Enterprise AI
LLM applications bring their own risks: prompt injection, data leakage via context, uncontrolled costs. We secure access via API management, routing, filters and observability.
In LLM applications, the context is part of the execution. Manipulated documents or web pages can inject instructions. Effective countermeasures are separating data and instructions, restrictive tool permissions and checks before every consequential action.
Instead of distributing keys across applications, requests run through a central gateway: authentication, quotas, logging and model selection in one place — also the foundation of our product LLMrouter.eu.
Token consumption per team and use case, budgets and alerts prevent surprises. Routing simple tasks to smaller models often significantly reduces costs without users noticing a difference.
Answers to the questions we are asked most often about LLM Security.
An attack in which instructions reach the model context through ingested content and manipulate its behavior. Countermeasures are context separation, minimal tool permissions and confirmations before consequential actions.
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