Runtime Guardrails
Enforcement layers that run around a model or agent at inference time — filtering unsafe inputs, blocking prohibited outputs, redacting PII, detecting prompt injection, and enforcing schemas. Distinct from AI governance policy: guardrails are the mechanism that makes policy actually stick during a live request.
Related terms
- AI Governance
The framework of policies, procedures, and controls that guide the development, deployment, and use of AI systems within an organization. Ensures AI aligns with organizational values and regulations.
- Prompt Injection
An attack where adversarial instructions embedded in untrusted content (user input, retrieved documents, tool responses) hijack the model into ignoring its original instructions. Considered the primary security risk for LLM applications; defenses include separation of channels and output validation.
- Agent Evals
Systematic testing of AI agents against representative tasks — measuring task completion, tool-use correctness, cost, latency, and safety across many runs. Distinct from single-turn LLM evals because agent behavior spans multiple steps and depends on tool outputs, memory, and planning quality. Considered table stakes before shipping agents to production.
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Where this fits
Runtime Guardrails is part of the Infrastructure vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.