While SIEM systems can analyze log data, they don’t typically discover and classify sensitive data. Vulnerability scanners focus on identifying system weaknesses. Network monitoring tools primarily analyze network traffic patterns.
A Data Discovery and Classification tool would be most suitable for automatically discovering and classifying sensitive data across the entire IT infrastructure. These tools are designed to scan various data repositories, including databases, file shares, and cloud storage, to identify and categorize sensitive information such as personal data, financial records, or intellectual property. They use pattern matching, regular expressions, and machine learning algorithms to recognize different types of sensitive data. Once identified, the data can be automatically classified based on its sensitivity level, enabling organizations to apply appropriate security controls and ensure compliance with data protection regulations. This automated approach helps organizations understand where their sensitive data resides and how it’s being used, which is crucial for effective data governance and risk management.