Definition: AI guardrails are the checks and controls placed around an AI model that keep its inputs, outputs and actions within safe, accurate and permitted limits.
Guardrails run before and after the model. Input checks screen for prompt injection, personal data or off-topic requests. Output checks validate format, flag unsupported claims, block restricted content and confirm that answers cite approved sources. For AI agents, action guardrails limit which tools can be called, with what permissions, and when a person must approve.
Models are probabilistic, so no prompt alone guarantees behavior. Guardrails turn policy into code that can be tested, logged and audited, which matters most when an application touches customers, money or regulated data.
Teams combine deterministic rules, schema validation, classifier models and human review for high-risk cases, then measure how often each guardrail fires as part of their evaluation and observability work.
Ryz LLM evaluation engineers and security engineers design and test guardrails in your stack, working on your team.
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