Managing and understanding large-scale data ecosystems is a significant challenge for many organizations, requiring innovative solutions to efficiently safeguard user data. Meta’s vast and diverse systems make it particularly challenging to comprehend its structure, meaning, and context at scale. To address these challenges, we made substantial investments in advanced data understanding technologies, as part of our Privacy Aware Infrastructure (PAI). Specifically, we have adopted a “shift-left” approach, integrating data schematization and annotations early in the product development process. We also created a universal privacy taxonomy, a standardized framework providing a common semantic vocabulary for data privacy management across Meta’s products that ensures quality data understanding and provides developers with reusable and efficient compliance tooling. We discovered that a flexible and incremental approach was necessary to onboard the wide variety of systems and languages used in building Meta’s products. Additionally, continuous collaboration between privacy and product teams was essential to unlock the value of data understanding at scale. We embarked on the journey of understanding data across Meta a decade ago with millions of assets in scope ranging from structured and unstructured, processed by millions of flows across many of the Meta App offerings. Over the past 10 years, Meta has cataloged millions of data assets and is classifying them daily, supporting numerous privacy initiatives across our product groups. Additionally, our continuous understanding approach ensures that privacy considerations are embedded at every stage of product development. At Meta, we have a deep responsibility to protect the privacy of our community. We’re upholding that by investing our vast engineering capabilities into building cutting-edge privacy technology. We believe that privacy drives product innovation. This led us to develop our Privacy Aware Infrastructure (PAI), which integrates efficient and reliable privacy tools into Meta’s systems to address needs such as purpose limitation—restricting how data can be used while also unlocking opportunities for product innovation by ensuring transparency in data flows ...