As organizations evolve, traditional data classification—typically designed for regulatory, finance or customer data—is being stretched to accommodate employee data. While classification processes and ...

In an era where sensitive data is a prime target for cyberattacks and compliance violations, effective data classification is the critical first step in safeguarding information. Recognizing the ...

Data stream classification and concept drift detection are essential components in the realm of real-time data analytics. As data streams continuously flow from sources such as sensors, financial ...

The addition of LLM to Sentra’s classification engine allows scanning and classifying sensitive enterprise data like source codes, and employee details. Classifying sensitive unstructured data like ...

A data storage strategy that addresses data sovereignty builds on the classification of data in the data audit to limit what data can go where. As part of the classification process, data will be ...

All college data are classified into levels of sensitivity to provide a basis for understanding and managing college data. Accurate classification provides the basis to apply an appropriate level of ...

To ensure a common understanding, Harvard uses a 5-step scale for data sensitivity. The higher the number, the more sensitive the data is, and the stronger protections you need to take when accessing ...

Corporate efforts to secure data, comply with regulations, tier storage and meet new legal-discovery demands depend on having a good data-classification method in place. Traditional classification ...

The Komprise data management survey shows that 74% have 5PB or more of stored data and 85% of participants expect to spend ...