Guardrails That Work: Preventing LLM Failures in Production
Practical approaches to hallucination detection, PII redaction, and output validation that pass security review.
Making LLMs Safe for Production
LLMs are powerful but unreliable. They hallucinate confidently, leak sensitive data in unexpected ways, and occasionally produce outputs that would embarrass your company or harm your users. In production, "usually works" isn't good enough.
The good news: most LLM failures are predictable and preventable. With the right guardrails — input validation, output checking, PII handling, and content filtering — you can deploy LLMs that your security team will actually approve.
This research covers the guardrail patterns we've implemented across production deployments in healthcare, finance, and enterprise software — domains where failures have real consequences.
Safety Research Findings
Hallucination Detection
Combining self-consistency checks with source attribution catches 85% of factual hallucinations before they reach users. The key is making the LLM cite its sources explicitly.
PII Leakage Prevention
Layered PII detection (regex + NER + LLM classification) achieves 99.2% recall on sensitive data. Single-method approaches miss edge cases like informal mentions or context-dependent PII.
Jailbreak Resistance
Input preprocessing that canonicalizes prompts before classification blocks 90% of known jailbreak attempts. Character-level attacks and encoding tricks become ineffective.
Output Validation Overhead
Comprehensive guardrails add 15-20% latency. For most applications, this is acceptable. For latency-critical paths, use tiered validation — fast checks synchronously, deep checks asynchronously.
Implementation Strategies
The Guardrail Stack
We implement guardrails in layers: input validation (schema checks, injection detection, content policy), processing controls (system prompt protection, tool call validation), and output validation (PII scanning, hallucination checks, format enforcement). Each layer catches different failure modes. Defense in depth matters.
Hallucination Mitigation
Three techniques work well together: force the model to quote sources explicitly, check facts against retrieved documents, and use self-consistency (generate multiple answers, flag disagreements). For high-stakes outputs, add a verification step with a separate model instance.
PII Handling Patterns
Identify PII in inputs and outputs using hybrid detection. For inputs, mask PII before processing and restore on output. For outputs, scan and redact before returning to users. Maintain audit logs of detected PII (hashed) for compliance. Different PII types need different handling — names can often be shown, SSNs never.
Monitoring and Alerting
Log all guardrail triggers with full context. Set alerts for: sudden increases in any guardrail type, new patterns of blocked inputs, and outputs that pass guardrails but get negative user feedback. Guardrails should improve over time based on real-world signals.
Ship LLMs Your Security Team Will Approve
We help you implement guardrails that protect users and meet compliance requirements without killing the user experience.