Data Privacy and PII Handling
Explore data privacy and PII handling in AI systems to understand how to identify sensitive information and enforce controls across AI pipelines. Learn to map data flows, apply redaction strategies, enforce access restrictions, and implement retention policies to maintain security, debug effectively, and meet operational requirements in production AI applications.
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 evals. 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 ...