Secure your AI assistant

LLM security is the practice of protecting large language models and the applications built on them from misuse, manipulation and data leaks. It covers what goes into the model, such as prompts, documents and tool results, and what comes out, such as responses that could expose personal data. A runtime security layer inspects that traffic in real time and enforces your policies on every request.

AI agent security protects assistants that do more than chat: they read emails, browse pages, call tools and take actions on your behalf. Because an agent acts on what it reads, a hidden instruction in a document can change its behavior. AI agent security inspects every input and action, blocks manipulation, controls which tools the agent can use and keeps a full record of what it did.

Yes. NeuralTrust sits as a gateway between your users and the model, so it works with Claude, ChatGPT, Gemini, Grok and other major providers, as well as open source and self-hosted models. You apply the same security policies across every assistant your teams use, without changing how people work with them.

Every prompt, document, web page and tool result is scanned before it reaches the model. Detection models trained on real attack patterns flag hidden instructions, jailbreak attempts and efforts to override the assistant's rules. Malicious content is stripped out or the request is blocked, and the event is logged so your security team can review it.

The main risks are prompt injection, where hidden instructions steer the model; jailbreaks, where users talk it out of its rules; sensitive data leakage, where personal information or secrets end up in prompts or responses; and unauthorized tool use by agents. A lack of logging makes all of these harder to detect. The OWASP Top 10 for LLM Applications is a good reference for the full list.