Why AI applications need their own safeguards
An AI application processes input, context and often external data in real time. That is exactly where attack surfaces appear that a traditional firewall or a classic endpoint tool does not cover. Prompts can inject instructions, sensitive content can end up in models by accident and output can be manipulated on purpose.
Employees use public AI services without approval. The first step is therefore rarely a new tool, but visibility into where AI is already being used.
The new attack surfaces
Prompt injection
Hidden instructions in input or documents make the model bypass its rules.
Data leakage via prompts
Sensitive information enters external models through input and leaves the controlled environment.
Unsafe output
Faulty or manipulated answers flow into processes and systems unchecked.
Autonomous agents
Agents with excessive permissions perform actions no one approved.
How AI Defense works
Discovery
Visibility into which AI services, models and agents are used, with which data and interfaces.
Detection
Suspicious behavior, risky prompts and data leakage are detected before damage occurs.
Protection
Guardrails, access rules and policies take effect directly at application runtime.
The result is not a ban on AI, but controlled use that fits the maturity of the organization.
