In today’s information security, the protection of data is a legal obligation, and it is critical for the survival and profitability of institutions. With the reduction of storage costs, organizations began to store their data for more extended periods. But big data can also cause serious security problems. Most of the data collected may consist of redundant, obsolete, unimportant, or unknown data or may not have been touched for years.
Storage costs today may be low, but they are not free. Storing large amounts of data unnecessarily increases costs and, more importantly, puts your organization at risk. Sensitive information stored digitally needs to be adequately secured. If finding important data feels like looking for a needle in a haystack, your organization isn’t safe.
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We can briefly define data classification as organizing the organization’s data by categorizing it according to its criticality levels. Thanks to the healthy completion of this process, it becomes easier to find the relevant data when needed and process it within the framework of the determined policy.
In determining the criticality level to be considered within the scope of data classification, the effects that may occur for the organization in case of data being changed, stolen, or destroyed should be considered. So a suitable risk assessment is required. Here, an organization’s risk profile, risk culture, and risk appetite will also play a vital role.
What is Data Classification?
Data classification is broadly defined as organizing data according to relevant categories so that it can be used and protected more efficiently. At a basic level, the classification process simplifies the finding and retrieval of data. Data classification is essential when it comes to risk management, compliance, and data security.
The role of data classification is of great importance. First, we need to identify the data or data that we are trying to secure. Data classification is an important step and is essential in determining the nature, sensitivity, and value of data. The collected information is then divided into predefined groups that share a common risk.
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Data classification is essential in ensuring that sensitive data is handled appropriately. Because security rules are created to secure each group within itself, data categorization technologies can help with sensitive data management and security. Brightly colored warning labels indicate data that is confidential or sensitive. The use of all this leads us to use and behave more carefully with data.
Warning labels, on the other hand, are essential against potential problems and dangers. Data classification ensures the protection of confidential and sensitive data. In addition, it prevents the protection of confidential and sensitive data from falling into the hands of third parties. When data classification is made into a daily routine, the level of security increases, the risks that may arise are reduced, and security breaches are minimized.
Why Should Data be Classified?
Data classification provides many benefits. Data classification is invaluable for effectively prioritizing your security controls and ensuring your most critical assets are adequately protected. For example, you can encrypt all documents classified as “restricted.”
Data classification simplifies risk management by helping organizations assess the value of their data and the impact it will have if certain types of data are lost, misused, or compromised. Data classification also simplifies regulatory and mandatory standards requirements such as PCI and improves user productivity by making data easier to find.
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It is also essential to ensure regulatory compliance and pass audits in both the public and private sectors, helping organizations protect the confidentiality of regulated data such as cardholder data (PCI DSS), health records (HIPAA), or GDPR.
Consider the following advantages of data classification:
- Identifying critical data for your organization and generally leading to a better understanding of the value of data as an information asset.
- Data classification helps restrict access to personal data and intellectual property.
- It allows future information requests regarding personal data to be met more quickly.
- It allows the data to be tracked more effectively within the legal waiting period and business need and to archive or destroy it promptly.
- It helps to define security controls according to the criticality level of the data and even helps to reduce the threat surface for the organization by separating critical data from more open data.
- Data classification helps ensure compliance with SOX and PCI DSS standards and regulatory legislation such as GDPR.
- Allows reducing search time by eliminating multiple copies of data.
- It provides ease of access to data within the framework of business requirements.
- It helps to reduce storage and backup costs by helping to prevent duplicate data processing.
What Are the Types of Data Classification?
Data classification often includes multiple tags that describe the data type, confidentiality, and integrity. Usability can also be considered in data classification processes. The sensitivity of data is usually classified according to varying levels of importance or privacy, which is then associated with the security measures implemented to protect each classification level.
Data classification can be made according to the content, context, or user choices of the relevant data:
- Content-based classification: It includes the examination and classification of files and documents.
- Context-based classification: Files containing data can be classified based on metadata such as the applications that created the data, the profiles that created or modified the data.
- User-based classification: It is the manual classification of files by authorized personnel within user criteria.
- Automatic data classification: Using data classification tools such as Boldon James and TITUS, data in systems is detected, analyzed and the appropriate classification category is assigned. The file parser in these tools allows the data classification tool to read the contents of several different file types. A set of analysis systems then matches the data in the files with defined search parameters.
What Are Levels of Data Classification?
Different types of data necessitate different classification levels, which is understandable. In general, it is determined as critical, confidential, and public according to data classification levels. We can also use data classes as high, medium, and low.
Most organizations follow a similar practice. Determining more than three criticality levels will necessitate a more sensitive approach to control the relevant data. However, this situation will bring with it a lot of formalities and procedures that must be followed. Having less than three data classes can also be problematic from a different perspective, and accessing data can be easier for everyone.
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Multiple degrees of categorization must be utilized to decide a range of issues, such as who has access to the data and how long it should be maintained, depending on the sensitivity of the organization’s data. Data is usually divided into four categories: public, internal only, confidential, and restricted.
- Confidential: Data that must be kept within the framework of legal requirements requires strict access controls and protections, as it is protected by law, such as personal data, intellectual property issues, and can cause significant harm to an individual or organization if breached.
- Service Specific: for corporate use only, the impact of a data breach is non-destructive.
- Public: Low criticality, publicly available data that does not require any access restrictions.
- Restricted: Restricted data includes data that, if compromised or accessed without authorization, could result in criminal charges, significant legal fines, or cause irreparable damage to the company. Examples of restricted data may include private information or research and data protected by state and federal regulations.
In such a categorization, public data is expected to have the lowest security requirements. For confidential data, incremental and robust data security controls need to be implemented. Such a basic level of classification would provide a good and safe start for organizations.
The “principle of least privilege” must be strictly adhered to when processing internal, confidential, and restricted data. It should also be noted that the categories mentioned above may contain subcategories that may provide more specificity regarding how the data is accessed, how long the data should be retained, etc.
What Are the PCI DSS Data Classification Requirements?
Data security should be an essential component of all system policies and practices regarding credit card payment acceptance and processing. Customers seek reputable and reliable vendors, and they expect the reassurance that their account information is protected and their data is safe.
To comply with regulations regarding credit cards, you must comply with the PCI Data Security Standards. A credit card number with one or more of the following data components is referred to as payment card information.
- Card Holder’s Name
- Service code
- Expiration date
- CVC2, CVV2, or CID value
- PIN or PIN block
- Contents of the credit card’s magnetic stripe
PCI DSS requires data classification in terms of regular risk assessment and security classification process. Cardholder data must be classified by type, retention permissions, and necessary level of protection to ensure that security controls are applied to all sensitive data and verify that all cardholder data in the environment is documented.
PCI DSS Requirement 9.6.1 specifies that media must be classified to determine the sensitivity of the data. We can define the purpose of data classification for PCI DSS compliance as follows:
- Data should be classified to reduce the risks associated with unauthorized disclosure and access.
- The scope of the data environment must be defined, and then all in-scope data reviewed.
- You must define data sensitivity levels and classify data. It is recommended to start with a minimum number of classes not to overcomplicate the process.
- You must develop data processing guidelines to ensure the security of each category of data.
How Should Data Classification Be Applied?
One of the most important aspects of a data protection strategy is data classification. Data classification provides a strong foundation for data protection by defining data in an organization according to its criticality levels. Once you know what data is critical, it will be easier to determine who should access that data and under what conditions it can be shared.
To ensure adequate security, you must first identify the exact data you are trying to protect. Data classification is a critical step. It enables organizations to identify the business value of unstructured data at the time of creation, separate valuable information that can be targeted from less helpful information, and make informed decisions about resource allocation to ensure data is protected from unauthorized access.
The information is divided into predefined groups that share a common risk, and the security controls needed to secure each type of group are defined. Data classification tools can help with sensitive data processing. A culture of security that raises awareness of data sensitivity can be promoted so that it is not inadvertently disclosed. Sensitive content is not stored on removable media or third parties.
Data with warning labels in striking colors can change our behavior by making us aware of the dangers that may cause us harm. Visual tags and watermarks such as “Confidential” can remind users to think twice and be more careful with digital and printed data.
Successful data classification directs the security measures applied to a dataset and aids businesses in complying with regulatory requirements for accessing specified information within a specific time limit.
- Defining the targets: The regulations and standards affecting the organization are examined in detail, and the principles to be followed are determined.
- Categorizing data: It reveals which data categories are processed in the organization. The criteria to be used in data classification are determined. Next, the classification levels of the data are defined.
- Data classification processes are defined: Data scanning steps to detect existing and new data are represented with a written policy. Roles and procedures are determined within the policy framework.
- Performing data scanning and classification: After the screening and classification work is done within the framework of the determined criteria, the results obtained are confirmed.
- Determining the use of outputs and classified data: Determining how the results will be organized and used.
- Monitoring and maintenance: the process is repeated periodically or through automated systems. It is monitored that the data in the system has the appropriate classification.
Best Practices for Data Classification
While data classification forms the basis of work to ensure that sensitive data is used appropriately, most organizations fail to set the right goals and approaches. This causes applications to become overly complex and fail to produce practical results. You can follow the steps below for effective data classification:
Complete the risk assessment of sensitive data.
Make sure you clearly understand your organization’s regulatory and contractual privacy requirements. Define your data classification goals with an interview-based approach involving key stakeholders, including compliance, legal, and business unit leaders.
Develop a formal classification policy.
Granular classification schemes tend to confuse. Three to four classification categories would be reasonable. Reinforce employee roles and responsibilities. Policies and procedures should be well defined, appropriate to the sensitivity of particular data types, and easily interpreted by employees.
Each category should specify the sorts of data it contains and data handling requirements, and associated dangers. It may be helpful to subcategorize the most sensitive type to indicate regulations or different access control models that may be required.
End-users should categorize all freshly created and recently accessed data from that day forward before turning their focus to old data after your policies are established and disseminated.
Classify data types.
Some difficulties may arise in determining what kind of sensitive data is available in your organization. Data classification is an effort that needs to be organized around business processes and directed by process owners. It would help if you considered each of your business processes. Monitoring the data flow gives an idea of what data should be protected and how. Consider the following questions when classifying data:
- What kind of information on your customers and partners does your company collect?
- What kind of information do you gather about them?
- What kind of confidential information are you generating?
- What kind of transaction data do you have to deal with?
- Of all the data collected and created, what is confidential?
Discover where your data is.
Once you have established the data types in your organization, it is essential to identify all the places where data is stored electronically. Identifying the flow of data into and out of the organization is a crucial consideration.
Data discovery tools can help inventory unstructured data and help you understand exactly where your company’s data is stored, regardless of format or location.
Data discovery tools also provide information about users who process data, helping to address the difficulties of identifying data subjects. You can include sensitive data or keywords such as credit card numbers or certain types or formats of data in your data discovery efforts.
Identify and classify data.
Once you know where your data is stored, you can only identify and classify it to ensure it is adequately protected. Learning about the potential costs associated with creating a dataset will allow you to set your expectations for the cost of maintaining it and what level of classification to set.
Commercial data classification tools will enable data classification processes by identifying appropriate classifications and then adding the classification tag to the item’s metadata or applying it as a watermark. Effective classification systems are user-oriented and offer system-recommended and automated features.
Enable data controls.
Take security measures for critical solutions and define policy-based controls for each data classification tag to ensure that appropriate solutions are implemented. High-risk data requires more protection, while low-risk data requires less protection.
Once you know where the data lives and the organizational value of the data, you can implement appropriate security controls based on the risks involved, you can then use classification metadata security controls in data loss prevention (DLP) tools, encryption, and other security technologies.
Monitor and maintain data.
Be prepared to monitor, maintain and make necessary updates to your organization’s data classification system. Classification policies should be dynamic. You should establish a review and update process that includes encouraging users to adopt and ensuring that your approach continues to meet the changing needs of the business.
Classifying all data is an expensive and cumbersome activity that few firms can handle. A good retention policy can help reduce datasets and streamline your efforts. Start by selecting specific data types to categorize according to your privacy needs and add more security for increasingly confidential data.
Data classification; It helps your organization ensure that its data is effectively protected, stored, and managed, right up to the destruction of information. Putting data classification at the center of your data protection strategy allows you to reduce risks for sensitive data, improve decision making, and increase the effectiveness of DLP, encryption, and other security controls.
Creating a simple classification scheme, comprehensively evaluating and locating data, and implementing the right solutions provide a simplified, streamlined system to ensure your organization’s sensitive data is used appropriately and mitigate threats to your business processes.
