Custom AI application defense

When you build AI into your own products — a chatbot, a copilot, an LLM-backed feature — you inherit the same risks as any AI app: prompt injection, sensitive-data leakage, and harmful or off-brand output. This page explains how WitnessAI lets you apply enterprise-grade guardrails to the AI in your own applications through an API.

The problem

AI features you build yourself sit outside the network controls that govern public AI apps:

Prompt injection. Untrusted input can hijack your model's instructions and make it behave in ways you never intended.
Data leakage. User prompts and model responses can carry sensitive or regulated data into and out of your application.
Harmful or off-policy output. Without screening, your application can return toxic, unsafe, or non-compliant content under your brand.

How WitnessAI solves the problem

WitnessAI exposes its guardrail engine as an API so you can screen prompts and responses inline, from inside your own application. You send the prompt (and optionally the model response) to WitnessAI, the same guardrails you use across the platform evaluate it, and you receive a decision — allow, warn, or block — before the content reaches your model or your user. The protection your organization already trusts becomes a service your applications can call directly.

What capabilities WitnessAI provides

An inline protection API. The API reference documents the endpoints for screening AI traffic, including Prompt-Protect for evaluating prompts, and the Complete, Input, and Text-Completion endpoints for integrating screening into your model calls.
The same guardrails, applied programmatically. Calls are evaluated against your guardrails, including Data Protection for sensitive content, Harmful Response Prevention for unsafe output, and Model Protection against prompt-injection and model-targeting attacks.
Consistent policy everywhere. Because the API uses the same guardrails as the rest of the platform, your custom application is governed by the same rules — and produces the same auditable record — as employee and agent AI use.

How to get started

Review the API. Start with the API reference to see the available endpoints and request/response formats.
Choose your integration point. Use Prompt-Protect to screen prompts before they reach your model, and the Complete or Text-Completion endpoints to fold screening into your model calls.
Configure your guardrails. Set up Data Protection, Harmful Response Prevention, and Model Protection so the API enforces the protections you need.
Test and roll out. Validate the decisions against representative prompts, then enable screening in your application.

Custom AI application defense The problem How WitnessAI solves the problem What capabilities WitnessAI provides How to get started