PRIV- 22 – Data Masking

What is this control about?

Data masking is a crucial control for safeguarding sensitive data and ensuring data privacy and security within an organization. It involves the process of disguising original data by replacing, scrambling, or encrypting sensitive information with realistic but fictitious data. The primary purpose of data masking is to protect sensitive information from unauthorized access and disclosure while still allowing the use of data for development, testing, or other non-production purposes.

Available tools in the marketplace

Tools:

Delphix Informatica IBM InfoSphere

Available templates

TrustCloud has a curated list of templates internally or externally sourced to help you get started. Click on the link for a downloadable version:

• N/A – No recommendations

Control implementation

Here are some guidelines to implement a Data Masking:

• Identify Sensitive Data: Start by identifying all sensitive data elements within the organization’s databases, files, and systems. This includes personally identifiable information (PII), financial information, health records, and any other data that requires protection.
• Define Masking Rules: Work with data owners, data custodians, and stakeholders to define masking rules for each sensitive data element. Determine the appropriate masking technique for each data type (e.g., randomization, encryption, substitution) to ensure data privacy while maintaining data utility for non-production purposes.
• Select Data Masking Tool: Evaluate data masking tools available in the market and choose one that aligns with the organization’s needs and requirements. Ensure that the selected tool supports the data sources and formats used in the organization.
• Build Test Environment: Set up a dedicated test environment that replicates the production data but without exposing sensitive information. This environment will be used for development, testing, and quality assurance purposes.
• Create Data Masking Policies: Develop data masking policies based on the defined rules and requirements. The policies should outline how each sensitive data element will be masked and the criteria for applying the masking technique.
• Implement Data Masking: Use the selected data masking tool to apply the defined data masking policies to the test environment. Execute the masking process to anonymize or obfuscate sensitive data while ensuring referential integrity and data consistency.
• Test Masked Data: Verify the masked data in the test environment to ensure that the data has been appropriately masked and that the masking does not affect the functionality of the applications or systems that use this data.
• Monitor and Audit: Regularly monitor the masked data to ensure that the masking remains effective and that any new sensitive data elements are included in the data masking process. Perform periodic audits to assess compliance with data masking policies and regulations.
• Document and Train: Document the data masking procedures, including the rules, policies, and processes used. Provide training to relevant staff members on how to use the data masking tool correctly and adhere to data masking policies.
• Secure Access: Ensure that access to the masked data is restricted to authorized personnel only. Implement strong access controls and security measures to protect the masked data from unauthorized access.
• Update Data Masking as Needed: As new sensitive data is identified or regulations change, update the data masking policies and procedures accordingly. Regularly review and enhance the data masking implementation to adapt to evolving security requirements.

What evidence do auditors look for?

Most auditors, at a minimum, are looking for the below-suggested action:

1. Provide Data Masking Policy and Procedures

Evidence example

For the suggested action, an example is provided below:

1. Provide Data Masking Policy and Procedures

Use this template Data Masking Policy