Securing GenAI Applications: Prompt Injection, RAG Risks, and Data Isolation

Most enterprises that have deployed a GenAI application in the last two years have done something dangerous without realizing it: they assumed that securing the cloud infrastructure was equivalent to securing the AI system running on top of it. It is not. The attack surface of a GenAI application is fundamentally different from anything that came before, and the security gaps it introduces are real, exploitable, and in many cases already being tested by adversaries. This article breaks down the three biggest security risks in enterprise GenAI deployments prompt injection, RAG vulnerabilities, and data isolation failures and gives you a practical framework for addressing all three before you reach production. The Hidden Security Gap in Enterprise GenAI Deployments Traditional cloud security is built around protecting data in transit, at rest, and at the perimeter. It assumes that if you lock down the network, encrypt the storage, and manage access through IAM policies, you are covered.