What is data classification?

Not all data is created equal. In today’s complex digital world, trying to protect every single data asset with equal force is neither feasible nor wise. With terabytes or even petabytes of data on their hands, data security teams need to get more sophisticated — they need data classification.  

Data classification is the practice of categorizing different data elements according to predefined criteria — such as type, sensitivity, or business value — so that you can easily reference it.  This is key to safeguarding critical and sensitive data because it allows you to apply effective security measures to the data that matters most. Data classification is central in protecting data from unauthorized access and breaches as well as ensuring full compliance with industry regulations and standards.  

In this article, we explore different data classification methods, their benefits and potential challenges, and how you can use them to reach your business goals.

The data classification process

Classifying data is a huge challenge, especially since businesses typically handle vast volumes of data. 

Here are a few simple steps you can take to make sure you get it right:

1. Define your goals

Before initiating the data classification process, it’s important to first identify security goals in the context of your specific business needs. 

Important questions to ask yourself: 

• What is this for? 

• What is the challenge I’m trying to solve? 

If your main objective is to comply with privacy regulations, for example, you should regularly assess which laws and regulations your company is subject to and identify the steps needed to protect data and avoid penalties. Common regulations to watch out for include the GDPR, CCPA, CPRA, HIPAA, and PCI DSS.

2. Assess the scope and prioritize

Data classification may seem like a monstrous challenge if you handle data on a large scale. But with some strategic thinking, classification can be reduced to manageable dimensions. Evaluating data through a meaningful set of criteria — such as risk, value, or regulatory requirements — will allow you to concentrate resources and security measures on the most sensitive and valuable information. This can dramatically narrow the scope of data classification, turning it into a highly targeted and feasible task.

3. Identify the relevant stakeholders in the organization

Identify who needs to get on board within the company, including security teams; governance, risk, and compliance teams; and engineering departments. Make sure to map their needs, their communication methods and existing workflows, and how they expect to use data classification in their work process.

4. Implement the data classification process

Set up and execute the classification methods that work best for your architecture and business objectives. 

This means working out some technical questions, such as:

• Do I scan data at rest or in motion? 

• Do I classify data based on context or content? 

5. Automate

It can be helpful to streamline the classification process with automated third-party security software solutions. These not only relieve you of manually performing arduous and error-prone classification tasks but can help you uncover data security gaps and support remediation.

6. Integrate with existing workflows

Once you understand what the stakeholders need and for what purpose, you can integrate your classification engine with existing workflows to minimize friction. This could include, for example, automatically generating a Record of Processing Activities (RoPA) for GDPR audits.

7. Reap the benefits of your work

Now that your critical data is being classified, it’s time to translate this into value. From a security perspective, you can define clear policies for securing sensitive data, including role-based permissions that manage how distinct data assets are processed, accessed, and stored. From a budget perspective, you can create policies for data retention and storage, determining the appropriate storage location and retention period for each data type. 

8. Rinse and repeat

It is advisable to regularly reassess and update your classification policies to ensure sensitive data stays protected at all times.

Data classification methods

Data classification is a big topic, and there are many things to consider before implementing it in your security toolbox. 

In this section, we take a look at two important aspects of data classification: the different data classification methods and the types of data being classified.

Classification levels

Many organizations categorize their data based on levels, which can be as detailed or as broad as the organization wants. 

The following data classification example shows how an organization might categorize their data based on levels that define how sensitive it is.

• High sensitivity: Data that belongs in this level includes important information that — if accessed by unauthorized users — could have detrimental effects on stakeholders. This includes financial account numbers, credit card information, and Social Security numbers.
• Medium sensitivity: This type of data includes information that is not public or available to those outside of the organization but is also not critical to operations or proprietary. Data in this level might include emails or documents that do not contain confidential data. 
• Low sensitivity: In this bucket, you can find data that is available to the public in websites, directories, and other repositories.

Context, content, and user-based classification

To stay on top of data security, it’s important to know what each type of classification is and how they differ from each other. 

• Context-based classification derives the data type from contextual information such as metadata, including history, attributes, asset owner, and environment. For example, data will be classified as an email address if it is found in a column named “EmailAddress.” Although this type of information is valuable, the conclusions drawn from metadata might be inaccurate, potentially rendering the classification misleading.
• Content-based classification, on the other hand, determines the data type by observing the data directly. This approach can identify whether a data asset is a name, email, address, or credit card number with a high degree of certainty, even if it is improperly tagged. For example, content-based classification can identify if a credit card number is located under a “comment” field.
• User-based classification depends on the manual input from a knowledgeable user and their discretion. Usually, these users label how sensitive the data is once they create or make edits to a document.  

You may be surprised to learn that most solutions perform classification based solely on context. Another subtle point to note here is that you cannot get context without looking at data in motion. The only way to obtain data in motion reliably and at a reasonable cost is to analyze data in runtime through the payload (as opposed to public cloud logs, such as AWS Flow Logs).

If you want to ensure sensitive data is recognized and classified correctly and cost-effectively, you should partner with a vendor that pairs content-based classification with context-based classification and make sure the latter is performed through the payload. Otherwise, you run the risk of racking up costs, missing out on important signals, and exposing vulnerable data to leaks and breaches.

Structured vs. unstructured data classification

Data comes in different shapes, but it can be broadly divided into two main groups:

• Structured data: Data in a “key-value” format, such as CSV files, JSON files, Excel spreadsheets, etc.
• Unstructured data: Free text, images (that might include free text), videos, documents, etc.

The important thing to note here is that the processes of classifying structured and unstructured data are different in nature, and not all classification solutions can handle unstructured data. 

The bottom line is this: If you think you may have sensitive data lurking in unstructured data, it is important to make sure your classification tools can detect and classify it. Remember that when data is processed by certain applications, it can be changed from structured to unstructured and vice versa. Classifying unstructured data is almost always a good thing to invest in.

Named entity recognition vs. large language models

Unstructured data used to be classified through traditional named entity recognition (NER) algorithms, which use machine learning to analyze labeled datasets. These algorithms were somewhat effective, but they had accuracy and context limitations, meaning they could only recognize a small set of data classes. 

Now, solutions that use large language models (LLMs) take data classification to a whole new level by recognizing a wide range of data types and catching the context that other models miss. LLMs are trained with vast amounts of data, which helps data classification reach high accuracy levels and align with industry benchmarks or out-of-the-box classifications. Examples of types of data that can be classified include anything from casual documents to complex source code, audio files, images, and videos.

The benefits of data classification

Taking the time to implement data classification tools into your data security operations may take some work, but it comes with some significant advantages.

The challenges of data classification

When incorporating data classification into your data protection strategy, there are some big pitfalls to watch out for. Let’s walk through some of these and how to handle them. 

Cost control 

With the massive volume of data generated daily, allocating adequate time and resources to collect, classify, monitor, and maintain it can quickly become expensive and complex, particularly when dealing with legacy data. Competing priorities and limited budgets can further exacerbate this problem.

To address this challenge, organizations can adopt an automated approach, eliminating labor-intensive tasks and the human error that comes with them. Additionally, organizations can prioritize the classification of the most sensitive pieces of information and implement policies that prevent the collection of unnecessary data, thereby saving time and effectively controlling costs.

Overreliance on engineering teams

Depending only on IT and engineering teams for data classification can create bottlenecks, tax teams, and lead to errors. With the complexity of the classification process and its technical requirements, this practice may not be sustainable in the long run. 

Automation can come to the rescue here as well. It can speed up the classification process, enhance its accuracy, and eliminate tension that may build between security and engineering teams.

Inconsistent policies and formats

Having inconsistent policies and formats chosen by different departments and teams can lead to confusion and errors, resulting in the loss of information, poor classification, and a waste of resources. 

To prevent this issue, organizations should establish standardized policies and formats that are adhered to consistently across departments.

Automated tools can help maintain this standard by enforcing predefined policies and formats. Regular monitoring, updates, and reviews can also help ensure these policies and formats remain relevant and effective.

Incorrect classification or missing context

Incomplete labels, poorly sorted data, missing context, or duplicate and ambiguous information can all lead to poor data classification. This can then result in critical errors. For example, the names of individuals in a health or financial record may be ascribed a low level of sensitivity when they should be tagged as sensitive and confidential.

To address these challenges, organizations should pay special attention to how data is collected, making sure they take into account metadata and missing links.

Automation tools can further help with this, using machine learning algorithms that mitigate anomalies, update policies, fix formats, and monitor data collection cost-effectively.

Data classification solution considerations

A strong data classification engine is imperative if you want to set rules and security controls that actually do their job. If you don’t have a firm grasp on what kind of data is flowing through your system, it will be nearly impossible to comply with regulations and mitigate risk.

The good news is that you don’t have to do this all by yourself. There are excellent third-party tools that can do the job for you. If you go down this path, however, there are several important things to watch out for. 

Here are some important things to assess before you sign a contract with an outside vendor that claims to classify data: 

1. How accurate is the classification solution? Can it handle unstructured data? Does it use content as well as context? 

2. Is the solution automated? How well does it integrate with your workflow?

3. Does the solution merely classify the data, or does it also come with tools that will enhance your organization’s security posture and provide reliable alerts?

If the vendor ticks all these boxes, chances are your classification journey will start on the right foot. This is cause for celebration — high-quality classification is one of the big milestones in reaching a robust security posture.

CrowdStrike’s data classification engine

Every moment, the amount of data under your care increases. Without a proper data classification strategy, businesses risk exposing sensitive information and facing severe legal and reputational consequences.

CrowdStrike’s data security posture management (DSPM) solution provides automated data discovery and classification. It is built to discover and classify sensitive structured and unstructured data no matter where it flows — whether it’s on-premises, in the cloud, or transferred to external services and shadow databases. 

CrowdStrike Falcon® Cloud Security brings runtime capabilities into DSPM, providing your team with an additional risk context layer that makes it easier to effectively prioritize risks and reduce alert fatigue. This enables organizations to safeguard their data across multi-cloud and hybrid deployments by responding to threats in real time.