The Ultimate Manual For Data Classification

Today’s organizations create anywhere from hundreds to millions of pieces of data each day. Some of this data is harmless and sometimes even useless in the grand scheme of things. Other types of data, however, can wreak havoc if stolen or destroyed. Data classification is the key to sorting through the endless mounds of data to guarantee data compliance and cybersecurity.

What Is Data Classification Anyway?

Data classification is an umbrella term used to describe the process of analyzing data and organizing it by relevant categories. The data may be classified by contents, file type, value, sensitivity, and other relevant metadata.

The main idea behind data classification is to discover what types of data an organization stores and where it is located. With this information, you can determine:

  • The value of your data
  • Whether the data is at risk
  • Controls needed to mitigate risk
  • How to comply with industry-specific regulations such as GDPR, PCI DSS, HIPPA, SOX
  • The security measures to prioritize
  • How to streamline e-discovery and data searches
  • Duplicate and stale data to get rid of
  • Investments needed for data security

At the very least, data classification helps enhance data security and guarantee regulatory compliance.

How Data Classification Works

In a typical organization, data may be stored in various locations such as cloud applications, network applications, hard drives, network servers, and folders. This divergence makes it difficult to organize and access the data.

Data classification helps identify where sensitive data is located, label the data according to its sensitivity, and specify how users access and share it.

Data classification may sound like a complex process, but it’s not something you’ll need to do manually. Today, data classification relies on automated, affordable, and easy-to-use data classification solutions.

This software discovers and classifies data automatically based on predetermined parameters such as Protected Health Information (PHI), Personally Identifiable Information (PII), and Payment Card Information (PCI).

The data classification software injects meta-data into the organization’s data to locate, sort, and label the data.

There are three main types of data classification:

Data-based classification – describes the nature of the data. A typical example of data-based classification is an email address or credit card number.

Source-based classification – describes where the data comes from. For example, customer data collected from an online opt-in form on the company website.

Context-based classification – describes the context of the data within the business. Examples of this kind of data include earnings data, public data, or sensitive data.

Here are some practical data classification examples to gain a better understanding of the topic:

Example 1: Data classification by sensitivity level

Typically, companies working with data with varying sensitivity levels assign three confidentiality labels to their data:

Public data – this includes documents that employees can share with the public unrestricted. Examples of this kind of data may include job postings, contact information, and product datasheets. Since there are no repercussions when this information is leaked to the public, there is no need to assign any controls.

Private data – private data typically includes internal information such as company-wide memos, business policies, and employee handbooks. This category may also include team-wide information such as contact information, marketing materials, and pricing materials. If leaked, repercussions may include reputation damage, losing the competitive advantage, and reduced brand equity. A business may implement various controls for this data, including advanced threat protection, data loss prevention, file encryption, and educating staff on data protection.

Restricted data – restricted data primarily includes documents subject to compliance restrictions. Typical examples of restricted data include health record information and payment card information. A business may implement high-tech security measures to protect restricted data, including reporting and auditing, advanced threat protection, limited access, and automated encryption.

Example 2: Data classification based on content

Our previous example of data classification deals mainly with data security and compliance with legal regulations. Another data classification option is sorting data sets by theme.

For example, an organization may label mortgage applications under “Finance” while employee offer letters may be categorized under “Human Resources.”

This kind of data classification makes it easier to search and locate files. Some software allows you to classify data in real-time.

For example, incoming data is classified and directed to the correct storage environment before it hits the disk. Then, it’s easy to create a searchable index to streamline access to crucial data.

Example 3: Day Pitney

Day Pitney, a U.S. law firm, is a real-world example of successful data classification. Dino Londis, the firm’s Information Security Manager, had specific concerns regarding data security, including:

  • The need to secure client data and proprietary data
  • Prove that internal controls are reliable
  • Require complete control of file share activity across the IT infrastructure, particularly of files containing sensitive information

Londis partnered with Netwrix, an IT security software company, to implement data classification. This collaboration allowed Dino to identify where all the sensitive data was stored, including personal health information and cardholder data.

Additionally, Netwrix software allowed the manager to take complete control of user privileges. He now has a full view of who has access to what data, including how they got that access. Dino even gets an alert every time a user joins a high-privilege group. This feature allows him to confirm that the user was approved by management.

Finally, the software allows the Information Security Manager to detect potential security risks such as inactive user accounts that are yet to be disabled and accounts with passwords that never expire.

Day Pitney is just one example of how data classification can help businesses meet their objectives. The objective, in this case, is to enhance data security and regulatory data compliance.

How to Get Started With Data Classification

There are plenty of benefits to be had from data classification. Some of the main reasons why organizations implement data classification include:

Data security – Data classification helps to identify sensitive data and place controls on who can access the data. These controls apply to users inside and outside the organization.

Compliance – Organizations that collect and handle sensitive data are bounded by regulatory requirements and legal compliance. In this case, classifying data and adding appropriate labels is part and parcel of enforcing the data classification policy.

Some states such as Nevada, California, and Maine have additional, comprehensive laws on top of the standard country-wide data protection and privacy laws. Also, EU countries, South Korea, Japan, and Australia, have stringent privacy protection laws. This makes data classification all the more critical for organizations working in these jurisdictions.

Awareness – The nature of data that employees work with day-to-day isn’t always apparent. Data classification helps make staff aware of the nature of the data they handle and its value. Additionally, users are consistently reminded of their responsibility to protect the data from alteration, damage, loss, or theft.

With these benefits in mind, here’s how you can get started with data classification:

Step 1: Start with a risk assessment of sensitive data

The first step to successful data classification is identifying all the data the organization collects, stores, processes, uses, and transmits. Next, determine the contractual and regulatory requirements related to the privacy and confidentiality of the data.

This process culminates in identifying the most critical data types and where to focus protection and controls. Examples of the most valuable data might include:

  • Business-critical documents such as agreements and strategic plans
  • Data assets, including information stored in your CRM database
  • Personal information such as social security numbers and payment information
  • Information and documents subject to regulations
  • Intellectual property, including technical specs and product designs

During this assessment, consider the impact of leaked, lost, or damaged information. For example, may the leaked information attract a fine from regulators, harm the brand, or expose your partners, customers, and suppliers? Taking this approach helps to determine the value of each piece of information accurately.

You can also identify specific types of data that employees should never collect or handle. For example, credit card information attracts a lot of scrutiny due to PCI requirements. In this case, you can create a specific policy that prohibits employees from handling credit card information under any circumstances.

Step 2: Develop a classification policy

Once you’ve identified the most critical data, you can now define who should have access to each type of data. Next, determine three to five categories for classification. Limiting your classes helps to keep things simple for users. Standard labels may include Confidential, Internal Only, Public, and Restricted.

It’s a good idea to standardize your classification policy across the entire organization. Use the same categories and labels across all departments to avoid inconsistencies and confusion.

It also helps if you have clear desired outcomes for your data classification project. While at it, set the scope for the project, especially for a large organization.

It may not be immediately possible to classify all of the organization’s data. In this case, it may be necessary to concentrate the initial efforts on specific departments.

By the end of this exercise, you should have identified:

  • Why the data classification process is essential and what you hope to achieve
  • A list of classification categories, complete with explanations
  • A roadmap for the classification process
  • How people should handle each data category, including how the data is stored, processed, shared, and encrypted

Finally, be sure to update your data classification policy regularly. Privacy regulations change and expand often, and you don’t want to be caught flat-footed in such an event. Also, make the updated policy easily accessible to all staff handling data and data systems.

Step 3: Choose a classification tool

While there is a manual element to data classification, the bulk of the work is automated. There are plenty of tools on the market to help you with this process.

Choose software that easily integrates with the existing applications that your organization already uses, such as Data Loss Prevention (DLP) technology.

This move also ensures you maximize the ROI of the existing technologies. It’s also much easier to integrate the data classification tools into the workflow with minimal disruption.

Consider software that makes it mandatory for users to classify data at the point of creation. This strategy makes employees constantly aware of the sensitivity of the information they handle day-to-day.

Some of the features to consider in your data classification software include:

  • Tagging and categorizing capability
  • Data security features to protect against cyber attacks
  • Duplicate detection and removal capability
  • Filters for low-quality data
  • Monitoring and alerts for security threats or data quality issues

Step 4: Start data discovery

It’s now time to catalog all the places you store data. Consider how you store and share data internally and externally. This includes cloud services like OneDrive, Google Drive, Dropbox, and mobile devices.

This is where the tool you chose in the previous step comes in. Good software will help you retrieve data from various sources and in different formats, including unstructured data. Besides, good software will help you discover data owners and search data using keywords or formats.

It might be worth looking for a data classification tool with data discovery features. Although there is a lot of overlap between data discovery and data classification, these are still two different concepts.

Data discovery, and data classification as a whole, is an ongoing process. The typical organization continuously adds new sources and new data. Also, data continues to be moved, changed, shared, and duplicated.

Some data may not be sensitive at the moment, but it might be once it’s changed. Mercifully, it is possible to automate data discovery and classification to stay on top of the process.

Step 5: Classify the data

With the help of your classification tool, determine the appropriate classifications for each data set. Then, apply the classification label to each item. Classification labels may include metadata or watermarks.

Next, assign controls for each data classification label. For example, you’ll need advanced protection for high-risk data compared to low-risk data. Match the security controls to the value of the data and its associated risk.

A good strategy is to start with the most recent data. This may include documents, files, and emails being created at the moment.

Set the president for how you handle data from the get-go. Then, you can focus on categorizing and labeling existing and legacy data. A discovery tool is crucial at this point, and most classification software comes loaded with this capability.

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Bryan Wise
Bryan Wise,
Former VP of IT at GitLab

Incredible companies use Nira