Generative AI is powerful, but without strong data controls it can leak Personally Identifiable Information (PII) in seconds. If you run AI pipelines, especially ones that ingest customer data, you need a clear PII catalog and enforcement rules.
A PII catalog is a live inventory of every data field that can reveal an individual’s identity. Names, emails, addresses, payment info, account IDs. In generative AI workflows, these fields can get embedded in training data, prompt context, or model outputs. Without cataloging them, you’re working blind.
Generative AI data controls bridge the gap between the raw flow of information and safe, compliant output. They intersect with the PII catalog by enforcing detection, masking, redaction, and blocking in real time. These controls must run at every point where a model touches sensitive data: ingestion, pre-processing, inference, and post-processing.
To build this, start with automatic PII detection that tags fields as soon as they enter your pipeline. Maintain a structured catalog that updates instantly as new sources appear. Feed this into your data control layer, which applies transformations based on policy—masking phone numbers, hashing emails, dropping unique identifiers.