The Importance of Data Classification in a Post-GDPR World
Data classification is the primary focus of many compliance standards and requirements. By identifying and classifying data, and appropriately protecting it, you can meet compliance requirements, reduce legal risk, prioritize the implementation of security controls, and allocate resources more effectively.
With the introduction of the General Data Protection Regulation (GDPR) in the European Union, data classification became more important than ever for companies that store, transmit or process personal data relating to EU citizens. These companies need to be able to classify their data so that they can easily identify content relevant to GDPR requirements, and take appropriate security measures.
GDPR Data Classification Requirements
When processing personal data subject to the GDPR, your organization is responsible for providing an adequate level of security, including protecting against unauthorized disclosure and destruction, in accordance with Article 5 of the GDPR.
In addition, the most advanced organizational and security measures must be used to protect the data—including encryption and pseudonymization (replacing real user content with realistic, fake content).
Sensitive data such as race and ethnicity are also defined as special categories of data in Article 9 of the GDPR, so special attention should be paid if you store this type of data.
Addressing all of these challenges requires a comprehensive understanding of the types of data you have, where you hold it, and its properties.
How Can Data Classification Help Your Organization Comply with GDPR?
GDPR requires organizations to protect consumer data and adopt appropriate security controls. Data classification can help organizations understand the data they own, and take appropriate action based on potential risks.
A key strategy for classifying data is the use of automated data classification. Automated classification can identify the class of a file or message using a predefined set of rules, statistical analysis, or more advanced machine learning techniques. These rules or analyses identify keywords or expressions found in the content that indicate its level of sensitivity. At the simplest level, if the document contains the word “confidential” or “secret”, it is classified as sensitive data.
Automated classification can extend classification to a variety of large-scale datasets, including data that is outside your organization’s control. It is highly useful for data from an automated process or system, such as reports generated from ERP or accounting systems, which need to be categorized at the time of creation without user intervention.
Automated data classification can help you stay compliant with GDPR in the following ways:
- Lets you organize your data and implement appropriate data protection controls.
- Enables storing and retaining data in line with specific requests from the data subject
- Ensuring data is properly deleted when it is no longer used, to reduce exposure and conserve cloud storage costs.
- Enables the use of monitoring tools to identify and protect sensitive data that is not appropriately protected.
- Enables detection of anomalies, and proactive mitigation of threats to data.
- Reduce the cost of securely maintaining and storing data by identifying duplicate and stale data.
In this article I explained GDPR requirements in brief, and showed how automated data classification can have a range of benefits. These include making it possible to organize and secure data for compliance purposes, assisting with deletion of data that is no longer needed, enabling monitoring and alerting, and reducing the cost of compliance.
I hope this will be of help as you explore automated strategies for meeting your organization’s data compliance obligations.