What Is Data Classification?

Reasons for Data Classification

Not all business data is equally sensitive or valuable. Some can be made public with little to no consequences. Other datasets may reveal valuable business secrets and intellectual property or disclose private information related to clients or finances.

This business data requires proper management and protection to prevent:

• Abuse
• 詐欺
• Fines for violating regulations.

The data classification process determines how you treat data across your organization, identifying what needs to be kept safe and secure and what can be made more accessible, both within the organization and to external parties.

Designating classification levels informs a range of data security processes, including:

アクセス制御

Determine the users who have access to different datasets.

While access is primarily determined by roles and ensuring users have the necessary data to complete their tasks efficiently, classification levels let organizations minimize the number of people with access to the most sensitive information.

暗号化

Choose the information that requires encryption and the level of encryption that should be implemented. Another factor is whether data is encrypted both at rest and in transit.

Some data should always be encrypted when sent across the public internet.

Data Recovery

Manage recovery systems that back up your most valuable data. Information that, if lost, would lead to significant disruptions or non-compliance. This includes:

• Defining how often different data classification levels should be backed up
• Where these backups are stored

リスクアセスメント

Data classification provides much of the information needed for data security risk assessments.

By categorizing your organization’s data based on sensitivity and value to the organization, you assess the risk associated with attackers gaining unauthorized access to each classification level.

Resource Allocation

Identify where to assign your resources to maximize protection for your most sensitive information.

Organizations must operate within budgetary and practical constraints, choosing where resources will have the greatest impact. Plus, identifying low-level data classification that requires minimal protection removes unnecessary security controls, resulting in:

• A better user experience
• A greater efficiency

コンプライアンス

Data classification helps ensure compliance by identifying the information that must adhere to regulations.

Whether it is dealing with General Data Protection Regulation (GDPR), the Payment Card Industry Data Security Standard (PCI DSS), or the Health Insurance Portability and Accountability Act (HIPAA), these regulations require dedicated security processes and controls to be in place for specific datasets.

The Data Classification Process

The data classification process varies depending on its objective and what your organization wants to achieve by defining set classification levels.

But, there are a few steps that are common across most data classification processes.

By following these steps, you enhance the outcome of your data classification process, ensuring that you categorize all data and utilize these levels to inform security controls and maintain compliance.

#1. Data Discovery

The first step is to identify all your data and understand the context for this information, including how it is used and where it is stored. Given the complexity of most modern business operations, data is:

• Generated by many users and systems.
• Stored across a range of different repositories.
• Copied to multiple locations or stores.
• Utilized by numerous applications, services, and employees.

The scale of data discovery often necessitates the use of automation and specialized tools to streamline this process.

While defining classification levels comes next, during discovery, you can also start to categorize based on different data types. This includes determining:

• Whether it needs to be retained in the first place
• Whether it is publicly available information
• Whether it must comply with any applicable regulations

#2. Defining Data Classification Levels

Develop the criteria or framework by which you will categorize your data. There are multiple ways to define your data classification levels. Identifying the right approach for your organization requires input from security teams, data scientists, end users, and other key stakeholders.

An easy approach might define three data classification levels:

• Low Sensitivity: Freely available to all users, including the general public. This information does not require data security controls.
• Medium Sensitivity: Also referred to as “business confidential,” this data classification level pertains to datasets that should only be accessible to employees. Although it contains some sensitive information, unauthorized access would not lead to severe consequences. An example could include anonymized data on staff and customers.
• High Sensitivity: Your most sensitive information that requires strict access controls and security measures to ensure compliance and prevent major disruption to your business operations. Examples could include financial records, client payment information, and intellectual property.

There are more detailed approaches that incorporate additional classification levels or employ more specific criteria to divide data. The most common approach would be to have:

• Publicly available information
• Internal-use information accessible to all employees
• Confidential information that requires some form of authorization
• Restricted information that only a small number of approved staff members can access

However detailed your final data classification levels are, they should provide a clear hierarchy based on the risk posed if the information were to be lost, stolen, or encrypted in a ransomware attack.

#3. Assigning Data Classification Levels

Reviewing all your data and assigning it to the corresponding classification level. With significant data volumes required for modern business operations, this step necessitates some form of automation. Automated data classification tools read the contents of your datasets, utilizing advanced algorithms to assign classification levels based on string analysis and understanding the context of each file.

While automated data classification is vital for preventing slow, time-consuming categorization processes, human input is also necessary to help improve accuracy. This includes:

• Human reviews that sample assigned classification levels for quality and feedback
• Manual inputs when automated tools identify edge cases between levels that would otherwise be assigned based on a lower confidence assessment.

#4. Translating Classification Levels to Security Controls

The goal of many data classification programs is to ensure compliance and minimize the risk of data breaches by implementing appropriate security controls.

Once you have organized and categorized your data into proper classification levels, you can align each level with the technical and administrative safeguards to mitigate associated risks. Examples could include:

• Public data that only requires integrity checks to prevent information from being changed or deleted by unauthorized users.
• Internal data that adheres to role-based access controls and perhaps encryption using less advanced standards.
• Confidential data that needs stronger access controls based on least-privilege access, activity tracking, secure sharing mechanisms, and higher levels of encryption.
• Restricted data that builds on confidential data security controls to include stronger encryption both at rest and in transit, multi-factor authentication, and continuous monitoring such that only authorized personnel have access.

You want to develop consistent security controls for each classification level that conform to industry standards, regulations, and customer expectations.

7 Best Practices for Effective Data Classification

While there are various potential challenges during data classification, these can largely be addressed and overcome by following a series of best practices. Here are data classification best practices to help deliver accurate categorization and improve future data security processes.

1. Larger operations with significant data usage shouldn’t try to categorize everything in one go. Incremental rollouts make the task more manageable while helping to identify and resolve challenges that limit data classification processes at a smaller scale before they impact the entire organization.
2. Rely on automation during both data discovery and classification. These tools can automatically scan and tag files based on the content they contain. Plus, security tools such as Cloud Access Security Brokers (CASBs) can help track data as it leaves your corporate network to be used by SaaS applications.
3. While automated data classification is vital for success, you should include humans in the loop to validate the results of these tools. Manually reviewing the results of automated data classifications helps ensure accuracy and alignment with your company’s policies.
4. Have a clear understanding of your regulatory requirements depending on your location and industry. Data classification compliance requires knowing what data is affected and the controls you need to put in place.
5. Deliver staff education programs and resources that communicate the value of data classification to minimize institutional resistance while teaching them how to handle data in the future based on their sensitivity level.
6. Implement processes that enable data classification at its creation. This could rely on both human input and automation, utilizing entry forms during the upload process and scans for sensitive data.
7. To minimize the risk of exposing sensitive data when using generative AI, educate staff on the data classification levels that can be entered into input prompts. Additionally, consider using private or self-hosted AI tools when analyzing sensitive datasets.