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Today’s organizations manage a mind-boggling amount of data.
This data contains everything from private medical information to calendar invites between coworkers, and can pose a real challenge from a risk governance perspective.
Your business needs a system for organizing both sensitive and low-priority data. Data classification can help you sort information according to risk level and set proper data security policies. Categorizing data can also help your organization streamline its data protocols.
It may sound technical, so we’ll provide a simple breakdown of the process. Read on to learn everything you need to know about data classification.
Data classification is the process of sorting data into different categories. This allows for easier management, security, and storage.
You can choose your own criteria for categorizing data. Then you can tag the data to make it searchable and trackable.
Data classification is useful for cybersecurity purposes, but there are other benefits. We’ll touch on why your organization should start classifying its data.
Data classification policies should help you develop a sensible risk management strategy. They can also be useful for creating data security and data retrieval processes.
The process of categorizing data can also improve your organizational efficiency. For example, you can find and cut duplicate data to reduce storage and backup costs.
Data classification is also needed for regulatory compliance.
Effective data categorization should help your organization:
Now that you know why data classification is worth the effort, we’ll walk you through how it’s accomplished.
Organizing data by sensitivity levels will help you understand where to focus your risk mitigation efforts.
The levels of data sensitivity range from high to medium to low. It’s helpful to think of data sensitivity in terms of how damaging it would be if lost or stolen.
The more sensitive the data is, the more you need to focus on protecting it.
High sensitivity data is commonly classified as restricted data. This is the data that if compromised, lost, or destroyed, would have a catastrophic impact on your organization. Organizations must place the strictest controls on high sensitivity data.
Examples of high sensitivity data include:
Medium sensitivity data is often classified as private data. It’s for internal use but would not have a catastrophic impact if compromised, lost, or destroyed.
Examples of medium sensitivity data include:
Low sensitivity data is classified as public data. It’s for public use and doesn’t require any confidentiality protections. Still, you may want controls in place to protect against damages.
Examples of low sensitivity data include:
Data is tagged based on a number of factors. These can include security, availability, confidentiality, integrity, and privacy.
The main methods of data classification include:
Many organizations use some combination of automated and user-based classification. Here’s how each classification works in practice.
Under user-based classification, you manually decide how to classify files. You can flag sensitive documents when they’re created, after an edit, or before a document is released.
Automated data classification categorizes file types by your pre-defined criteria. The two main methods of automated classification are content-based and context-based classification.
Content-based classification reviews files and documents for sensitive information before classifying them. A risk category is assigned based on what’s inside each file or document.
Context-based classification uses metadata instead of content to find indicators of sensitive information.
Examples of metadata include:
Automated classification tends to be more efficient than user-based classification. But, you should still verify the results manually.
Determine which classification system is right for your organization. Then, you can plan your data classification process.
There are some key steps your organization should take during the data classification process:
Mapping out this process can help provide employees and third parties with a clear framework for categorizing data.
Here are a few more questions that will help you develop your data classification policy.
Other questions that can help you develop your data classification policy include:
Answering these questions will help your organization think strategically about your data. Where are you vulnerable? How can you optimize your protection?
Once you can answer those questions, you should be ready to adopt your data classification policy. These are some guiding principles to consider.
Use these best practices to build an effective data classification policy:
Once you’ve taken these steps, you should:
After following these practices, you should understand your business’s data better. This will help you develop the best strategy for its management and protection.
Compliance frameworks can be useful for building your data classification policies.
There are several regulatory security frameworks that you should keep in mind when classifying data.
Systems and Organization Controls (SOC) 2 evaluates how a company’s security aligns with the Trust Services Criteria. These criteria include security, availability, confidentiality, processing integrity, and privacy.
This framework helps your organization manage customer data and third-party partner risk management.
While valuable, implementing SOC 2 can be complicated. Secureframe can help simplify your SOC 2 compliance.
The Health Insurance Portability and Accountability Act (HIPAA) created standards for protecting patient health information (PHI).
PHI is considered high-risk data. Healthcare organizations must follow strict cybersecurity practices to comply with HIPAA. You need procedures for classifying the data you collect, use, store, or transmit.
You can learn more about streamlining your HIPAA compliance here.
The Payment Card Industry Data Security Standard (PCI DSS) requires businesses that handle credit card data to protect cardholders’ information.
Unlike government frameworks, private payment companies (MasterCard, Visa, etc.) enforce PCI DSS compliance.
Here you can accelerate your PCI DSS compliance with Secureframe.
The General Data Protection Regulation (GDPR) protects the data of European Union citizens.
Under GDPR, any organization that handles an EU citizen’s personal data must have a data classification system. Organizations also need a system for tagging data as public, proprietary, or confidential.