In what way does Zscaler classify documents and data automatically?

Zscaler classifies documents and data automatically primarily through the use of artificial intelligence and machine learning technology, leveraging anonymized data from millions of documents. This approach allows Zscaler to analyze patterns and behaviors within the data, making it possible to identify various classification criteria efficiently.

By utilizing extensive datasets for training these AI/ML models, Zscaler can automate the classification process to provide more accurate and timely assessments of data. This method reduces the need for manual intervention and enhances the scalability of data management, as the system continually learns from new data and adapts its classification strategies accordingly.

The other approaches, while they can be components of an overall security strategy, are not automated methods for document classification. User reporting relies on individuals identifying suspicious content, which is not scalable or efficient for classification. Manually created rules require ongoing oversight and updates from administrators, which can be resource-intensive. Lastly, third-party audits focus on compliance and security assessments rather than real-time data classification. Hence, the AI/ML approach stands out as the most effective automated solution.