Data is considered to be the lifeblood of businesses, but not all data is the same, and thus shouldn’t be treated in a similar way. Data security is not only crucial, but quite valuable as well, and it requires several layers of protection in order to prevent data breaches and leaks.
One way to do so is data classification, which is the core objective of several compliance standards and requirements.
Data classification is also important for companies to ensure that their critical and valuable data is protected from several risks and compliance issues. This article covers:
- What is Data Classification?
- What is the Purpose of Data Classification?
- The Four Levels of Data Classification
- Are the Levels of Data Classification Still Relevant?
- Different Types of Data Classification
This is part of our extensive data classification guide.
What is Data Classification?
Data classification is considered to be a focal point for compliance requirements and standards, and it involves the identification, categorization, and maintenance of data protection, while also reducing legal risk and implementing security controls. In turn, this helps organizations in effectively allocating resources.
Data classification hinges on the fact that you should know the data your organization collects, processes, and uses for its operations, as well as the level of security that needs to be applied to each type of data. Therefore, you classify each type of data in order to achieve compliance and prevent cyberattacks.
What is the Purpose of Data Classification?
Data classification is integral not just for organizations to meet compliance requirements, but also to implement stronger security measures in order to protect companies from any cyberattacks and threats. It also helps businesses perform a risk assessment for their operations. Once you understand how your organization stores and processes data, you would be able to implement data security controls that can eliminate any risks.
When risk assessment is being conducted within the organization, it is crucial to find out about sensitive data in order to detect any threats or loopholes that might trigger a data breach. It can actually be cost-effective for companies, since they can allocate data security resources in a better manner. Moreover, it would help them comply with data privacy standards and also contain any hacks or data breaches that might take place within an organization.
The Four Levels of Data Classification
There are various levels of data classification in an organization. Generally, government agencies have more classification levels, namely top secret, secret, confidential, sensitive, and unclassified. However, these don’t apply to other organizations, which is why they usually employ the following four classification levels.
The first data classification level is known as public, and it involves public data that can be openly used and shared on the company website, as well as with the general public. Public information can be used without any additional controls and security protocols, and it can be discussed openly as well.
For instance, it could include a datasheet about the company’s products and services or other promotional content.
Another type of data classification is called internal information, and it is implemented across the organization. Although this information is not sensitive, it should not be shared externally.
An example of this is the employee handbook and company memos which, even if disclosed to the general public, won’t cause the company any harm.
As the name suggests, confidential information has stricter access control and is limited to a particular team only. Therefore, it is much more sensitive and is limited for use within the business.
Examples of confidential information include pricing policies, employee reviews, vendor contracts, and other sensitive data. If this type of information is disclosed or leaked, it can have a negative impact on the business or the brand.
Last but not least, restricted data is a notch higher than confidential information, and its access is much more restricted as well. Basically, it is limited to a need-to-know basis, and is protected through a Non-Disclosure Agreement (NDA), to minimize legal risk and ensure compliance.
Examples of restricted information include trade secrets, potentially identifiable information, credit card information, financial data, and even health information. If this type of information is revealed, it can cause massive legal and financial damage to the organization.
Are the Levels of Data Classification Still Relevant?
Data classification levels are critical in order for organizations to maintain the confidentiality, privacy, and integrity of the data that is key to their operations. It also helps them mitigate the risk of sensitive information being compromised.
If the data classification levels aren’t maintained and enforced within an organization, it can lead to catastrophic results. Therefore, it is still important for companies to categorize their data according to the different classification levels, to maintain compliance and minimize risks that lead to security issues and data breaches. So, they are certainly relevant.
However, in many organizations, data is classified without the use of data classification levels. As long as that does not conflict with compliance requirements, and the results are clear data access policies to sensitive data, that is perfectly ok.
Different Types of Data Classification
Data classification is usually conducted according to each business’ requirements, but there are a few common types of data classification, which are as follows:
- Data-Based Classification – This type of classification is used to describe the nature of the data, i.e. an email address, phone number, or credit card number.
- Context-Based Classification – This type of classification involves a description of the business content of the data, and it generally involves more sensitive data, such as the company’s revenue or earnings data.
- Source-Based Classification – This type of classification provides a description of the source of the data. This can include data collected from customers through several sources, i.e. a webinar, contact form, etc.
There are several other types of data classification that are relative to individual businesses and their requirements. Plus, various compliance standards and regulations require companies to classify their data efficiently, although the requirements might vary from standard to standard.
By following the data classification methods and sticking to the levels, companies would be able to ensure better compliance and reporting to the local and global regulations, and it would also help them manage data access and authorization in a better way as well.
This concludes our guide on the data classification levels, and whether they are still relevant or not. If you are looking to establish a data classification policy, you can start by conducting a data risk assessment and follow it up with a data inventory, which can help you in setting up stricter data security controls.
Data Classification with Satori
Satori provides a different approach to data classification. With Satori, data is continuously discovered and classified, instead of performing ad-hoc scans.