The log files told a story no one wanted to read. Sensitive data had slipped into a generative AI output, undetected until it was too late. This is the risk every engineer faces when building systems with large language models — without strong data controls, you’re trusting your integrity to chance.
Generative AI data controls are no longer optional. They are the foundation of safe, reliable deployments. In the world of Site Reliability Engineering (SRE), these controls protect both the system and the people it serves. They stop private, regulated, or internal information from leaking through model prompts, training data, or machine-generated text.
Effective controls start at the input layer. Filter prompts before they reach the model. Strip or mask any fields containing customer data, credentials, or internal identifiers. Apply strict validation rules and log every intercepted event for audit trails.
The next layer is output inspection. Post-process every generative response through deterministic checks. Look for patterns like phone numbers, social security numbers, and API keys. Apply redaction before responses go downstream or hit production endpoints.