What is Data Classification?

With organizations expected to handle massive amounts of data over the course of everyday operations, it can become a major challenge to locate information quickly and to ensure that no sensitive or otherwise valuable data is left vulnerable. 
A key part of maintaining visibility and control over this information is data classification. 

Data classification is the continuous practice of tagging and organizing data into pre-defined categories, making it easier to locate and retrieve but also enforcing secure access for authorized users. In this introductory data classification guide, we will look at how the practice is essential for good data management, along with why it should be a critical component of your data security strategy.
 

Why classify data? In addition to making information easier to locate, it comprises an essential element in cybersecurity best practices. One of the greatest benefits of data classification is that you can tag progressively more sensitive types of data and use the categories to determine automated security responses to attempts to access, transmit or copy data. 

Depending upon the level of risk, this may involve restricting access or simply auditing an interaction so it is available for future review. By ensuring that security teams know where to find sensitive information and by putting rules in place about who is allowed to access it, you can prevent or contain data breaches and keep unauthorized users away from resources they shouldn’t have. Proper data classification practices are necessary for maintaining a strong security posture.
 

There are three primary types of data classification, each of which carries its own pros and cons, and different data classification solutions may focus on different approaches. Which approach you primarily use will depend upon factors such as the size of your organization, the training level of your users or the proportion of your data that would be considered sensitive.

• Content-based classification: This is the practice of examining files and searching for sensitive information inside them. This can be helpful if you have a problem of information that is not for public consumption hiding in seemingly innocuous file types. But you also run the risk of generating false positives that waste employee time.
• Context-based classification: Instead of examining file contents directly, this approach primarily looks at the metadata associated with files to find clues indicating that data inside is sensitive. This may include identifying the location where a file is saved, which user created it or which application the file is built for. This approach works well when your user base is well trained and you already have a degree of control over your sensitive data.
• User-based classification: This puts the burden upon users to comb through files and categorize them. While at its best this approach can significantly cut down on false positives, it relies upon having not only a highly trained user base but also the time to manually classify data. That means that it is typically only suitable for a leaner organization or a smaller dataset.

Most organizations distinguish among three levels of data risk, although your own needs might lead you to use a different number. It is important to note that these risk levels are not synonymous with data categories. In this list we will look at the three main risk levels and which data categories tend to correspond to each level; however, a category such as Personally Identifiable Information (PII) may fall anywhere on the risk spectrum from low to high, depending upon the company mission and what type of information is being gathered.

• Low risk: This data is safe for public consumption and does not need present a danger if it leaks. This tends to also mean that it is either easy to replace if it goes missing or not important to the organization’s operations. Some internal information may present a lower risk if its release would not present a competitive edge or damage an organization’s reputation.
• Moderate risk: This data is usually intended for internal consumption and should not be released to public view, but it does not present a major threat to the organization’s mission if leaked. This might include company records with no potential reputational risk but that might be difficult to replace if lost. Some organizations will use different categories for basic internal data and confidential information.
• High risk: Any data that has a direct bearing on organizational operations will fall under this level of risk. This includes proprietary information such as trade secrets. Data with a high risk level should have access tightly controlled and may beneficially be stored in an encrypted format.
   

Getting the most out of data classification requires taking proactive measures in several areas. These include:

• Identification – Find where your sensitive data resides, including cloud repositories and physical hard drives, and take any necessary immediate steps to secure them with encryption, physical access controls, etc.
• Organization – Come up with the scheme that you will use to organize data into categories. Don’t get overly elaborate; the fewer categories you use, the more effective your classification activities will be.
• Training – Empower employees to take a role in tagging data and placing it in the proper place based on its category. The more people who have a role in the process, the more stringent your training needs to be to make sure that human error doesn’t compromise your efforts.
• Compliance – Go to the effort of understanding the applicable data security and data privacy regulations for your operations, along with the penalties for noncompliance. See below for more about regulatory compliance.
• Solutions – Locate the data classification solution that best suits your organization. In many cases it can be best to utilize a comprehensive data security platform that can assist with data discovery, classification and prioritization instead of patching together different solutions from various vendors.

If your organization has a global footprint, there are likely multiple regulations dictating how you are expected to care for your data. Take time to understand the requirements of applicable regulations, which may include GDPR, HIPAA or PCI DSS. Especially when it comes to PII and Personal Health Information (PHI), your data classification practices should be drawn up in line with pertinent regulations. These will often impact where sensitive data is stored and how quickly it can be retrieved on demand. A good data classification solution can help you to anticipate your regulatory needs and respond quickly to audits and information requests.

You can increase the accuracy and efficiency of your data classification practices with Forcepoint Data Classification powered by Getvisibility. This solution leverages Machine Learning (ML) and Artificial Intelligence (AI) to more accurately classify unstructured data, all while covering the broadest range of data types in the industry. You can increase the increase the speed and efficiency of data classification to reduce false positives and spend more time on legitimate data security incidents.

And when you integrate Forcepoint Data Classification with Forcepoint Data Loss Prevention (DLP), you can select the requirements and criteria for data classification to easily deploy Forcepoint Data Classification into Forcepoint DLP and Forcepoint ONE integrated DLP policies.