What is Data Classification? A Definition & Overview

Defining Data Classification

Imagine going to a library where none of the books are organized—not by the Dewey Decimal System and not by genre. It would be complicated and inefficient for anyone to find what they are looking for. The same idea pertains to data, which is why data classification is a process that all companies should consider practicing. 

Data classification is the process of categorizing data into relevant subgroups so that it is easier to find, retrieve, and use. The data classification process involves marking or tagging data with a classification label such as Confidential or Public and simultaneously cleaning your company’s storage of stale and duplicate data that has been hidden and unkempt. 

Why is Data Classification Necessary?

One of the primary reasons to conduct ongoing data classification is to support data security requirements and prevent security incidents. However, and more importantly, classification acts as a visual cue for your employees and users to better understand the level of safety and alertness required when handling a given document. Classification gives your business insight into the data it is creating, the amount and type of data it is collecting, and the level of sensitivity it has. 

Classifying data also helps businesses improve their posture with ever-changing data regulations. A few prominent compliance laws are the GDPR, PCI DSS, and HIPAA.

Meeting business objectives and enhancing operational efficiency is another reason for your organization to begin the classification process and keep up with it automatically and regularly. Knowing where millions of files are and what purpose they serve allows your company to analyze data and see trends, which enhances decision-making and streamlines productivity. Maintaining data awareness and organization early on can also reduce maintenance and storage costs.

Types of Data Classification

There are three main types of data classification, according to industry standards.

1. Content-based classification

This approach, which probes and interprets data using deep inspection for sensitive, personal and confidential information which then determines the appropriate classification label to be applied.. 

2. Context-based classification

This approach examines files based on metadata rather than their content such as: 

• The location of where data was created or modified
• The creator of the data 
• The application of the data. For example, financial or healthcare software.

3. User-based classification

Synonymous with manual human-generated classification where a person decides how to classify the data. User-based classification is heavily reliant on personal discretion and the employee’s knowledge of data.

Data Classification Labels

Generally, the more classification labels you implement, the more detailed you can categorize your data. However, more labels also lead to more complexity which ultimately makes it harder for users to follow.

General best-practice recommends no more than 3 or 4 classification labels and the following is the most commonly used:

Public data – This category of data is freely accessible to the public including all company employees. It can be freely used, reused, and redistributed without repercussions. An example might be marketing brochures, press releases or a public company’s stock report.

Internal-only data – This category of data is limited strictly to internal personnel or employees who are granted access. This might include internal-only emails and correspondence, recordings or other communications, business plans, org charts, internal staff contact list etc. 

Confidential data – Access to confidential data requires special access privileges that must be strictly controlled. Types of confidential data can include personal and sensitive data of customers and employees, M&A documents, privileged information you exchange with your clients under NDA and more. Usually, confidential data is protected by data privacy and security regulation laws like HIPAA, GDPR, CPRA and the PCI DSS.

Restricted data – Restricted data includes data that, if compromised or accessed without authorization, could lead to criminal charges and massive legal fines or cause irreparable damage to the company. Examples of restricted data might include proprietary information or research and data protected by state and federal regulations.

What is the Data Classification Process?

When done manually, data classification can be a tedious and complex process. Manual classification processes are vulnerable to human subjectivity compared to trained algorithms that a classification tool would rely on. However, humans should still be part of the process. While automation does streamline the overall process, you will still need processes and procedures in place that outline the roles and responsibilities of employees in your organization in regard to data classification.

Below are five steps to take for data classification:

1. Define the objectives. What compliance requirements apply to your organization?
2. Categorize types of data. Identify what kind of data your organization collects and define classification levels.
3. Create workflows based on your data discovery tool. Identify the process to scan and discover new data.
4. Define outcomes and usage of classified data. Identify how to organize collected data and how to use it to make business decisions.
5. Monitor & maintain. Continue classifying and discovering new data to ensure sensitive data is being protected and your organization remains in compliance.

Classify Data with Ground Labs

In order to properly classify data, you will need a data discovery tool. Not only will it help you have a complete understanding of where all your data resides and what category it belongs to, but it will assist your company in ensuring compliance with data protection laws. Our solutions, like Enterprise Recon and Card Recon, help businesses discover over 300 types of data across a variety of surfaces, such as desktops, email, and cloud, among other environments. These tools also help to remediate data compliance issues and keep your business functioning more efficiently. 

If you are ready to take control of your data and streamline your classification process with tools that also support compliance initiatives, contact us today.