What Is Data Classification?

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. 

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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?

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.

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.

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.

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

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

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

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).