Data Classification: A Concise Definition

What Is Data Classification? The Simple Explanation

Data classification is the process of organizing data into relevant categories based on predefined criteria, such as importance and sensitivity, to make it easy to store, sort, and retrieve for future use. With a well-planned data classification system in place, it's easy to find and retrieve data, which is particularly important for data security, compliance, and risk management.

Key Concepts in Data Classification

Data Classification Process

While the steps to classify data differ from one organization to the next, here's what the process generally looks like:

Step 1: Gather Information and Assess Risk

Before they can classify data, organizations need to inspect all the data that should be classified. This includes knowing where it's stored, the number of copies available, and who can access it.

Once all the data is available, organizations should perform a risk assessment to determine the data's sensitivity and figure out how an attacker might try to breach the network.

Step 2: Develop Policies and Enforce Standards

Stakeholders then develop a framework to organize the data, including assigning tags and metadata to the information to make it searchable and sortable. The framework should include things like the number of classification levels and who should have access. This will allow the classification software to identify the categories the data belongs to.

Disclosure of restricted, confidential, or sensitive information (such as biometric data or protected health information) without authorization can be disastrous. To be able to follow the right protocols and prevent breaches, organizations should make sure the data is categorized according to the degree of sensitivity.

Step 3: Process and Monitor Data

In this step, data is identified and classified according to the classification policies defined earlier.

Continuous monitoring is also essential to ensure the data stays secure. With proper monitoring controls in place, it's easy to detect anomalies and reduce the time it takes to detect and mitigate threats.

Benefits of Data Classification

Data classification helps eliminate data duplication, reducing backup and storage costs while making the search process faster. It also helps organizations comply with industry-specific regulatory mandates like GDPR, DSS, PCI, and HIPAA.

Assigning classification levels also helps companies manage, handle, and protect their data assets. This in turn allows them to apply the right security measures and prioritize resources based on the requirements of each level. Plus, since the data is easy to find, companies can apply the appropriate protection to reduce the data footprint and lower the risks associated with exposure.

Types of Data in Data Classification

There are three sensitivity levels: low, medium, and high.

Low-sensitivity data is public information that doesn't need any access restrictions. This includes public website content, blog posts, and job postings.

Medium-sensitivity data includes data that's intended for internal use but that won't create a catastrophic problem if it's breached. Examples include nonidentifiable personal data and emails.

High-sensitivity data requires stringent protection and access control since it's usually protected by laws like HIPAA, CCPA, and GDPR. There can be a catastrophic impact on individuals and organizations if it's destroyed or compromised. Examples of such data include intellectual property, authentication data, and financial records.

However, since these labels are quite generic, organizations usually define their own categories that make the most sense to them. For instance, government agencies typically define data as top secret, secret, confidential, sensitive but unclassified, and unclassified. Meanwhile, private sector organizations might classify data as restricted, confidential, internal, private, proprietary, public, and archived.

Government Data Classification Categories

Below are the data categories that government agencies tend to use:

Top Secret

Information that needs the most protection and the highest access control is classified as top secret. If disclosed, it can threaten national security.

Secret

Secret data is not as highly classified as top-secret data, but it still needs a high level of protection. If disclosed, it can create a serious risk to national security.

Confidential

The lowest level of classified government information is considered confidential data and requires less protection than secret or top-secret data. Even so, disclosing it without authorization can still harm national security.

Sensitive but Unclassified

Data that is not classified as top secret, secret, or confidential falls into this category. It's still sensitive and requires some level of protection. If disclosed, it might violate citizens' privacy rights.

Unclassified

All data that's not sensitive is categorized as unclassified and doesn't need any protection.

Private Sector Data Classification Categories

Private sector organizations tend to classify data into the following categories:

Restricted

This includes highly sensitive data that's handled on a "need to know" basis and as such has restricted use and access. Examples of restricted data include personally identifiable information (PII), intellectual property, cardholder data, public health information (PHI), and trade secrets. If this data is disclosed without authorization, there can be significant legal or financial implications.

Confidential

Confidential data is internal to an organization and is typically subject to legal restrictions that regulate the way data should be handled. Examples of confidential data include contracts, marketing plans, employee reviews, and pricing. While unauthorized disclosure of this kind of data may not have catastrophic effects, it can still harm the company, its employees, partners, and customers.

Internal

Internal data is internal to an organization's operations, communications, contractors, and employees and is not intended for public disclosure. It's not as sensitive as confidential data and requires less protection. This might include employee handbooks, corporate guidelines, company-wide memos, project plans, employee payroll information, emails, and company directories. If this data is disclosed without any authorization, it will have a minimal impact on the organization but can lead to loss of competitive advantage and cause embarrassment in the short run.

Private

This usually includes personal data that might or might not be protected by law, like nonsensitive and sensitive personally identifiable information.

Proprietary

Data that gives organizations a competitive edge, such as organizational processes and business secrets, is considered proprietary.

Public

As the name suggests, public data is openly available and can be used and distributed freely without any legal restrictions. In addition to public web pages, this can include publicly disclosed information that companies use for market research, price lists, and press releases.

Archived

Archived data is no longer actively used but is retained for historical, legal, or regulatory reasons. Examples include old personnel records and financial reports.

Techniques in Data Classification

Three primary techniques are used for data classification:

Content-Based Classification

Files in datasets can sometimes include critical data that must be restricted. This is where content-based classification comes in. The process involves scanning, inspecting, and interpreting files to look for sensitive information. Based on the contents, it assigns labels or tags that define the type of data and its sensitivity level. This helps determine if the data can be available to the public or if it should be kept confidential.

Context-Based Classification

Instead of a file's direct content, this technique looks at the metadata, such as user information, file format, or file location, to determine if the content inside is sensitive. You can use this approach to automatically tag and classify documents produced by a particular user or application. And if, for example, the files were modified or authored by the finance department, they can be automatically tagged as financial information. You can also use this kind of classification to generate labels using predefined rules that clearly define the sensitivity level and data type.

User-Based Classification

In this approach, users go through the file contents manually and categorize them. Since it relies on the user's discretion and knowledge, the user must be highly capable and trained for this task. This user can either be the data's creator or have the authority to classify it. However, this method is not very scalable, especially for organizations that produce huge amounts of data.

Data classification helps organizations protect sensitive information, comply with regulations, improve data management, and optimize resources.