What is AI Guardrails?
Turkish: AI Guardrails (Yapay Zeka Koruma Bariyerleri)
AI guardrails are control layers that constrain model inputs, outputs, and tool use against safety, policy, and quality rules.
What is AI Guardrails?
AI guardrails set boundaries instead of assuming that a model will behave correctly in every situation. These boundaries may include input checks, output review, tool permissions, source validation, and human approval.
In a customer support assistant, a guardrail can mask personal data, block legally definitive wording, or require agent approval before a refund action. The goal is not to silence the model; it is to route risky situations into a safer behavior.
Types of Guardrails
- Input controls: Flag malicious instructions, sensitive data, or out-of-scope requests
- Output controls: Review the answer for policy, tone, format, and data leakage
- Tool controls: Limit which API or file operation can run under which condition
- Evaluation controls: Monitor model behavior with test sets and logs
Limits and Use
Guardrails can reduce prompt injection and hallucination risks, but they do not provide a guarantee by themselves. A reliable system combines authorization, observability, source grounding, and safe fallback behavior.
In production projects, guardrail decisions should match the business context. An internal knowledge assistant, customer support bot, and financial transaction agent do not carry the same risk level.
Related Terms
AI hallucination is when a model produces information that sounds plausible but is false, unsupported, or not grounded in the source.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
Prompt InjectionPrompt injection is an attack where user or external content tries to override hidden instructions and steer an AI model.