Which one of the following is not a commonly-used business classification level?

Learn about the different types of classification and how to effectively classify your data in Data Protection 101, our series on the fundamentals of data security.

A Definition of Data Classification

Data classification is broadly defined as the process of organizing data by relevant categories so that it may be used and protected more efficiently. On a basic level, the classification process makes data easier to locate and retrieve. Data classification is of particular importance when it comes to risk management, compliance, and data security.

Data classification involves tagging data to make it easily searchable and trackable. It also eliminates multiple duplications of data, which can reduce storage and backup costs while speeding up the search process. Though the classification process may sound highly technical, it is a topic that should be understood by your organization’s leadership.

Reasons for Data Classification

Data classification has improved significantly over time. Today, the technology is used for a variety of purposes, often in support of data security initiatives. But data may be classified for a number of reasons, including ease of access, maintaining regulatory compliance, and to meet various other business or personal objectives. In some cases, data classification is a regulatory requirement, as data must be searchable and retrievable within specified timeframes. For the purposes of data security, data classification is a useful tactic that facilitates proper security responses based on the type of data being retrieved, transmitted, or copied.

Types of Data Classification

Data classification often involves a multitude of tags and labels that define the type of data, its confidentiality, and its integrity. Availability may also be taken into consideration in data classification processes. Data’s level of sensitivity is often classified based on varying levels of importance or confidentiality, which then correlates to the security measures put in place to protect each classification level.

There are three main types of data classification that are considered industry standards:

Content-based classification inspects and interprets files looking for sensitive information
Context-based classification looks at application, location, or creator among other variables as indirect indicators of sensitive information
User-based classification depends on a manual, end-user selection of each document. User-based classification relies on user knowledge and discretion at creation, edit, review, or dissemination to flag sensitive documents.

Content-, context-, and user-based approaches can be both right or wrong depending on the business need and data type.

Determining Data Risk

In addition to the types of classification, it’s wise for an organization to determine the relative risk associated with the types of data, how that data is handled and where it is stored/sent (endpoints). A common practice is to separate data and systems into three levels of risk

Low risk: If data is public and it’s not easy to permanently lose (e.g. recovery is easy), this data collection and the systems surrounding it are likely a lower risk than others.
Moderate risk: Essentially, this is data that isn’t public or is used internally (by your organization and/or partners). However, it’s also not likely too critical to operations or sensitive to be “high risk.” Proprietary operating procedures, cost of goods and some company documentation may fall into the moderate category.
High risk: Anything remotely sensitive or crucial to operational security goes into the high risk category. Also, pieces of data that are extremely hard to recover (if lost). All confidential, sensitive and necessary data falls into a high risk category.

Note: Some also use a more granular scale, adding “severe” risk or other categories to help further differentiate data.

Using a Data Classification Matrix

Creating and labeling data may be easy for some organizations. If there aren’t a large number of data types or perhaps your business has fewer transactions, determining the risk of data and your systems is likely less difficult. That said, many organizations dealing with high volume or multiple types of data are likely to need a comprehensive way of determining their risk. For this, many use a “data classification matrix.”

Creating a matrix rating data and/or systems from how likely they are to be compromised and how sensitive that data is will help you quickly determine how to better classify and protect all things sensitive.

An Example of Data Classification

An organization may classify data as Restricted, Private or Public. In this instance, public data represents the least-sensitive data with the lowest security requirements, while restricted data is in the highest security classification and represents the most sensitive data. This type of data classification is often the starting point for many enterprises, followed by additional identification and tagging procedures that label data based on its relevance to the enterprise, quality, and other classifications. The most successful data classification processes employ follow-up processes and frameworks to keep sensitive data where it belongs.

The Data Classification Process

Data classification can be a complex and cumbersome process. Automated systems can help streamline the process, but an enterprise must determine the categories and criteria that will be used to classify data, understand and define its objectives, outline the roles and responsibilities of employees in maintaining proper data classification protocols, and implement security standards that correspond with data categories and tags. When done correctly, this process will provide employees and third parties involved in the storage, transmission, or retrieval of data with an operational framework. The video clip below gives techniques for classifying sensitive data and is from our webinar, How Classification Defines Your Data Security Strategy, which is presented by Garrett Bekker, Senior Analyst, Information Security at 451 Research. You can watch the full webinar here.

Policies and procedures should be well-defined, considerate of the security requirements and confidentiality of data types, and straightforward enough that they are easy for employees promoting compliance to interpret. For instance, each category should include information about the types of data included in the classification, security considerations with rules for retrieving, transmitting, and storing data, and potential risks associated with a breach of security policies.

With the General Data Protection Regulation (GDPR) in effect, data classification is more imperative than ever for companies that store, transfer, or process data pertaining to EU citizens. It is crucial for these companies to classify data so that anything covered by the GDPR is easily identifiable and the appropriate security precautions can be taken.

Additionally, GDPR provides elevated protection for certain categories of personal data. For instance, GDPR explicitly prohibits the processing of data related to racial or ethnic origin, political opinions, and religious or philosophical beliefs. Classifying such data accordingly can significantly reduce the risk of compliance issues.

Steps for Effective Data Classification

Understand the Current Setup: Taking a detailed look at the location of current data and all regulations that pertain to your organization is perhaps the best starting point for effectively classifying data. You must know what data you have before you can classify it.
Creating a Data Classification Policy: Staying compliant with data protection principles in an organization is nearly impossible without proper policy. Creating a policy should be your top priority.
Prioritize and Organize Data: Now that you have a policy and a picture of your current data, it’s time to properly classify the data. Decide on the best way to tag your data based on its sensitivity and privacy.

There are more benefits to data classification than simply making data easier to find. Data classification is necessary to enable modern enterprises to make sense of the vast amounts of data available at any given moment.

Data classification provides a clear picture of all data within an organization’s control and an understanding of where data is stored, how to easily access it, and the best way to protect it from potential security risks. Once implemented, data classification provides an organized framework that facilitates more adequate data protection measures and promotes employee compliance with security policies.

Additional Data Classification Resources

The Definitive Guide to Data Classification
Gartner Report: Innovation Insight for Unstructured Data Classification
Webinar: Data Classification to Get Ahead of Compliance
How Should You Classify Your Data? A Guide to Using Context-, Content-, and User-Based Data Classification Effectively
Selling Data Classification to the Business: 3 Tips for Getting Organizational Buy-In
Expert Panel: The Top Considerations for Comparing Data Classification Tools
Digital Guardian for Data Classification

Frequently Asked Questions

What is the classification of data?

Data classification is the practice of organizing and categorizing data elements according to pre-defined criteria. Classification makes data easier to locate and retrieve. Classifying data is instrumental in promoting risk management, security, and regulatory compliance.

What are examples of data classification?

Following are some examples of data classification.

Implementing an automated process that searches files and employs content-based classification to identify sensitive data.
Using an automated context-based classification to identify sensitive financial records generated from ecommerce platforms.
Engaging subject matter experts to determine how to classify data with user-based classification.

Why is data classification important?

Data classification is important because it allows organizations to understand the types of information they are processing and storing. The knowledge gained through data classification allows a company to take the necessary measures to protect the data based on its importance or sensitivity.

Classification facilitates regulatory compliance and can result in cost savings by implementing the appropriate level of security for all information. By classifying its data, a company can concentrate its resources on protecting its valuable information with encryption and heightened security. Lower-risk data can be handled using less expensive methods.

What are the levels and types of data classification?

The three levels of data classification are:

Low risk - This classification level includes public data and data that is easily recreated or recovered.
Moderate risk - Data at this level encompasses internal data such as operating procedures that, while important to an organization, do not qualify as high-risk data.
High risk - High-risk data is sensitive and confidential data that should not be disclosed to the public. It also includes information that is essential for business operations and data that is difficult to recover.

Three methods are used to classify data.

Content-based classification searches files for sensitive information.
Context-based classification uses indirect indicators such as the data’s creator, location, or application to identify sensitive information.
User-based classification is a manual process that depends on user knowledge to determine the sensitivity of data elements.

Content and context-based classification can be performed with automated tools and can be supplemented with a manual, user-based process.

What is a data classification standard?

A data classification standard is the set of policies and standards an organization uses to classify its data. The standard provides a framework that is used to assess data sensitivity and assign it to the proper classification so it can be handled effectively. A standard ensures that an organization’s data is classified consistently, enabling more efficient information security, management, and compliance.

What are the 4 types of data classification?

Data types with similar levels of risk sensitivity are grouped together into data classifications. Four data classifications are used by the university: Controlled Unclassified Information, Restricted, Controlled and Public.

What are the 3 main types of data classification?

Data classification generally includes three categories: Confidential, Internal, and Public data.

Which of the following is typically not a characteristic considered when classifying data?

Which of the following is typically not a characteristic considered when classifying data? B. Size is not a criterion for establishing data classification. When classifying an object, you should take value, lifetime, and security implications into consideration.

What are the data classification levels?

Data Classification Levels Data Classification in Government organizations commonly includes five levels: Top Secret, Secret, Confidential, Sensitive, and Unclassified. These can be adopted by commercial organizations, but, most often, we find four levels, Restricted, Confidential, Internal, Public.