All data evaluated by DLP runs through the platform’s API, whether it’s gigabytes or terabytes of information. Google says that it never logs or stores any data, but DLP is too resource-intensive to run locally. And for Google Cloud Platform customers this is less of a consideration anyway, since they already store their data with the company.

DLP’s main goals are classification of sensitive data, particularly identifying data, and thorough masking and deidentification, so that data can still be used for things like research or analysis without creating a privacy risk to individuals. The platform also analyzes risk for large quantities of data, and flags potentially problematic aberrations.

But DLP’s new user interface will at least make it easier for small businesses or other organizations without extensive IT resources to get some data de-identification benefits.

Though not every company is facilitating massive medical studies, DLP can also be helpful for general ass-covering—with real potential benefits to users. Misconfigurations in cloud platforms that lead to data continue to represent a major societal privacy issue. But a company that has redacted its data with DLP will at least avoid leaking identifiable information if its cloud administrators make an error in setting up data access controls.

But the ability to mask this data and then do risk analysis and say ‘what else did we not find that might be a statistical outlier? That’s really important.