Data Privacy and PII Handling
Explore effective strategies to handle personally identifiable information in AI applications. Understand how to map data flows, identify PII hotspots, apply redaction techniques, enforce access control, and implement retention policies to protect user privacy while ensuring system observability and compliance.
LLM apps need high quality logs and traces to debug failures like wrong tool calls, missing retrieved context, or malformed outputs, but those same logs can capture user text, uploaded documents, and transcripts that include sensitive fields. Set constraints up front so observability stays useful without turning telemetry into a sensitive data store.
Treat personally identifiable information (PII) as any field that can identify a person directly or indirectly in our product context. Typical sources include chat messages, attachments like PDFs or images, metadata like emails in headers, and transcripts from audio. LLM apps amplify exposure because the same input can be copied into prompts, retrieved chunks, tool outputs, traces, and evaluation datasets, so one leak path becomes several.
Keep only what the system needs to run, evaluate quality, and debug incidents, and delete everything else on a schedule that matches our risk tolerance and operational needs. If we cannot explain why we store a field, we should not store it.
Map the end to end data flow
With constraints defined, the next ...