Scaling Privacy Infrastructure for GenAI Product Innovation

This post details the scaling of Meta's Privacy Aware Infrastructure (PAI) to address the unique challenges of GenAI, focusing on data lineage and privacy controls for AI glasses. It introduces PAI's enhanced observability through automated data detection and cross-stack lineage tracking (web, logger, warehouse, inference, training), and efficient privacy controls via Policy-enforcement APIs and automated policy adherence. The contribution highlights the 'Discover' stage of PAI, explaining how cross-stack lineage is collected and linked using a common privacy library (PrivacyLib) to create an end-to-end graph for interaction data. It demonstrates how this lineage is transformed into proof for AI glasses by guiding the placement of Policy Zones, blocking boundary crossings, and continuously verifying data processing jobs.