PRIV- 21 – Data Anonymization and De-identification

What is this control about?

Implementing the control ‘Data Anonymization and De-identification’ is important because it plays a critical role in safeguarding individuals’ privacy and protecting sensitive information. In today’s data-driven world, organizations collect and process vast amounts of personal data, which can include sensitive details about individuals’ identities, habits, and preferences. However, retaining and using this data without proper safeguards can expose the organization to significant privacy risks and potential legal liabilities.

Data anonymization and de-identification are techniques used to transform or remove personally identifiable information (PII) from datasets, making it impossible to link the data back to specific individuals. By doing so, the data becomes less sensitive, reducing the risk of potential harm or misuse if a data breach or unauthorized access occurs

Available tools in the marketplace

Tools:

Privitar Talend Micro Focus

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 Anonymization and De-identification:

• Identify Sensitive Data: Start by identifying all the sensitive data elements within the organization’s datasets. This includes personally identifiable information (PII) such as names, addresses, social security numbers, financial data, and any other information that could potentially identify individuals.
• Classify Data Sensitivity: Categorize the data based on its sensitivity level. Classifying data into different sensitivity tiers will help in determining the appropriate anonymization and de-identification techniques to be applied.
• Determine Anonymization Techniques: Based on the sensitivity classification, select the appropriate anonymization techniques to be used. Common techniques include generalization (e.g., replacing exact values with ranges), pseudonymization (replacing identifiers with unique but non-identifiable values), and suppression (removing certain data fields entirely).
• Create Anonymization Policies: Develop clear and comprehensive anonymization policies that outline how each data element will be anonymized and the specific techniques to be applied. These policies should be aligned with data privacy regulations and best practices.
• Implement Data Anonymization: Utilize data anonymization tools or scripts to apply the anonymization policies to the datasets. Ensure that the original sensitive data is replaced with the anonymized versions effectively, and the process does not introduce any data integrity issues.
• Verify Data Quality: After anonymization, conduct thorough testing and verification to ensure the quality and accuracy of the data. Check for any anomalies or errors that might have arisen during the anonymization process.
• Document and Monitor: Document all the steps taken for data anonymization and de-identification, including the techniques used, policies applied, and testing results. Implement a monitoring process to regularly review and update the anonymization policies as needed.

What evidence do auditors look for?

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

• Provide the Anonymization Policies and Procedures

Evidence example

For the suggested action, an example is provided below:

• Provide the anonymization Policies and Procedures. Here is an example