You Don’t Know What You Don’t Know: 5 Best Practices for Data Discovery and Classification

This is the second installment in a two-part series about data discovery and classification. Be sure to read part one for the full story.

Discovering and classifying data across the enterprise is crucial to any data protection strategy, but it can be complicated due to the constantly shifting nature of the cybersecurity landscape, the difficulty of unifying processes across diverse environments and the sheer scale of the task at hand.

5 Tips for Effective Data Discovery and Classification

If you’re feeling overwhelmed trying to keep track of and meet the myriad data security and compliance requirements organizations face today, the following five best practices can help you develop effective data discovery and classification processes, which can help address the data security, data privacy and compliance requirements for your organization.

1. Automate Your Processes

In today’s data-centric world, it’s simply no longer possible to do data discovery and classification manually. It’s inaccurate and inconsistent, and thus, very risky. People make mistakes, and these mistakes can mean that your data is misclassified or not classified at all. As a result, your data may not be protected properly, or you may not be in compliance. Manual classification is also incredibly time-consuming.

Look for a solution that automates data discovery and classification and supports multiple methods for classification, such as catalog-based search, regular expression and patterns, as well as next-generation data classification, which can search data directly from within a table. This enables more expressive results and delivers higher accuracy.