Building a custom AI application has become remarkably straightforward. Engineering teams can connect a Large Language Model to internal company knowledge, set up a vector database for Retrieval-Augmented Generation (RAG), throw together a sleek chat interface, and ship a functional internal copilot in a weekend.
However, making an AI application work is fundamentally different from making it secure.
When organizations move from experimental scripts to production systems—customer support bots, internal policy assistants, or document analyzers—they quickly discover that traditional security perimeters no longer apply. The model is no longer just processing structured database queries; it is processing unstructured natural language, dynamic retrieved context, and generated text.
That shifts the attack surface from the infrastructure level directly to the AI workflow itself.
The Model Isn't the Entire Attack Surface
