Privacy Controls for Data Flow

With ConsentGrid™ Privacy Controls for Data Flow technology your organization can:

Eliminate development and maintenance costs for ad hoc privacy implementations.
Remove or mask sensitive data as it moves through ETL pipelines for data warehousing or machine learning, or when exchanged with third-parties.
Manage data exchange and data usage policies with reusable privacy components.
Implement privacy-by-design principles by embedding automated privacy controls into data processes.

Secure and Private

Privacy controls for data flow technology uses components that are installed on the host data center or cloud provider. Sensitive data never leaves the host network.

Filter/Mask Data

ConsentGrid™ works as a filter intercepting data flow between applications or organizations. It tracks the flow of personal data between data processing locations and uses consent and policies to filter or mask data in-flight.

Data Agnostic

Privacy controls for data flow uses schemas to add a privacy layer to JSON, XML, and CSV documents.

Anonymize, pseudonymize

Mask, filter, or redact data fields using consent and organizational policies based on the situation at hand. Anonymize data in-flight as it moves between systems or locations.

Layered Flow Policies

Build data exchange policies by composing overlays to classify, mask, and filter data.

Integrate consent to data flow

Integrate user consent to data flow to process records with valid consent. Use granular consent to remove parts of data during exchange.

How does it work?

Privacy control for data flow technology uses components that are installed at the host organization network. Sensitive data never leaves the network.

1 Use schemas to describe your business entities used in data processing and data exchange.

2 Define overlays to classify data, to represent privacy constraints on fields, and to mask/remove fields.

3 Define data flow policies by combining overlays.

4 Use ConsentGrid™ APIs to process data with privacy controls before exchanging data with a third-party, before loading data to a data warehouse, or after extracting data from a system for processing.