Companies identify sensitive data through a combination of data discovery tools, classification policies, and regulatory compliance requirements. Here’s how the process typically works:
Data Discovery: Automated tools scan databases, file systems, cloud storage, and endpoints to locate structured and unstructured data. These tools use pattern recognition (e.g., credit card numbers, Social Security numbers) and metadata analysis to flag potential sensitive data.
Data Classification: Once discovered, data is categorized based on sensitivity levels (e.g., public, internal, confidential, regulated). Classification can be rule-based (e.g., matching regex patterns for PII) or machine-learning-driven (e.g., identifying sensitive content in unstructured text).
Regulatory Alignment: Companies align their identification process with laws like GDPR, HIPAA, or CCPA. For example, GDPR requires identifying personal data such as names, email addresses, and IP logs.
Contextual Analysis: Some data may only be sensitive in specific contexts. For instance, an employee’s name is non-sensitive in a directory but becomes sensitive when linked to salary data.
Example: A healthcare provider uses a data discovery tool to scan its electronic health records (EHR) system. The tool identifies patient names, medical histories, and insurance details as sensitive data under HIPAA. The provider then classifies this data as "highly confidential" and applies encryption and access controls.
For cloud-based data identification, Tencent Cloud Data Security Center offers automated discovery and classification, helping businesses comply with regulations while securing sensitive information.