How to Implement AI Guardrails

AI guardrails intercept LLM outputs before they reach users, allowing you to block, redact, or modify harmful content. Here's how to implement them.

Step 1: Define Your Policies

Identify what content should be blocked or modified:

• Safety: Harmful advice, dangerous instructions
• Privacy: PII, credentials, internal data
• Brand: Competitor mentions, off-brand content
• Compliance: Regulated content, disclaimers

Step 2: Choose Guardrail Actions

• Flag: Record but allow (for monitoring)
• Warn: Add disclaimer to response
• Redact: Remove sensitive content
• Block: Prevent response entirely

Step 3: Implement with DriftRail

// Create a guardrail
POST /api/guardrails
{
  "name": "Block High Risk",
  "rule_type": "block_high_risk",
  "action": "block",
  "config": { "threshold": 75 }
}

// Check content against guardrails
POST /api/guardrails/check
{
  "output": "AI response here",
  "classification": { "risk_score": 80 }
}

Step 4: Test and Iterate

• Test with known harmful content
• Monitor false positive rate
• Adjust thresholds based on results
• Review blocked content regularly

Best Practices

• Start with flagging, then escalate to blocking
• Use different thresholds for different use cases
• Provide fallback responses for blocked content
• Log all guardrail triggers for analysis

FAQ