Guide to Data Classification: Types and Examples

Data classification is the process of organizing data into categories based on sensitivity, regulatory requirements, and business importance. It helps you understand what kind of data you have and who should have access to it. Perhaps most importantly, data classification helps determine how your data should be protected.

By assigning classification levels (such as confidential, public, or internal), you can make smarter decisions about data storage and security. Data classification simplifies data management and strengthens data protection. It also supports compliance with frameworks like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI DSS).

Data classification starts with visibility. You need to know what data exists before you can protect it. The process typically follows these steps:

• Data inventory: First, you take stock of your data assets, including structured and unstructured data, as well as where they live.
• Defining classification levels: Most organizations use a tiered system (such as public, internal, confidential, and restricted) based on how sensitive or valuable the data is.
• Categorization: Each data asset is categorized based on its content (what it is), context (how it’s used), and potential impact if compromised.

Organizations often rely on a mix of automated and manual methods to handle this process. Automated tools can scan large volumes of data quickly, flagging files or fields that contain sensitive information. But manual review adds human oversight, especially for ambiguous or high-stakes content.

Salesforce makes it easier to apply the right policies that help you maintain regulatory compliance, and it keeps your most critical data protected.

Read more on how you can classify sensitive data with Salesforce tools.

Data classification is the act of labeling and organizing data based on sensitivity or importance. It’s tactical and helps you apply the right protections and controls to the right information.

Data governance, on the other hand, is the broader framework for how data is managed across your organization. It defines policies, roles, and responsibilities to make sure data remains secure and accurate, while still being available for the necessary teams or processes.

The two are closely connected since data classification is a foundational element of effective data governance. Without knowing what kind of data you have, governance efforts can fall short.

A thoughtful data classification strategy does more than just check compliance boxes. Often, it sets the stage for better performance across your organization. Here’s what you can expect when classifying your data.

The more sensitive the data, the more it needs to be secured. Data classification helps improve data protection and privacy by preventing unauthorized access and applying the right controls where they matter most. It’s a key part of maintaining strong data security protocols.

Many regulations (including GDPR, HIPAA, and PCI DSS) require organizations to know what personal or sensitive data they store. Classification helps you meet these data compliance requirements with confidence.

Knowing where your most sensitive data lives helps you identify vulnerabilities and reduce the risk of data breaches or loss. Data classification also makes it easier to prioritize incident response and spot unusual patterns that could indicate a threat.

Clear data categories typically lead to smarter workflows. For instance, customer service teams can spend less time hunting down information and more time acting on it. Classification also helps support cleaner integrations and data classification uses across business systems.

There’s no single way to classify data, and different organizations use different models depending on their needs. That said, most data classification approaches fall into one of three categories: content-based, context-based, or user-based.

Content-based classification analyzes the actual data itself, often by scanning for credit card numbers or personal identifiers. This method, used by Data Detect, is helpful for flagging sensitive information automatically, especially large datasets.

Context-based categorization considers how and where the data was created. For example, data generated by your HR software might be tagged as internal or confidential by default, based on its origin.

User-based classification relies on human judgement. Employees manually assign classification levels based on their knowledge of the data’s sensitivity or importance. While this method can be slower, it’s useful for nuanced cases that automation might miss.

Although organizations are free to define their own categories, many data classification systems use the following five levels as a starting point. These levels help determine how data is stored and shared.

1. Confidential data: This includes highly sensitive information, such as trade secrets, financial records, or personally identifiable information (PII). Unauthorized access could lead to serious legal consequences and reputational harm.
2. Internal use only: Data intended strictly for internal operations, including team communications or internal reports. It isn’t sensitive enough to require heavy restrictions, but it shouldn’t be shared outside the organization.
3. Restricted data: This data is sensitive and access is limited to a select group of users. It may include project documentation, performance evaluations, or even early product designs. Security controls are typically stricter at this level.
4. Public data: Information approved for broad distribution, such as marketing content or published research. While it’s low-risk, labeling public data helps avoid confusion and misuse.
5. Archived data: Older data retained for compliance or historical purposes. Although it may not be actively used, it still needs to be protected based on its classification and retention requirements.

Building a consistent and scalable classification process takes planning. These best practices can help you get started or improve your current approach:

• Understand your data: Start with a full inventory of your data assets across systems and teams. You can’t protect what you don’t know you have.
• Set clear objectives: Define what you want your classification policy to achieve. That could include stronger data protection, more compliance support, or simplified access control.
• Identify compliance requirements: Know which regulations apply to your industry and geography. This will shape your classification levels and security protocols.
• Implement access controls: Use your classification labels to limit access based on roles or departments. You may also choose to implement need-to-know policies.
• Validate and audit regularly: Classification is not a one-time task. It’s important to review your categories and policies over time to make sure they’re still accurate and relevant.
• Automate where possible: Tools that can scan, tag, and track data automatically can reduce human error and simplify your data classification uses across the organization.

Imagine a healthcare provider handling patient records. Medical histories and diagnostic results would be classified as confidential data, which means they require strong encryption and restricted access. Internal notes between care teams might fall under internal use only. Marketing brochures or wellness tips posted on the provider’s website would be labeled public.

By classifying each type of information appropriately, the provider can maintain compliance with regulations like HIPAA while keeping sensitive data secure and accessible only to the right people.

Data classification is a foundational step toward better security and stronger data governance. However, factors like human error, time-to-execute, and the sheer effort required for bulk input often make manually classifying your data a challenge. To address this, Salesforce offers automated approaches to data classification

These tools include:

• Security Center: Identify sensitive fields with prebuilt and customizable categorization templates, advanced filtering, and bulk classification.
• Shield: Data Detect: Streamline data classification by linking data sensitivity levels and categories to actual field data, so you can take the necessary protective actions.

If you’re looking to turn your data classification policy into a full-fledged data protection strategy, these tools can make the process easier and get you set up for long-term success.