A specialized security layer that protects AI systems - especially large language models (LLMs) - by inspecting and filtering both user inputs (prompts) and model outputs. Unlike traditional firewalls, it analyzes natural language semantics and intent, blocking threats like prompt injection, data leakage and unsafe outputs in real time. AI Firewall security introduces a new paradigm: Traditional security → focuses on network traffic and signatures  AI security → focuses on language, intent, and behavior  That shift is why entirely new terms - like prompt injection, semantic filtering and LLM guardrails - have become essential in modern cybersecurity.

The process of analyzing meaning and intent in text rather than just packets or protocols. AI firewalls operate at this layer, enabling them to detect attacks hidden inside seemingly normal language.

Programmable policies that constrain AI behavior. They enforce rules such as “don’t reveal sensitive data” or “avoid harmful content,” typically implemented via input/output filtering around the model.

The component of an AI Firewall that evaluates prompts and responses against predefined rules, compliance requirements or risk thresholds and decides whether to allow, block or modify them.

A deployment model where the AI Firewall sits between users and the AI system, intercepting all traffic for inspection and control before it reaches the model.