AI Safety
What Are AI Guardrails?
Protecting LLM outputs before they reach users
AI guardrails are safety mechanisms that monitor, filter, or block LLM outputs before they reach users. While models have built-in safety training, guardrails provide an additional layer of protection that you control.
What are AI guardrails?
AI guardrails are safety mechanisms that monitor, filter, or block LLM inputs and outputs to prevent harmful content, policy violations, PII exposure, or other risks. They act as a safety layer between your AI model and users, enforcing rules that the model itself cannot reliably follow.
Why Guardrails Matter
LLMs are trained to be helpful, but they can't reliably enforce business rules or safety policies. Guardrails fill this gap:
- Models can be jailbroken despite safety training
- Business policies change faster than models can be retrained
- Compliance requirements need deterministic enforcement
- Brand safety rules are organization-specific
Types of Guardrails
What types of AI guardrails exist?
Common guardrail types include: content filtering (block toxic/harmful content), PII redaction (remove personal data), topic restrictions (prevent off-topic responses), brand safety (block competitor mentions), risk thresholds (block high-risk outputs), and custom rules (regex patterns, keyword blocking).
Content filtering — Block toxic, harmful, or inappropriate content based on classification scores.
PII redaction — Automatically detect and remove personal information before logging or returning responses.
Topic restrictions — Prevent the model from discussing certain topics (competitors, politics, medical advice without disclaimers).
Risk thresholds — Block any response above a certain risk score from your safety classifier.
Custom rules — Regex patterns, keyword lists, or custom classifiers for organization-specific needs.
Guardrail Actions
What actions can AI guardrails take?
Guardrails can: flag content for review without blocking, add warnings to responses, redact sensitive information (replace PII with placeholders), block responses entirely and return an error, or modify content to remove problematic sections. The action depends on the severity and use case.
| Action | Behavior | Use Case |
|---|---|---|
| Flag | Record for review, allow through | Low-risk monitoring |
| Warn | Add disclaimer to response | Medical/legal content |
| Redact | Replace sensitive content | PII protection |
| Block | Return error, don't show response | High-risk content |
Performance Considerations
Do AI guardrails add latency?
Yes, guardrails add some latency since they must analyze content before returning responses. Simple pattern matching adds milliseconds. AI-based classification adds 50-200ms typically. For most applications where LLM inference takes 500ms-3s, guardrail latency is negligible. Async guardrails can flag without blocking.
DriftRail provides inline guardrails that can block, redact, or warn on content in real-time, with configurable rules and thresholds.
Add guardrails to your LLM
DriftRail provides inline content protection with configurable rules.
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