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Data classification involves identifying and categorizing all data in a database based on their associated risk value. For instance, an EU resident's home address and contact details would be classified as personal data under the GDPR. Whereas certain data types, such as genetic data or health data of an EU resident, would be considered special categories of personal data (also known as ‘sensitive personal data’, with strict requirements for processing.
Data can be classified into personal data and sensitive personal data under the GDPR. Personal data is any data relating to an identified or identifiable natural person. An identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to one or more identifiers, such as a name, an identification number, or an online identifier. Sensitive personal data constitutes personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic data, biometric data, which is processed for uniquely identifying a natural person, data concerning health, or data concerning a natural person’s sex life or sexual orientation. Processing of sensitive personal data is prohibited unless one of the listed exceptions under the GDPR applies.
Data Classification is becoming an integral part of any organization’s operations. Data is growing at an exponential rate. According to IDC, ‘in 2020, 64.2ZB of data was created or replicated, defying the systemic downward pressure asserted by the COVID-19 pandemic on many industries, and its impact will be felt for several years.’ This shows that the amount of data is speedily growing regardless of external issues.
Many organizations process a mix of personal and sensitive personal data of different data subjects. Classifying such data based on their nature is key to legal compliance. Data classification can help organizations implement appropriate security and governance controls over data. Furthermore, classification can also help organizations with data analytics and decision-making and reduce storage and maintenance costs by enabling organizations to eliminate unneeded data.
GDPR requires organizations to protect their consumers' data and ensure proper security controls are in place. Data classification can help organizations categorize their stored data based on assumed risk and then take appropriate security, protection, and governance measures. Data classification can help you stay compliant with the GDPR in the following ways:
An important part of GDPR compliance is ensuring that if your organization no longer requires any category of stored data for the purposes it was collected, then the organization needs to make sure that it is deleted as appropriate. Data classification can help you locate the data not currently in need or use and delete it. Data classification can help you determine what is contained within certain files, which can help you decide whether or not you need them. Having a record of what you delete and why is also a good idea. GDPR renders accountability a crucial step of compliance and requires the data owner to be aware of all the stored data and whether it should be maintained or deleted, along with the accompanying justification.
To implement proper data classification, organizations need to have a proper plan in place. There is no single plan that every organization can adopt, but there are some key steps that every plan should have.
Organizations need to classify their backlog of data by discovering and categorizing it. Although it can be done manually, this process should be done through a data discovery tool to remove any chances of error and make the process more efficient.
Once the data has been discovered, the next step is to assign sensitivity levels to each category of personal data, taking into account the consequences of any potential breach, and analyze whether the stored data has sufficient security controls as per their nature. GDPR requires organizations to implement appropriate technical and organizational security measures to protect any personal data processed by them. Discovering data can also help identify the individuals with access to the files and revamp access controls if necessary. Allowing excessive access to data can pose a security threat.
Data classification is not a one-time process but an ongoing initiative requiring constant monitoring. The nature and amount of data an organization processes constantly changes, necessitating corresponding adjustments in the classification schema. To make this process facilitative, it is advised to use an automated tool to undergo scheduled and routine tasks.
Following classification, it is important to ensure that an organization complies with all its GDPR obligations per the categories of data they are processing. They should tailor data protection measures according to data sensitivity and risks, including encryption and access controls. Further, they should set appropriate data retention periods and maintain accurate records of processing activities (RoPAs). Organizations must conduct Data Protection Impact Assessments (DPIAs) for high-risk processing operations to proactively identify and mitigate potential data-related risks.
Data classification is one of the most important steps toward ensuring that the sensitive data within your organization is secure. This can help your organization comply with privacy regulations such as the GDPR.
An integral aspect of GDPR compliance is adequate knowledge and understanding of the categories of personal data collected and processed by an organization. This is the foundation of all further compliance initiatives. Thus, organizations need a dynamic, refreshable, and scalable solution that results in fewer false positives, works with structured and unstructured data stores, handles sensitive information securely, and is applicable for SaaS apps or IaaS data stores.
Securiti's Exact Data Match (EDM) Classification solution is designed to detect and secure customers' most sensitive content, particularly data such as MRN, bank account numbers, or SSNs, with zero false positives. The sensitive data used in exact data indexing can be periodically refreshed for any incremental changes.
The solution provides the ability to define the templates for Exact Match lookup data, refresh sensitive content used for Exact Match Indexing, and create Exact Data Match classification profiles, which can be applied across our 150+ datastores.
To learn more about how you can classify data with the help of EDM. Request a demo!
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