What Is Data Classification? Definition and Uses

Capturing and classifying data moved from physical filling cabinets to computer hard drives - and eventually to enterprise cloud storage solutions. The digital revolution saw a sudden influx of data generation where organizations had to manage, sort, and secure large amounts of data in a fraction of the time - this is where the idea of modern data classification stems from. Today’s technology allows us to store massive amounts of data while also categorizing, analyzing, and drawing valuable insights from that data. Manual data classification became impractical with the surge of computing capabilities and algorithm-based accuracy.

As technology advances further, organizations generate even more data. This pushes the need for better and more efficient data classification software and tools for all types of raw, unstructured forms of data. In this glossary article, we look at the meaning of data classification, how it works, and the types of data classification available. We also look at data classification examples and categories.

What Is Data Classification?

Data classification can be described as the process of sorting data according to a set of standards and policies. Data classification is based on importance, sensitivity, and risk levels. Generally, data classification makes it easier for organizations to store, access, and retrieve data in a safe, fast, and practical way. Data classification can also be considered part of an organization's overarching enterprise data management system. Effective data classification tools ensure that companies stay compliant with regulatory standards and provide insightful analysis of datasets.

Sorting data can also help to streamline processes for companies that have to maintain large amounts of data daily. This improves visibility across the organization and enhances data control and access. The criteria for different types of data classification depend on importance, sensitivity, and other factors. Now that we have a better idea of what data classification is, we can explore the different types of data classifications available.

Data Classification Methods

The type of data classification used will depend on multiple factors such as the size of your business, the expertise of users, and the amount of data that is considered sensitive. Generally, it can be grouped into three different types:

• Content-Based Data Classification: Files in datasets can often seem regular yet hold critical information that should be restricted. Using content-based data classification, files are scanned, inspected, and interpreted to find any sensitive information inside. This helps to classify if the data found is confidential or can be accessed by the public.
• Context-Based Data Classification: This approach will look at the metadata of files rather than the direct contents to figure out if the data inside is sensitive. Context-based data classification is based on file locations, user information, or file formats. Typically, this approach works better for a well-trained user pool.
• User-Based Data Classification: In this type of data classification, the onus falls on users to sift through file contents and categorize data as needed. Naturally, this means that the user needs to be highly trained and capable of classifying data. This form of classification relies on the user’s knowledge and discretion.

Now that we can identify the different types of data classification, we can see how they compare on a hierarchy of different levels as well.

Levels of Data Classification

While data can be sorted according to the different types and sources, there are also different levels that data classification is based on. These levels are often used to determine the risk presented for each type of data. Typically, data levels are divided into three groups:

• Low-Risk Data Classification: This usually includes data that is publicly accessible and easy to recover. The risk of this data being lost forever, leaked, or stolen is very low and the data itself is often not vital to the operation of the company.
• Moderate-Risk Data Classification: Moderate-risk data often includes data that is not publicly available or critical to operations but is proprietary and used internally. While the data itself is meant for internal use and access, its release to the public would not be a major threat.
• High-Risk Data Classification: Unlike the previous categories, high-risk data includes anything that could potentially harm the organization, its clients, or security policies if released publicly. This form of data is made up of confidential records, irreplaceable datasets, trade secrets, or authentication data. Sensitive or private data falls under high-risk data as they would also be the first to be targeted by threat actors.

The different levels of data classification will determine how data is stored, transferred, and handled. Once you can visualize this hierarchy of data categories, you can begin to understand how the classification of data works.

How Does Data Classification Work

To carry out effective data classification, companies need to determine the types of data and their risk levels using the aforementioned categories. Different sectors will use different strategies to sort data accordingly. This is all dependent on how organizations gather data, the type of data generated, and the people who have access to that data. This is all achieved through a specific data classification process that we can now delve further into.

Data Classification Process

The data classification process begins from the moment data is created and lasts throughout its lifecycle. Data Lifecycle Management (DLM) is used to determine the properties of every piece of data – including access controls, usage, regulations, and more. Data classification is part of the overall data lifecycle and follows a general process:

1. Collection of Data: Naturally, the first step is collecting raw data – often from physical data stores or a cloud data warehouse - according to the set standards required. This phase will also often overlap with the data aggregation phase of a typical data lifecycle management framework.
2. Defining Classification Levels: To properly classify data for your organization, you need to set up the categories specific to the data you’ve collected. For example, a medical organization might have patient records, financial records, and regulatory data that require high-risk-level classification. Ownership of data is often established in this phase as well to ascertain accessibility controls and security protocols.
3. Categorizing Data: This step involves finding patterns, criteria, and context to classify datasets. Data is assessed and categorized using interviews, documentation reviews, automated scanning tools, and classification workflows. The structure created from this process will ensure that data is sorted accurately. Access controls are also decided within this phase.
4. Applying Security Controls and Monitoring: After data assets are classified, security controls and monitoring protocols need to be put in place according to the different risk levels for each dataset. This ensures that data is protected.
5. Reviewing, Updating, and Training: Data classification is a process that needs to be constantly updated and evolved. As new data regulations and standards come into play, companies need to adjust classifications to ensure better safety. Consistent reviews will also indicate weaker spots and places where improvements can be made. Training and awareness campaigns will also enhance the expertise of the workforce.

Data classification processes can be quite simple once an organization determines the type of data classification policy needed. A set data classification standard provides a universal system to secure and categorize data as required.

Types of Data Classification

When classifying data, it’s important to recognize that data can be different in many ways. The different classification categories ensure that organizations choose the right data to secure in the right way. Generally, data classification types include four main categories:

Public Data Classification

This is low-risk data that is available in the public domain. All data in this category can be freely redistributed, shared, and used without any legal limitations. This data is freely available and can be accessed by any member of the public. This includes:

• Marketing data such as flyers, advertisements, etc.
• A company’s name, executive information, and social media profiles
• Job postings
• Addresses, phone numbers, and email addresses
• Press releases

Internal Data Classification

Unlike public data, internal data usually contains proprietary information for the employees of a company. While this data cannot cause the business harm by being publicly released, it is only meant to be seen by the internal workforce. This includes:

• Email, text, or telephone correspondence.
• Business processes
• Internal memos
• Business plans

Confidential Data Classification

This form of data is often collected by medical or financial institutions. Confidential data is – as its name suggests – confidential. These files contain specific private information of people and need to be regulated by laws like HIPAA and the PCI DSS. This data can also be classified as Personally Identifiable Information – or PII. PII data classification will sort this higher-risk data into more secure folders that follow strict regulations and protocols to be properly handled, secured, and stored. This information can also be government-protected and have national security implications when handled incorrectly. This includes:

• Social Security numbers
• Health Records
• Contact details
• Cardholder information
• Addresses
• Banking details

Restricted Data Classification

Going a level higher in terms of security, restricted data refers to any files or information that are illegal to access and could result in hefty fines or jail time. This includes:

• Proprietary information
• Research or data protected by government or federal regulations

These types of will provide a generalized framework for classification.

Examples of Data Classification

Data Classification Standards - ISO 27001

The ISO 27001 data classification standard can be defined as an information security management system created by the International Organization for Standardization (ISO). This provides a data classification framework for organizations looking to sort sensitive data. Under the ISO 27001 standard, data includes intellectual property, customer data, financial records, employee records, personal information, and any other type of confidential data. This system also ensures that companies of any size, in any sector, can choose the right security protocols to protect data while adhering to all local and internal regulations. The existence of a globally renowned standard in itself can attest to the importance of data classification in general, however, we can still touch on why the practice is crucial for any organization.

Data Classification Compliance Framework

Several types of regulatory data standards exist to ensure the proper usage of data. These frameworks and legal guidelines ensure that sensitive data is not being used without consent or in a way that would harm the user. These are some of the main data classification policies and standards that are used to ensure data integrity:

SOC 2 The SOC 2 Trust Services Criteria is a set framework of auditing standards and guidelines set by AICPA to evaluate the controls of an organization undergoing an audit. They are used to assess an organization’s information security practices against industry best practices and regulatory requirements. The top five requirements include security, availability, confidentiality, processing integrity, and privacy. The standard ensures that all service organizations maintain proper and objective confidentiality.

HIPAA The Health Insurance Portability and Accountability Act of 1996 - or HIPAA - is a federal law that requires the creation of national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge. The HIPAA Privacy Rule restricts the use and disclosure of Protected Health Information (PHI) – forcing organizations to regulate all the data classified accordingly.

PCI DSS

According to the PCI Requirement 9.6.1, media that contains sensitive data needs to be classified accordingly to prevent it from being stolen or copied. The regulation cites the classification of data as the means of securing sensitive data.

GDPR

The General Data Protection Regulation (GDPR) works to protect personal data from being classified in a completely objective manner. This means that certain data – such as race, ethnic origin, political opinions, biometric data, and health data – is considered ‘special’ and needs to be stored more securely. Companies need to classify all data that can be covered by GDPR properly to avoid legal ramifications.

Understanding the regulatory frameworks and policies in place to protect data will help to ensure that your company stays compliant and on the right side of the law. It’s important to note that data regulations may differ according to different locations and different types of data – making a certain data classification policy applicable in certain regions.

Importance of Data Classification

Data classification is a vital element of any organization’s data management process. Here are some of the main reasons why the classification of data is important:

• It ensures that companies have better visibility of data assets and can classify data according to their importance and risk level. This allows high-risk data to be adequately protected.
• Classification of data helps to process the large amount of raw, unstructured data sitting in an enterprise data lake as efficiently as possible.
• Classifying data allows companies to also glean insightful information and patterns that can be used to make informed decisions.
• It streamlines processes by making data easier to find through grouping like datasets.
• Organizations can use it to ensure compliance with all data privacy and regulatory standards.
• It forms part of an effective cybersecurity solution and framework by ensuring the highest level of protection for the most important data.
• A company’s cybersecurity posture is also enhanced by data classification by automatically notifying security protocols if sensitive data is being accessed.
• It also ensures data integrity and transparency.
• Semi-structured data found in an enterprise data warehouse can also be effectively classified and stored.
• It also helps to identify duplicate copies and avoid unnecessary space being used.

As mentioned before, data classification is often used to avoid breaching standards and policies in place to protect sensitive information. Organizations must be prepared for the worst by keeping data secure and well-organized. Data classification is an efficient and effective way of categorizing information according to risk, importance, and usage. Companies need to invest in the right cybersecurity and infrastructure to maintain their data properly. Learn more about Sangfor’s Enterprise Data Storage Solution here. Sangfor offers superior cloud computing and cyber security solutions that will keep your organization streamlined, secure, and ahead. Contact Sangfor Technologies today for more information.

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