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What Is Data Classification?

Data Classification Definition

Data classification eases the processes involved in finding and retrieving data, securing data, optimizing data-based processes, and maintaining compliance. Data classification is based on the organization of data according to specific categories so that users and applications can make more efficient use of it. 

1. Identify Sensitive Files

Files can be categorized based on whether they contain sensitive information like identity details, usernames, passwords, or proprietary secrets the organization does not want hackers to get their hands on.

2. Secure Critical Data

When critical data is classified appropriately, specific protections can be applied to encrypt or otherwise shield it from potential thieves or saboteurs.

3. Track Regulated Data

The movement of regular data through different areas of the network and past the workstations of different individuals often needs to be tracked for an organization to remain compliant with government regulations.

4. Optimize Search Capabilities

Classifying data containing certain keywords and phrases can make a website easier to search by users. You can also reorganize the site and choose to feature pages with successful keywords with the help of classification.

5. Identify Duplicate or Stale Data

Duplicate data can clutter your storage, wasting valuable—and sometimes expensive—space, making search results confusing. Classifying data can make duplicates easier to identify.

Purpose of data classification

What Are the Data Sensitivity Levels?

1. High—Social Security Numbers and Legal Documentation

This kind of data can be used by cyber criminals or others to directly extort individuals, steal their identities, or otherwise defraud them. Therefore, it is the most sensitive data.

2. Medium—Non-Identifiable Personal Data

Non-identifiable personal data needs at least one or more other data points to be used against someone to steal their identity. However, it can still be used as a building block for fraudulent activity.

3. Low—Public Web Pages and Blogs

This data is open to all and, therefore, typically does not need protection. However, an organization can mistakenly include medium-sensitivity data on a website or blog if they are not careful.

Data Classification Process

1. Define the Objectives

The first step in classifying data is to set forth why you are categorizing your data and connect this to a related revenue or security-related payoff.

2. Categorize the Types of Data

While the execution of this step will vary from one process to the next, the objectives typically define the categories. For example, with classifying customer information, if the objective is to protect social security numbers from hackers, the categories may be different than if the objective is to organize customers according to geographic location.

3. Define Outcomes and Usage of Classified Data

The action steps that follow the classification are critical to making sure the implementation of the process is successful. They may be used to enhance data security, streamline a process, or better inform decision-makers.

4. Monitor and Maintain the Classification

Once the classification has been made, other data that falls in the same categories needs to be appropriately classified. Also, if the classification is not producing the results desired, the strategy may need to be adjusted.

Data Classification Types

You can perform data classification based on context, content, or user-defined parameters.

  1. Context-based categorization involves categorizing files according to metadata, such as the program used to create the file, the individual who created the document, or the place where the file was created or edited.
  2. Content-based classification includes classifying documents and files after reviewing their content.
  3. User-based classification entails categorizing files based on the judgment of an experienced user. After a significant modification or review of a document, or just before it is made public, users can designate its classification.

Data Classification Policy

A data classification policy establishes who is in charge of classifying data. Program Area Designees (PAD) are responsible for data classification for various programs or organizational divisions.

To establish your data classification policy, take the following into consideration:

  1. Are there any rules or compliance requirements that apply to the information, and what are the consequences of noncompliance?
  2. Who is the creator or owner of the program, organization, or data?
  3. Who is in charge of data integrity and accuracy?
  4. Where is the data stored?
  5. Which organizational division has the most knowledge about the context and content of the information?

Data Classification vs. Data Governance vs. Data Fabric

A solid data governance approach must start with data classification. This is because knowing what you have and how to use it will help you make the best use of your data. Data classification is the framework that outlines what data must be included and how, while data governance is the plan for using the data.

To address queries regarding where the user requesting access is located or how the data is classified, data fabric comes into play. Data fabric is a solution that integrates data pipelines for a more holistic, data-driven approach to decision-making.

Best Practices of Data Classification

1. Identify Which Compliance Regulations or Privacy Laws Apply to Your Organization

It is important to pinpoint the laws, such as the Payment Card Industry Data Security Standard (PCI DSS), that apply to your organization to remain in legal compliance and prevent fines and lawsuits. Otherwise, you may expose your company to potentially crippling litigation, not to mention the harm it could cause customers.

2. Start With a Realistic Scope

It is virtually impossible to classify all the data in your organization. Starting small and then learning from how that process went can make future efforts more successful.

3. Validate Your Classification Results

Even though a successful data classification policy necessitates data classification levels, data management, and data tagging, validating the results is just as important. In this way, you ensure the process is successful. You can also identify weaknesses and improve them in the future.

4. Figure Out How to Best Use Your Results

Even though a successful data classification policy necessitates data classification levels, data management, and data tagging, validating the results is just as important. In this way, you ensure the process is successful. You can also identify weaknesses and improve them in the future.

How Fortinet Can Help

With the FortiGuard Database Security Service, your organization gets a centrally managed database protection structure fit for enterprise-level companies. Content can be automatically updated according to preconfigured policies. These policies can be designed to reveal and address weaknesses in your information security strategy, operational risks, access privileges that need to be reconsidered, known exploits, and best practices that pertain to your regulatory requirements.

FortiGuard shields your organization from threats trying to enter via email, while actively monitoring the system, producing alerts that can help you address a potential problem before it becomes costly. FortiGuard also maintains a collection of predefined policies, generating reports using policy information gathered during the initial scan. In this way, violations of policies can be easily detected, and data gained from subsequent scans can be used to adjust the system.


What is data classification?

Data classification refers to the way data is organized according to specific categories in order for users and applications to make more efficient use of it.

What are the purposes of data classification?

Data classification eases the processes involved in finding and retrieving data, managing security risks, optimizing data-based processes, and maintaining compliance.

What are the data sensitivity levels?

The data sensitivity levels are as follows:

  1. High: social security numbers and legal documentation
  2. Medium: non-identifiable personal data
  3. Low: public web pages and blogs

What are the best practices of data classification?

The best practices of data classification include:

  1. Identify which compliance regulations or privacy laws apply to your organization
  2. Start with a realistic scope
  3. Validate your classification results
  4. Figure out how to best use your results

What are the examples of data classification?

Some of the most common examples of the need to classify data are when organizations have to meet the guidelines of the General Data Protection Regulation (GDPR).