What is Data Classification?
Every day, humans generate 2.5 quintillion bytes of data—that’s equivalent to 10 million blu-ray discs. While that increase can result in valuable new insights for businesses (and consumers), it can also result in a loss of data manageability and a greater risk to security.
While most data is harmless when exposed, there are still significant amounts of data that are classified as high-risk which has the potential to compromise the privacy of your customers or the intellectual property of your company.
More than ever, businesses need a plan for categorizing their data and determining risk. A good place to start is understanding data classification, the benefits of a strong classification process, and best practices to follow.
Data classification is the process of tagging or categorizing data by sensitivity, type, and value. When done effectively, data classification simplifies how we search, track, and filter data. Below are a few types of data you may need to classify.
Customer data: account information, bank account or credit card numbers, health records, payment history, or support interactions
Internal communications: email chains between employees, attached files, created documents, and internal presentations
Company information: business plans, financial projections, intellectual property, and revenue metrics
Within these categories, many variables affect data sensitivity. For example, an outdated email interaction with a client might not expose sensitive information. But an attached file that includes a client’s intellectual property could represent significant risk.
While you can perform data classification manually, you can also automate the process. To do this well, you’ll need a data classification policy or template that helps define your company’s specific parameters for classifying data (which we’ll explain in a minute).
Why You May Need a Data Classification Process
Most companies will benefit from data classification. Data classification can help you become more efficient, insight-driven, and even profitable. Below are just a few key benefits.
First, tagging data can help you identify and eliminate anything that’s redundant or outdated. You can reduce costs on unnecessary storage and create a more efficient data infrastructure.
Improve Business Outcomes
Second, data classification can help you leverage valuable data. Good data management and retrieval processes make it easier to identify helpful insights.
Reduce Costs and Prevent Fines
The average cost for a data breach in 2021 was $4.24 million. Financial companies, healthcare providers, medtech companies, universities, retailers, and manufacturing businesses collect and store large amounts of sensitive data. Having a good data classification program and strict security measures can help you avoid hefty fines that come with non-compliance.
Reduce Security Risk
Data classification can also help you mitigate security risks by:
Implementing appropriate security measures to manage, store, and transfer sensitive data.
Mitigating the risk of employee error (unintentional exposure of sensitive data).
Identifying sensitive data that can put your organization at risk for data leaks or breaches.
Build Customer Trust
Data classification can also play a role in boosting customer trust and retention. A recent survey showed that 87% of consumers won’t do business with a company if their security policies raise concerns.
To add, nearly half of consumers who stay up-to-date on data privacy issues chose to switch companies or providers over their data privacy policies. Creating the right infrastructure that properly classifies and stores data can protect your customers’ personal information.
Finally, data classification will help you ensure you stay compliant with information security standards, such as SOC 2, ISO 270001, and PCI, as well as regulations including HIPAA, GDPR, and CCPA.
Without a data classification policy, there is a higher risk that an organization may not identify the types of data they possess and in turn, the standards and regulations that they must adhere to.
Types of Data Classification
Classifying data isn’t always as straightforward as simply looking at a document. For example, some types of customer data may seem low-risk. But when exposed, this data can cause you to fall out of compliance with GDPR.
With that said, you need to consider what makes sense for data classification at your company. What kinds of data are you housing? What methods will allow you to assess the data? Here are the three primary ways to classify data.
Content-based classification asks the question, “What’s in the document?” It focuses on the content in the document itself and uses different methods to analyze or assess the content. It may involve file fingerprinting, which is used to identify and track sensitive information.
Context-based classification looks for context as a means of classification. It can include the person or creator of the file, the software tool that generated the data, or the location of the data. Context-based classification looks at the source as a potential indicator of file sensitivity.
User-based classification relies on the knowledge and insight of a user to assess a document or file for sensitivity and/or value. Both content- and context-based classification can be done through automation. User-based classification requires manual work to tag data. Both are valuable.
While some data is undeniably high-risk (electronic medical records) or low-risk (an outdated To-Do list), other types of data fall across a spectrum of sensitivity. Data is generally classified across four levels of security:
Public data can be exposed to the public with no risk. That can include press releases or job postings.
Internal-only data is accessible to employees with access. It represents a low security risk, but it’s not meant to be shown to the public. This includes business plans, some employee communications, or memos.
Confidential data requires a specific type of authorization or clearance to access. It often includes sensitive customer or client information, or driver’s license numbers.
Restricted data represents an enormous legal risk or irrevocable damage to the company if exposed. This data is often protected by confidentiality agreements or considered protected health information (PHI), and can include social security numbers and credit card numbers.
Part of effective data classification is knowing how to properly respond to each category with the correct measures and implementations.
To be effective, data classification cannot be an afterthought—it must be woven into the culture, processes, and tech stack of a company.
Data classification requires buy-in from:
C-suite execs and decision-makers
IT staff who will implement classification
Employees who are generating data
Getting support from everyone will help ensure that data classification is implemented effectively.
While some data classification may need to be performed manually, most of it can be done with an automated platform. Automation can help you identify sensitive material without spending hundreds of hours sifting through your data. It can also help you to classify your data on an ongoing basis, without additional labor.
If you’re looking to be compliant with SOC 2, ISO 27001, PCI DSS, or HIPAA, you may also want to use an automation platform that can continuously monitors your security posture and evidence collection to further simplify this process.
Implement a Data Classification Policy
Data classification requires you to develop a policy that addresses the unique aspects of your company and data. Your classification policy should provide the criteria that classify your data as low, medium, or high sensitivity. To create an effective data classification policy:
Write in clear, concise language
Consider the unique aspects of your industry and company
Use a Data Classification Policy Template
To help get you started, click below to download our data classification policy template and customize it to your needs.
To implement your data classification policy, you’ll want to use a tool that requires users to classify their data at the point of creation. You can also use the policy to retroactively classify data that’s already been created.