AI Safety Filter

An AI safety filter is a protective control layer that reviews inputs, outputs, or planned actions before they are processed, shown to a user, or executed by a system. It may detect harmful content, privacy violations, prompt injection attempts, jailbreak patterns, sensitive data, unsafe tool calls, or violations of an internal policy. A filter can run before the model, after the model, or inside an agent runtime between planning and execution. The distinction is important: a safety filter is not the whole governance program. It is a specific technical checkpoint that decides whether a step should be allowed, blocked, redacted, escalated, or logged. In a simple chatbot, this mostly affects text. In agentic systems, the stakes are higher because the agent may read files, call APIs, modify code, or send external messages. Effective AI safety filters are context-aware. They separate harmless responses from actions with real-world impact, record why something was blocked, and allow human approval paths instead of shutting down useful workflows indiscriminately.