Cloud AI Security
Cloud AI security is shaped by shared infrastructure, remote APIs, data governance, tenant isolation, and large-scale service operation.
Overview
Cloud deployment centralizes compute and makes large models accessible, but it also concentrates risk. The attack surface expands to include APIs, orchestration services, data pipelines, storage, logging, access control, and multi-tenant infrastructure.
Threat model
Adversaries may target model APIs, abuse system prompts, exfiltrate data, steal behavior through repeated queries, exploit weak isolation, or misuse deployment plumbing around the model itself.
Countermeasures
Important controls include strict identity and access management, audit logging, input/output filtering, rate control, prompt boundary design, secure storage, data minimization, and infrastructure isolation. For sensitive settings, privacy-preserving inference and confidential execution may also matter.
Open challenges
The key unresolved issue is how to maintain flexibility while preserving trust. Many cloud AI systems are built from fast-moving components, and the surrounding glue code can become the weakest point.