What does Zscaler’s data discovery feature primarily rely on?

Zscaler’s data discovery feature primarily relies on machine learning algorithms to effectively identify, classify, and protect sensitive data within an organization. These algorithms can analyze large volumes of data dynamically, recognizing patterns and flagging sensitive information without the need for manual intervention. By utilizing machine learning, Zscaler can continuously improve its detection accuracy and adapt to new data types and threats over time.

In contrast, options like internet browsing history analysis and manual data entry reviews would not provide the same level of comprehensive insight into the data landscape as machine learning does. While internet browsing history can provide context, it does not directly relate to the identification of sensitive data throughout the organization's entire dataset. Manual reviews are labor-intensive and prone to human error, limiting their effectiveness. Lastly, third-party audits may provide insights on compliance and security postures but do not focus on real-time data discovery and management, making them an inadequate substitute.