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

As developers increasingly adopt chain-based and agentic LLM application architectures, the threat of critical sensitive data exposures grows due to their high privileges within applications and infrastructure. Prompt injection attacks can occur via direct prompting or indirect methods such as hidden instructions in linked assets or compromising downstream tools like retrieval-augmented generation (RAG) systems. To mitigate these risks, organizations should monitor LLM applications for prompt injection attacks and sensitive data exposures, implement prompt guardrailing and data sanitization techniques, restrict access to sensitive information, and use monitoring solutions to detect potential attacks.