The term "Data Nulling" refers to the targeted deletion or obfuscation of personal or sensitive data within a system or dataset. The goal is to modify data in such a way that it can no longer be attributed to a specific individual or reconstructed. Data nulling is commonly used in the context of data protection regulations such as the GDPR, for example, to fulfill deletion requests, anonymize data, or prepare datasets for secure sharing with third parties.
Rule-Based Deletion: Automated removal of data based on predefined criteria (e.g., time-based rules, user requests).
Field-Level Overwriting: Replacing individual data fields with placeholders or neutral values such as "NULL" or "***".
Selective Nulling: Deletion or masking of specific categories of data (e.g., contact details, financial information) while retaining the rest of the record.
Audit Logging: Logging all nulling actions for traceability and compliance purposes.
Custom Anonymization Profiles: Configuring specific nulling strategies for different data types or scenarios.
Integration with Data Privacy Workflows: Linking with processes such as managing data subject rights (e.g., “right to be forgotten”).
A company deletes personal data of a former customer from its CRM system in response to a GDPR deletion request.
When sharing data with an external service provider, sensitive fields like names and phone numbers are automatically masked.
After legal retention periods expire, certain database fields are reset to "NULL".
An HR system overwrites confidential data of former employees with placeholders while preserving statistical information.