Data masking and redaction
Data masking and redaction are techniques used to protect sensitive information by obscuring or replacing it with fictional or anonymized data. These techniques are especially important when sharing or using sensitive data for non-production purposes, such as development, testing, or analytics, while ensuring that the original data remains confidential.
Sensitive data should be masked or redacted to prevent unauthorized exposure. For instance, credit card numbers can be masked to display only a subset of digits to non-authorized users.
Key aspects of data masking and redaction are as follows:
- Confidentiality: Data masking and redaction help preserve the confidentiality of sensitive information, preventing unauthorized access
- Privacy compliance: These techniques assist in adhering to privacy regulations by ensuring that sensitive data is not exposed
- Data utility: While securing data, data masking and redaction aim to retain the usefulness of the data for testing and analysis purposes
Data masking
Data masking involves modifying sensitive data in such a way that the original value is replaced with a fictitious but consistent value. The goal is to ensure that the masked data retains the same format, structure, and relationships as the original data.
Data redaction
Data redaction goes a step further by not only changing the content of sensitive data but also altering its format or structure. This ensures that even metadata or contextual information related to sensitive data is obscured.
Implementing data masking and redaction involves the following:
- Identify sensitive data: Determine which data elements are sensitive and require masking or redaction.
- Define masking rules: Develop rules that dictate how sensitive data should be transformed while retaining data consistency.
- Testing and validation: Verify that masked or redacted data maintains its usefulness for testing or analytics purposes.
Tools and methods for data masking and redaction include the following: - Database masking tools:
• Delphix: Offers data masking and virtualization capabilities
• Informatica Data Masking: Provides masking and redaction solutions - Database redaction features:
• Oracle Database Redaction: Built-in feature for redacting sensitive data in Oracle databases
• SQL Server Dynamic Data Masking: Masks sensitive data in SQL Server databases - Anonymization tools:
• ARX Data Anonymization: Offers privacy-preserving data anonymization solutions - Custom scripting:
• Organizations might develop custom scripts or applications to apply data masking and redaction - Database views and synonyms:
• Create views or synonyms that present masked or redacted data instead of original data
Data masking and redaction are valuable techniques for protecting sensitive data while maintaining its utility for various purposes. By applying these techniques, organizations can balance the need for data security and data usability within cloud environments.