The amount of data in companies is growing exponentially today. The challenge for security teams is to properly protect this data from potential cyber attacks within the available time, budget and human resources.
The best way to cope with this task is to properly prioritize the data. Data classification plays a crucial role here, as this technology helps companies effectively enforce their risk management, compliance and data security requirements.
Data classification basics
Data classification is broadly defined as the process of organizing data into relevant categories so that it can be used and protected more efficiently. In the classification process, data is tagged to make it easier to find and understand. It also eliminates multiple duplicates of data, which can reduce storage and backup costs while speeding up the search process.
The ability to classify data has improved significantly over time. Today the technology is used for a variety of purposes, often to aid data security initiatives. However, data can be classified for a variety of reasons, such as to make it easier to access, to comply with legal requirements, or to meet various other business goals. In some cases, data classification is a legal requirement because data must be searchable and retrievable within certain time frames. For the purposes of data security, data classification is a useful method to enable appropriate security measures based on the nature of the data being accessed, transmitted, or copied.
Data classification against the background of the GDPR
With the entry into force of the General Data Protection Regulation (GDPR), data classification is more mandatory than ever for companies that store, transfer or process data from EU citizens. For these companies, it is critical to classify data so that everything that falls under the GDPR is easily identifiable and appropriate security precautions can be taken.
In addition, the GDPR requires increased protection for certain categories of personal data. For example, the regulation expressly prohibits the processing of data relating to ethnic origin, political opinions, and religious or philosophical beliefs. Classifying such data accordingly can significantly reduce the risk of compliance issues.
Types of data classification
Data classification often involves a variety of tags and labels that define the type of data, its confidentiality, and its integrity. Availability can also be taken into account in data classification processes. The level of sensitivity of data is often classified based on different levels of importance or confidentiality, which then correlate with the security measures used to protect each classification level.
There are three main types of data classification that are considered the industry standard:
- Context-based classification examines and interprets files based on their context as it searches for sensitive information
- Content-based classification regards application, location, or creator, among other variables, as indirect indicators of sensitive information
- The user-based classification is based on a manual selection of each document by the end user. It relies on the knowledge and discretion of the user to create, edit, review or share them to mark sensitive documents.
An example of data classification
For example, an organization can classify data as restricted, private, or public. In this case, public data is the least sensitive data with the lowest security requirements, while restricted data has the highest security classification. This type of data classification is often the starting point for many companies, followed by additional identification and labeling procedures that label data based on its relevance to the company, its quality, and other classifications.
The data classification process: aided by automation
Data classification can be a complex process. However, automated systems can help streamline the process. However, an organization must establish the categories and criteria used to classify data, understand and define its goals, outline the roles and responsibilities of employees in maintaining proper data classification protocols, and implement security standards that are consistent with the data categories and tags. When done correctly, this process provides an operational framework for employees and third parties involved in storing, transferring, or querying data.
Steps for effective data classification
1. Data discovery
A detailed look at the location of the current data and any regulations that apply to the business is the best place to start for effective data classification.
In order to identify all sensitive data that needs to be classified and protected, the company must first know what data is being searched for - such as personal data, credit card information or intellectual property. Managers should focus on the places where this data is likely to be found, from endpoints and servers to databases on premises and the cloud. The data discovery is not a one-off event, but a continuous process, whereby data at rest, in transmission and in use throughout the company should be taken into account.
2. Creation of a guideline for data classification
Compliance with data protection principles in an organization is almost impossible without a corresponding policy. Creating a policy should therefore be your top priority.
Companies should clearly communicate internally how the classification can help increase sales, reduce costs and reduce risk. It is important to ensure that users know the guidelines and understand why the program is being introduced. An effective policy balances the confidentiality and privacy of employees and users, and the integrity and availability of the data to be protected.
3. Prioritize and organize data
After companies have created a policy and have an overview of their current data, they are classified based on their sensitivity and the necessary protective measures. Many people in charge get bogged down in data classification projects due to overly complex classification schemes. Typically, adding more sets adds complexity, but not quality. Companies should therefore start with three categories to make it much easier to get started.
Many of today's data classification tools are automated and classification can be done based on context (e.g. file type) and content (e.g. fingerprint). This option can be expensive and require a great deal of fine-tuning, but once it runs it is extremely fast and the classification can be repeated any number of times.
It is also possible to choose the classification of a file manually. This approach relies on a data professional to lead the classification process and can be time consuming. However, in organizations where the classification process is complicated and subjective, a manual approach may be preferred.
Some companies also choose to outsource the classification process to a service provider. While this is typically not the most efficient or cost-effective option, it can provide a one-time classification of data to get a snapshot of where the company is currently in terms of compliance and risk.
Use and protect data treasures sensibly
The classification of data not only makes it easier to find it. It is a necessary measure so that modern companies can use their increasing amounts of data sensibly and protect them from potential security risks. Once implemented, the data classification provides an organized framework that facilitates data protection measures and effectively supports compliance with the security guidelines by employees.
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