How to Add Guardrails to LLM Applications

Large language models are powerful, but they introduce new risks when deployed in production. Users can accidentally (or intentionally) include sensitive data in prompts — emails, phone numbers, API keys, even social security numbers. Without guardrails, this data flows directly to third-party AI providers.

Why guardrails matter

When you ship an AI feature, you're creating a new data pipeline. Every prompt is user input that gets sent to an external API. This means:

• **PII leakage**: Users paste personal information into prompts without thinking
• **Secret exposure**: Developers include API keys and credentials in test prompts
• **Cost runaway**: A single user can generate thousands of dollars in API costs
• **Compliance gaps**: Without logs, you can't prove what was sent or when

The SignalVault approach

SignalVault sits between your application and the AI provider, inspecting every request and response:

1. **Pre-flight rules** evaluate the prompt before it reaches the AI provider
2. **Post-flight rules** evaluate the response before it reaches your user
3. **Every interaction** is logged with an encrypted audit trail

Rule types

• **PII Detection**: Regex-based matching for emails, phone numbers, SSNs
• **Secret Detection**: Pattern matching for API keys, tokens, AWS credentials
• **Token Limits**: Enforce maximum token budgets per request
• **Model Allowlists**: Restrict which models can be used

Actions

Each rule can take one of four actions: - `allow` — log but don't interfere - `warn` — log a warning, allow the request - `block` — reject the request entirely - `redact` — replace matched content with placeholders

Getting started

Install the SDK and wrap your OpenAI calls:

npm install @signalvaultio/node openai

That's it. All requests now flow through SignalVault's guardrail engine before reaching OpenAI.

Conclusion

Guardrails aren't optional for production AI. They're the difference between "we think it's fine" and "we can prove it's compliant." Start with PII detection and secret blocking — those cover the most common risks — then add token limits and model restrictions as you scale.