Data Classification: Overview, Types, and Examples

What Is Data Classification?

The process of analyzing unstructured or structured data and categorizing it based on contents, file type, and other metadata is referred to as data classification.

Organizations can use data classification to answer essential questions about their data, which helps mitigate risk and manage data governance policies. It can tell you where your most important data is stored and what types of sensitive information your users are most likely to create. To comply with current data privacy regulations, comprehensive data classification is required (but not sufficient). Organizations can use data classification software to identify relevant information to their goals. 

To comply with data privacy regulations, businesses typically launch classification projects to find any personally identifiable information (PII) on their data stores, allowing them to demonstrate to auditors that it is appropriately managed.

Although there are some similarities, data classification is not the same as data indexing. While both involve examining content to determine whether it is relevant to a keyword or concept, the classification does not always result in a searchable index. Without storing an index of the object's content, classification results will often list the object name and the policy or pattern that was matched:

• Object: Customers.xls
• Patterns Matched: American Express (PCI-DSS) California Motorist's License (CCPA) 

Some data classification solutions create an index to aid in the fulfilment of data subject access requests (DSAR) and right-to-be-forgotten requests by allowing for quick and efficient searches.

Purpose of Data Classification

Risk Mitigation

1. Access to personally identifiable information is limited (PII)
2. Control the location of intellectual property and its access (IP)
3. Reduce the attack surface area on data that is sensitive.
4. The classification should be integrated into DLP and other policy-enforcing applications.

Governance/Compliance

1. Determine which data is governed by GDPR, HIPAA, CCPA, PCI, SOX, and other regulations.
2. To enable additional tracking and controls, apply metadata tags to protected data.
3. Legal holds, quarantining, archiving, and other needed actions can all be enabled.
4. Facilitate Data Subject Access Requests and the "Right to be Forgotten" (DSARs)

Efficiency and Optimization

1. Allow efficient access to content based on type, usage, and other factors.
2. Finds and removes stale or redundant data.
3. Move data that is frequently accessed to faster devices or cloud-based infrastructure.

Analytics

1. To improve business operations, enable metadata tagging.
2. Inform the organization about where the data is stored and used.

It is worth noting that, while classifying data is an essential first step, it's rarely enough to take action in many of the use cases listed above. Adding more metadata streams, such as permissions and data usage activity, can significantly improve your ability to use classification results to achieve critical goals.

Data Sensitivity Levels

The data sensitivity classification levels are high, medium, or low.

High Sensitivity Data

If compromised or destroyed in an unauthorized transaction, the organization or individuals would suffer catastrophic consequences. Financial records, intellectual property, and authentication data are just a few data classification examples.

Medium Sensitivity Data

Intended for internal use only but would not have a catastrophic impact on the organization or individuals if compromised or destroyed. e.g., Documents and Emails with zero confidential information.

Low Sensitivity Data

They are intended to be used by the general public. E.g., content of a public website.

Types of Data Classification

Data bracket substantially entails multiple markers that define types of data and their integrity and confidentiality. In data classification processes, availability may also be taken into account. Data sensitivity is frequently classified based on various levels of importance or privacy, linked to the security measures implemented to defend each classification level. There are three types of data classification that are widely used in the industry:

• Content-based classification examines and interprets files in search of sensitive data.
• Context-based classification considers characteristics such as creator, application, and location as indirect markers.
• User-based: The classification of each document is based on a manual selection by the end-user. To sensitive flag documents, user-based classification depends on user knowledge and discretion during creation, edit, or review.

Depending on the firm's need and data type, content, context, and user-based approaches can be right and wrong.

Determining the Risk of Data

In addition to classification types, an organization should assess the risk associated with the different types of data, how it is handled, and where it is stored/sent (endpoints). Separating data and systems into three levels of risk is a common practice.

• Low threat: If data is accessible to the public and not easily lost (e.g., recovery is simpler), this data collection and the systems that compass it are likely to be less perilous than others.
• Moderate risk: The data is not available publically and is used internally by the company or its partners. It's also unlikely to be too critical to operations or sensitive to be considered "high risk." Moderate items include proprietary operating procedures, cost of goods, and some company documentation.
• High-risk tems include anything remotely sensitive or critical to operational security. Also, data that is extremely difficult to recover (if lost). All sharp and essential types of data are known as high risk.

The Application of a Data Classification Matrix

Some organizations may find it simple to create and label data. Determining the risk of data and systems is likely to be easier if there aren't many different data types or if your business has fewer transactions. However, many organizations dealing with large amounts of data or multiple types of data will require a comprehensive risk assessment. For this purpose, most people employ a data classification matrix.

Effective Data Classification Steps

• Understanding the Current Setup: Understanding the current setup, including the location of existing data and all applicable regulations, is perhaps the best place to start when it comes to effectively classifying data. Before you can organize data, you must first understand what you have.
• The Establishment of a Data Classification Policy: It is impossible to comply with data protection without sound and strong policy principles in place in an organization. Your priority should be to create a policy.
• Prioritize and Organize Data: Now you have a policy in place and a visual representation of your current data, it's time to classify it correctly. Based on the sensitivity and privacy of your data, choose the best way to tag it.

Data classification has more advantages than just making data easier to find. Modern businesses require data classification to make sense of large amounts of data available at any given time.

Data classification gives an organization a clear picture of all data under its control and an understanding of where the data is stored, how to access it quickly, and how to protect it from potential security threats. Data classification, once implemented, creates an organized framework that allows for more effective data protection measures and encourages employee adherence to security policies.

Data Classification Process

Data classification can be a time-consuming and challenging process. Automated systems can help speed up the process. However, an organization must first determine the categories and criteria to classify data, outline employee roles and responsibilities in maintaining proper data classification protocols, and establish security standards that correspond to data categories and tags. When done correctly, the process will provide an operational foundation for workers and third parties involved in data storage, transport, or retrieval. There are many video clips and webinars that can help you better understand the techniques for classifying sensitive data.

Policies and procedures should be well-defined. It should consider security requirements and data type confidentiality and be simple enough for employees who promote compliance to understand. Each category, for example, should include information about the types of data classified, security considerations such as rules for retrieving, transmitting, and storing data, and potential risks associated with a breach of security.

The data classification process varies slightly depending on the project's goals. Most data classification projects require automation to process the massive amounts of data that businesses generate every day. There are a few best practices that lead to successful data classification projects in general:

1. Define the Data Classification Process's Objectives

• What exactly are you looking for and why?
• What systems are included in the preliminary classification phase?
• What rules do you have to follow when it comes to compliance?
• Are there any other business goals you'd like to pursue? (for example, risk management, storage optimization, and analytics)

2. Classify Data Types

• Determine the types of data that the company generates (e.g., customer lists, financial records, source code, product plans.)
• Distinguish between private and public data.
• Are you looking for GDPR, CCPA, or other regulated information?

3. Determine the Levels of Classification

• How many classification levels are you going to require?
• Each level should be documented, and examples should be provided.
• Users should be taught how to classify data (if manual classification is planned)

4. Define the Process of Automated Classification

• Determine which data should be scanned first and how to prioritize it. Prioritize the active over the stale, and open over the protected.
• Determine how often you'll use automated data classification and how much time you'll devote to it.

5. Define the Categories and Criteria for Classification

• Define and provide examples for your high-level categories (e.g., PII, PHI)
• Define or enable classification patterns and labels that are appropriate.
• Create a procedure for reviewing and validating both user-defined and automated results.

6. Define Classified Data Outcomes and Use

• Steps for risk mitigation and automated processes should be defined; for example, if PHI is not utilized for 180 days, it can be moved or archived; and global access groups should be automatically removed from folders containing sensitive data.
• Define a method for using analytics to improve classification results.
• Determine what you want to happen as a result of the analytic analysis.

7. Observe and Maintain

• Create a routine for classifying new or updated data.
• Review and update the classification process as needed due to changes in the business or new regulations.

Examples of Data Classification

Data can be classified as Restricted, Private, or Public by an organization. In this case, public data is considered as the least sensitive data with the lowest safety requirements, whereas restricted data is the most sensitive data with the highest security classification. Many businesses begin with this type of data classification, followed by additional identification and tagging procedures that tag data based on its relevance to the business, quality, and other classifications. The most successful data classification processes use follow-up processes and frameworks to keep sensitive data where it belongs.

Example

RegEx is a string analysis system that defines specifics about search patterns. It is short for the regular expression. Particularly, if you wanted to find all VISA credit card numbers in your data, you could use the RegEx:

This sequence searches for a 16-character number that begins with a '4' and has four quartets separated by a '-.' A positive result is generated only when a string of characters matches the RegEx. A Luhn algorithm can be used to validate this result further.

In this case, a RegEx alone will not suffice. This RegEx finds valid email addresses, but it can't tell the difference between personal and business emails:

A more advanced data classification policy might use a RegEx pattern matcher and a dictionary lookup to narrow down the results using a library of personal email address services such as Gmail, Outlook, and others.

Many parsers will look at a file's metadata like the file extension and owner—to determine its classification in addition to regular expressions that look for patterns within text. Some scanning engines are capable of incorporating permissions and usage activity into the classification rule in addition to the file's contents.

Data classification at an advanced level employs machine learning to find data rather than depending solely on predefined rules or policies made up of dictionaries and RegExes. For example, a corpus of 1,000 legal documents could be fed to a machine-learning algorithm to teach what a typical legal document looks like. The engine can discover new legal documents on its model without relying on string matching.

Best Practices for Data Classification

These are some best practices to be kept in mind as you implement and scale a data classification policy:

• Determine which compliance or privacy laws apply to your company and create a classification plan based on that information.
• Begin with a limited scope (don't try to boil the ocean) and well-defined patterns (like PCI-DSS)
• To process large amounts of data quickly, use automated tools.
• When necessary, create custom classification rules, but don't reinvent the wheel.
• As needed, change the classification rules/levels.
• Check the accuracy of your classification results.
• Determine how to make the most of your findings and apply classification to various topics, including data security and business intelligence.

Data classification is an essential component of a comprehensive data security strategy. Once you've determined what data is sensitive, you'll need to determine who has access to it and what happens to it at all times. That way, you can protect your sensitive data while preventing your company from making the news.

Challenges of Data Classification

Almost every company holds sensitive information — often far more than they realize. However, it's unlikely that they know precisely where that data is stored and how it could be accessed or compromised throughout their infrastructure. Establishing effective data classification programs within organizations can lead to various challenges. 

Data Classification Can Be Time-Consuming and Costly

A few organizations only use traditional (manual) data classification methods. This poses several difficulties, including:

• Sensitive information can get lost in data silos, where it becomes unreachable and unprotected.
• Client embarrassment and revenue loss can result from improper handling of sensitive information.
• Mishandling regulated data can result in fines and penalties for businesses.
• Client data breaches can result in lawsuits, tarnish an organization's reputation, and reduce goodwill.

Data Classification Best Practices Are Not Well Understood

Poor data bracket prosecution can lead to a waterfall of data security and sequestration failures, posing the following challenges:

• Data and privacy concerns are pushed to the back burner favoring more pressing priorities such as sales, marketing, expansion, and product costs.
• Companies might have no idea where their data is or how to find it.
• Organizations are falling behind on constantly changing compliance regulations.
• Companies overcomplicate data classification, resulting in a lack of practical results.

Data Privacy Policies Are Not Being Enforced

Many organizations have theoretical rather than operational data classification policies. In other words, the corporate policy is either ignored or left to the discretion of business users and data owners.

The problem arises from failing to respond to critical questions such as:

• Are there any discussions about data privacy that are inappropriate at the highest levels of an organization?
• Who is responsible for data privacy in the end, and do they have the authority to implement and control solutions?
• Is sensitive and confidential data shared with other organizations?
• Is it possible that privacy and compliance policies are being disobeyed, either intentionally or inadvertently?

What Are the Functions of Data Classification in the Data Lifecycle?

The data lifecycle is an ideal structure for managing data flow across an organization. Every step of the way, businesses must account for data security, privacy, and compliance. Data classification is helpful because it can be applied at any data lifecycle stage, from creation to deletion. These are the six stages of the data lifecycle:

1. Creation - Emails, excel documents, word documents, google documents, social media, and websites generate sensitive data in various formats.
2. Usage in Role-based Security Controls - Role-based security controls are tagged with sensitive data based on internal security policies and compliance rules.
3. Storage - Data is stored with access controls and encryption after each use.
4. Sharing - Employees, customers, and partners constantly share data across various devices and platforms.
5. Archive - Most data is eventually archived in a company's storage systems.
6. Destroy indefinitely - Large amounts of data must be destroyed to reduce the storage burden and improve overall data security.

As soon as data is created, it should be classified. The classification of data should be evaluated and updated as it progresses through the data lifecycle stages.

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Conclusion

Data Classification is a fundamental core component of any security program. It is the guidelines for how IT security is weaved into information security and entrusts the protection of your firm’s most sensitive information.

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