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How Data Classification Can Help You Comply with GDPR?

Published on July 7, 2022

Data classification involves identifying and categorizing all data in a database into certain types based on their associated risk value. For instance, details such as home address and contact details of a British resident are classified as PII per the GDPR.

Data can be classified in a number of different ways, and classification entails organizing data into fixed categories that represent different types of data. Following are the categories in which data can be classified:

  • Public data
  • Confidential data
  • Sensitive data
  • Personal data

Data Classification is becoming an integral part of any organization. Data is growing at an exponential rate, and 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 goes to show that regardless of external issues, data is still growing.

Organizations that store all this data have a mix of personal data as well as sensitive data of their consumers, and classifying them based on their nature is key towards compliance. Classification can help organizations implement appropriate security and governance controls over the data. Furthermore, classification can also help organizations with data analytics, decision making and reduce storage and maintenance costs by enabling your organization to eliminate unneeded data.

Data Classification for GDPR

GDPR requires organizations to protect the data of their consumers and ensure proper security controls are in place. Data classification can help organizations organize their stored data based on assumed risk and then act on it accordingly. Data classification can help you stay compliant with the GDPR in the following ways:

  1. Organize data and implement appropriate security controls
  2. Retrieve consumers data and fulfill data subject requests
  3. It can be used in conjunction with monitoring tools to secure sensitive data
  4. Detect anomalies and work proactively to curb any data threat

Cleaning up data

Another important part of GDPR compliance is ensuring that if your organization has no business use for the stored data, then the organization needs to make sure it is deleted as appropriate. Data classification can help you locate the data not currently in use and delete it. Data classification can help you figure out what is contained within certain files, which can then help you decide whether or not you need it. It is a good idea to also have a record of what you delete and why. GDPR accountability is crucial and requires the data owner to be aware of all the data stored and whether it should be deleted or not, and why.

Data Classification Plan

In order to implement proper data classification, organizations need to have a proper plan in place. There is no single plan that can be used, but there are some key steps that every plan should have.

Step 1 – Discover Sensitive Data

Organizations need to classify their backlog of data by discovering and categorizing it. This process, although it can be done manually, should be done through the means of a data discovery tool in order to remove any error and make the process more efficient.

Step 2 – Assess the Results

Once the data has been discovered, the next step is to analyze whether this data has sufficient security controls. Discovering this data can help you identify the people that have access to the files and assign roles accordingly. Giving excessive access can pose a security threat.

Step 3 – Be Proactive and Continuous

Data classification is not a one-time process and needs to be constantly monitored. Data is constantly changing within an organization which in turn means that the data classification needs to be changed as well. In order to make this process simple, it is advised to use an automated tool to undergo scheduled tasks.

Step 4 – Risk Assessment

It is important to assess the risk on data and classifying this risk into different categories. A simple way to categorize them would be low, medium, and high risk:

  • Low risk: Any information that could have no Personally Identifiable Information (PII) but still may be disclosed to the public.
  • Medium risk: Data that may have PII but would deem useless on its own. This data still needs to be protected but will not have a substantial effect.
  • High risk: Data such as name, address, and credit card information, which is deemed as highly sensitive, all found in the same file, at risk of being disclosed to the public.

Data classification is one of the most important steps towards ensuring that the sensitive data within your organization is secure. This can help your organization comply with privacy regulations such as the GDPR.

How can Securiti Help

GDPR requirements require organizations to have tools that can accurately identify records from diverse data sources that match predefined criteria. They require the solution to be dynamic and refreshable, scalable, result in fewer false positives, work with structured and unstructured datastores, handle sensitive information securely and be applicable for SaaS apps or IaaS data stores.

Securiti's Exact Data Match (EDM) Classification solution is designed to detect and secure customer's most sensitive content, particularly data such as MRN, bank account numbers, SSN 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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