Explanation/Reference:
Data edits are implemented before processing and are considered preventive integrity controls.

Section: Protection of Information Assets
Explanation:
Criticality of assets is often influenced by the business criticality of the data to be protected and by the
scope of the impact upon the organization as a whole. For example, the loss of a network backbone
creates a much greater impact on the organization as a whole than the loss of data on a typical user's
workstation.

Before implementing a DLP solution, it is crucial to understand the organization's data landscape.
Conducting a data inventory and classification exercise helps identify the types of data that need to be protected and their sensitivity levels. This step involves identifying the locations, formats, and repositories where data resides, as well as categorizing the data based on its sensitivity and regulatory requirements. This exercise forms the foundation for developing effective DLP policies and controls.

The best way to prevent accepting bad data from a third-party service provider is to implement business rules to reject invalid data. Business rules are logical expressions that define the business requirements and constraints for specific data elements. They can be used to validate, transform, or filter incoming data from external sources, ensuring that only high-quality data is accepted into the enterprise data warehouse. Business rules can also help to identify and resolve data quality issues, such as missing values, duplicates, outliers, or inconsistencies.