Each enterprise has their own set of sensitive data, however, protecting this can be a challenge. In this section, we will go over best practices on how to successfully implement a DLP solution.
To start, enterprises should decide which information, if it were stolen, would pose the biggest threat. Classifying data by context is an efficient, scalable method. This entails attaching a category to the database, source application, or user who originally produced the data. Understanding why protocols are being made and developing measures to minimize data risk can eliminate the common, problematic challenges in data loss. Organizations can create more specialized, fine-tuned controls to lower risks as the DLP program evolves.
Moreover, data distribution to user devices or sharing with partners, clients, and supply chains can pose major risks. In these situations, data is most vulnerable while it is being used on endpoints. Data mobility needs to be considered by a comprehensive DLP program to prevent on-going data breaches. These endpoints include data being transferred to a removable storage device or attached to an email.
To safeguard against unauthorized data activity, user training can lower the likelihood of insiders accidentally erasing data when information is moved. Educating employees helps prevent inadvertent disclosure and wrongful deletion. Comprehensive DLP solutions will also be able to notify data administrators any of such incidents.