Data sprawl refers to the growing volumes of data produced by organizations, and the difficulties this creates in effectively managing and monitoring this data. As companies collect more data – both internally and through the broader range of enterprise software tools in use today – and increase the amount of storage systems and data formats, it can become difficult to understand which data is stored where. This can lead to increased cloud costs, inefficient data operations, and data security risks as the organization loses track of where sensitive data is stored – and fails to apply adequate security measures as a result.
To mitigate the impact of data sprawl, automated data discovery and classification solutions can be used to scan repositories and classify sensitive data (see: The Big Guide to DSPM). Establishing policies to deal with data access permissions can also be beneficial. Data loss prevention (DLP) tools can detect and block sensitive data leaving the organizational perimeter, while DDR tools offer similar functionality in public cloud deployments.