, and internal policy. For the full framework, see building secure AI infrastructure for LLMs. For the enterprise requirements, see enterprise GPU cloud security.

The Six Requirements

Isolation: single-tenant dedicated infrastructure, or multi-tenant with verified logical isolation including GPU memory clearing. Encryption: at rest and in transit for every surface — storage, network, GPU memory — with key residency matching data residency. IAM: least-privilege, MFA, role-based access, and documented provider staff access scope. Audit logging: all access and data movement events logged and tamper-resistant, covering AI-specific events (checkpoint saves, inference requests, model exports). Network segmentation: isolated fabric with east-west controls, encrypted transit. AI data path: checkpoints, inference logs, vector databases, and GPU memory governed with the same controls as traditional data — because they carry the same sensitive content. For each, demand evidence — not claims. For the audit methodology, see auditing AI infrastructure providers.

FAQ

What security does AI infrastructure need for enterprise deployment?

Isolation, encryption, IAM, audit, network segmentation, and AI data path protections. All six verified with evidence. See the requirements above and building secure AI infrastructure.

Summary

Enterprise AI security requires six controls verified with evidence. For the full framework, see building secure AI infrastructure.