Building Secure AI Applications

Cybersecurity · 2026-07-10 · 6 min read

Prompt injection, data leakage and over-trusted output: a practical security model for AI features in business software.

AI features introduce failure modes that a traditional security review does not catch. The defences are practical, but they have to be designed in from the start.

Treat all model input as untrusted

Prompt injection, meaning instructions hidden in content the model reads, is the defining attack. Anything the model takes in, from user messages to retrieved documents, is untrusted input. Keep instructions and data structurally separate, and never let retrieved content change how the system behaves.

Limit what output can do

An AI response that triggers an action is an execution path. Put consequential actions behind human confirmation, validate outputs against a schema, and give the AI the permissions of the person asking, never a privileged service account.

Protect the data path

Retrieval must enforce the same access controls as the source documents. Log what the model read to produce each answer. When a wrong or leaked answer shows up, you need that trail.

Keep evaluating

Accuracy and safety drift as models, prompts and data change. A small evaluation suite run on every change catches regressions before users do.