Moving large volumes of data from data warehouses to a data lake is usually a gnarly process. So before a data migration, the CISO of a large U.S. university was concerned about security risks in the cloud. If a data breach occurred, thousands of student data records and valuable intellectual property would be exposed. The school could face penalties from governments at home and overseas. So, his priority was to get a migration solution that provided safe, private storage and reduced the risk of data breaches and theft.

The head of data platforms of a large bank engaged Protecto, expressing special concern about the division�s data warehouse, which had grown to billions of rows of data but without an equal growth in security and risk management controls. Protecto gave unprecedented visibility into the data risks facing the division, with granularity down to the individual user and database table level.

Protecto saved several months of work that the privacy team would have spent in analyzing and discovering data protection (potential GDPR) issues across one million files and tables. Our platform identified sensitive data. Our insight that 99% of the data was never used helped the customer reduce their data and associated risks tenfold.

Our solution helped a Silicon Valley consumer technology company with over 100M users. They were expecting to find ~20 data tables, but our solution found 5700 tables across their data lake. The data analytics team was surprised to see 92% of the data was stale that no one in the company was using it. Many of the stale data tables had personally identifiable information, posing a significant risk.

Finding data security and privacy risks in an enterprise is complex, time-consuming work. Our platform applies AI, data analysis, and privacy engineering to automate complex steps.