Secure AI Infrastructure On-Premises from Day One

**Executive summary:** This document outlines the emerging security risks of running AI workloads on-premises—such as prompt injection, data leakage between tenants, unscanned images, misconfigured clusters, and runaway GPU consumption—and recommends enterprise controls including visibility into models, image and SDK scanning, least-privilege access, prompt/response monitoring, runtime guardrails, and forensic logging; it concludes by positioning Aqua Secure AI as a platform that discovers AI usage, strips sensitive data from prompts, and enforces runtime protections.