Best practices for monitoring LLM prompt injection attacks to protect sensitive data

This article details how to implement "guardrails" around Large Language Model (LLM) applications to ensure safe and reliable performance. It emphasizes proactive measures like input/output validation, content filtering, and defining clear conversational boundaries to mitigate risks such as harmful outputs, data leakage, and unexpected behavior. Ultimately, establishing these guardrails is crucial for responsible LLM deployment and building user trust.