  Content based data classification is an open challenge. Traditional Data Loss
Prevention (DLP)-like systems solve this problem by fingerprinting the data in
question and monitoring endpoints for the fingerprinted data. With a large
number of constantly changing data assets in Facebook, this approach is both
not scalable and ineffective in discovering what data is where. This paper is
about an end-to-end system built to detect sensitive semantic types within
Facebook at scale and enforce data retention and access controls automatically.
  The approach described here is our first end-to-end privacy system that
attempts to solve this problem by incorporating data signals, machine learning,
and traditional fingerprinting techniques to map out and classify all data
within Facebook. The described system is in production achieving a 0.9+ average
F2 scores across various privacy classes while handling a large number of data
assets across dozens of data stores.
