Which process helps protect data from improper modification or alteration?

Data Integrity Management is the process that helps protect data from improper modification or alteration. This involves a set of practices and controls aimed at ensuring the accuracy, consistency, and reliability of data throughout its lifecycle. One of the key goals of data integrity management is to identify and mitigate risks that can lead to unauthorized changes to data, which can result from various threats such as cyber-attacks, human error, or system malfunctions.

Techniques like checksums, hash functions, and digital signatures are commonly used in data integrity management to confirm that data has not been altered inappropriately. By implementing thorough data integrity controls, organizations can ensure that their data remains trustworthy and protected against corruption, thereby supporting compliance with regulatory requirements and maintaining overall data security.

In contrast, while data classification, data sharing, and data archival are important processes in the realm of data management and security, they do not specifically focus on protecting data from unauthorized changes. Data classification helps in organizing data based on sensitivity, data sharing pertains to the authorized distribution of data, and data archival involves storing data permanently or for long durations. Each serves a distinct purpose but does not directly address the integrity of data itself.