Protect Customers Data in Snowflake
ACME’s service relies on advanced analytics to transform how public transportation systems operate into dynamic, on-demand and efficient route planning. That means that multiple teams need access to the central Snowflake data lake and data warehouse.
The traditional way of making sure that only authorized employees have access to sensitive data is to abstract the raw data using secured views and implement role based access controls.
The challenges in this approach is that maintaining views and role based access control places a huge operational burden on the data and security teams. Read more...
Enable Analytics for Datasets with PII Aware Masking
ACME’s service relies on advanced analytics to provide the service and to monitor business performance. That means that multiple teams need access to a central Snowflake data warehouse and an S3 based data lake.
The traditional way of providing analytics on top of PII is to create a masked copy of the data for analytics that is separated from the raw data.
The challenges in this approach was twofold. First, the PII in ACME data stores is collected through forms their customers publish on their websites. Neither ACME nor their customers have control over what data is shared in these form fields. Read more...
Continuously Monitor PII for Agile Compliance
ACME’s service relies on advanced analytics to optimize their food delivery operation. That means that hundreds of analysts and data consumers ingest, create new analytics and consume data on a daily basis.
The traditional way of monitoring sensitive data in a data store is either optimized for control through slow and time consuming manual review and approval processes for any new data type or optimized for completeness with periodic scans of the entire data landscape and manual validation. The challenges in those approaches is that ACME business goals do not allow for slowing down analytics teams. Read more...