In today’s digital era, data drives every organization. Every day, massive amounts of data are generated, ranging from personal information to financial information to intellectual property. Organizations often find it challenging to arrange, protect, and manage such a huge amount of data. Data classification is the solution to the problem every organization faces.
Categorizing data based on its sensitivity and the impact on the organization if it’s lost or stolen can help organizations arrange, protect, and manage it. Here, we will take you through the meaning and the different types of data classification, and will also help you understand how it is different for various industries.
Data classification forms the base of data security to help organizations manage and protect their data. The process helps categorize data on the sensitivity and importance, and also ensures compliance with regulations like GDPR and HIPAA. It helps prevent data breaches, reduces costs by preventing overprotection of less important data, and helps improve the overall data governance and business productivity.
The key reasons data classification is important include:
A. Improves Security
Data classification helps identify sensitive information, thus helping organizations safeguard sensitive data by implementing appropriate security measures, access controls, and monitoring to protect critical data from breaches, theft, or loss.
B. Supports Regulatory Compliance
The process helps organizations meet legal and regulatory obligations by ensuring sensitive and confidential data is handled in accordance with the specific guidelines, like the General Data Protection Regulation (GDPR) and HIPAA.
C. Helps in Efficient Data Management
Arranging data into different categories helps in more efficient handling and processing of the information. The data classification process makes it easy for organizations to locate, manage, and secure different types of information.
D. Reduces Costs
Organizations can focus on protecting the most important data by implementing data classification methods. It helps businesses decide on the level of protection for different types of data by classifying them on the basis of their importance.
E. Improves Awareness
Data classification is the process that helps improve awareness of the different data types and how they are responsible for protecting sensitive information. Proper classification enables a stronger data security culture.
F. Helps with Data Mapping
The classification process helps organizations map out their complete data landscape. It helps them gain a clear understanding of their data sets and the risks associated with them.
G. Maintains Trust and Reputation
Proper classification ensures effective protection of customer data and business data. By protecting such confidential data, companies can preserve trust and safeguard their brand reputation from the negative impacts of data compromise.
The benefits of data classification are enough to help you understand why companies often implement such methods.
What Are the Different Data Classification Types or Levels?
The data classification types are based on the sensitivity and access requirements for each data. The following are the common data classification types:
A. Public Data
Public data is the least sensitive data. This is the information that is freely available to the general public and does not need any protection. Examples of public data include marketing materials, public website content, and price lists.
B. Internal Data
This data is for internal use by employees only. The data requires some level of security. However, unauthorized disclosure of the data may lead to embarrassment or a short-term loss of competitive advantage. Examples of internal data assets include employee handbooks, sales playbooks, and organizational charts.
C. Confidential Data
Confidential data is sensitive data, which, if compromised, can cause harm to the company, its customers, partners, or employees. It needs clearance for access and is, therefore, considered sensitive. Data classification examples of confidential data include vendor contracts, employee salaries and reviews, and certain customer information.
D. Restricted Data
Restricted data is the most sensitive data of all the types. It includes personal information or data that can lead to significant legal, financial, or reputational damage if it is compromised. Examples of restricted data include personally identifiable information (PII), protected health information (PHI), credit card details, and trade secrets.
Data classification helps organizations understand the data type and apply security measures accordingly. Businesses often avail data classification services to ensure proper classification of data based on its content.
The Different Types of Data Classification Methods
The data classification methods are divided into three categories based on the type of data, where it is, and who is responsible for it, respectively. Here are the three different types of data classification methods:
A. Content-Based Classification
This method analyzes data or the actual content of the files and documents to understand their classification. It helps tag the data on the basis of the sensitive data the file or document has, like personal identifiable information (PII), or credit card numbers.
B. Context-Based Classification
This type of classification method examines the metadata around the data pipeline, which includes its source application, location, creation time, and owner.
C. User-Based Classification
This method relies on users to manually classify the data based on their knowledge and discretion. They assign labels like ‘internal use’ or ‘for your eyes only.’
Organizations use any of the three methods to classify vast amounts of data. The present AI-powered data classification process is different from the traditional strategy. The following section will help you understand how they are different from one another.
A Comparison Between Traditional and Modern Data Classification Strategies
Data classification strategies are broadly divided into two categories, namely, traditional and modern strategies. Here’s an insight into how these strategies differ from one another:
| Characteristics | Traditional Strategies | Modern Strategies |
|---|---|---|
| Approach | Primarily manual, with IT administrators or data owners tagging files based on predefined rules or regex patterns | Automated and AI-driven data classification |
| Data Types | Multiple points outlining an object’s exact shapeFocused on structured data | Handles both unstructured and structured data |
| Scalability | Limited and not scalable | Highly scalable |
| Accuracy | Prone to human error | More consistent and accurate |
| Context | Lacks context | Aware of the context |
The modern data classification tools have proved to be more effective in data loss prevention and classifying data properly. It can group the raw data into groups for a better understanding and implementation of security measures.
What Are the Different Types of Data Classification Schemes?
Data classification schemes can be defined as a structured system or framework to categorize data based on specific criteria, like sensitivity, confidentiality, compliance requirements, format, or usage within an organization. Here’s a brief explanation of the different schemes:
A. Sensitivity-Based Schemes
Under this scheme, data is classified based on its confidentiality and risk. The type of data may include public, internal, confidential, highly confidential/restricted. The schemes help organizations implement proper security controls as per the level of risk and regulatory demands. Organizations often avail content moderation services to ensure proper filtering of posts and comments.
B. Compliance-Based Schemes
In this case, the data is grouped to meet regulatory standards like PII (GDPR/CCPA), PHI (HIPAA), or financial data (PCI-DSS). This data architecture helps in legal protection and simplifies audits and reporting.
C. Format-Based Schemes
Here, the categories are based on the structure of data, such as structured databases, unstructured databases, and semi-structured databases. It helps in efficient storage, retrieval, and analysis, specifically for advanced systems and machine learning models.
D. Context-Based Schemes
The scheme classifies data based on its meaning and business use. Context-based data classification uses AI to analyze the context, such as user behavior, sentiment, or transaction history, for dynamic, real-time sorting and deeper business insights. Businesses often avail data annotation services to classify data.
E. Government and Commercial Schemes
Data is classified using schemas like top secret, secret, confidential, and unclassified. Commercial organizations often use categories like public, internal, confidential, and restricted. Hybrid or custom schemas are used to combine elements to fulfil requirements specific to industries.
Before we end this discussion, it is crucial to help you understand how data classification differs from one industry to another and how they implement their data management strategies to spot and prevent potential security breaches.
Understanding How Data Classification Is Different for Various Industries
A. Healthcare
In healthcare, data is classified based on its sensitivity and compliance requirements. This is crucial for Protected Health Information (PHI). The industry uses schemes to emphasize data classification levels of relevant data and categorize their data, like restricted/confidential, for patient records and diagnostic data to meet HIPAA regulations for access, handling, and sharing.
B. Financial Services
The financial sector prioritizes protection for client data (PII), transaction records, and payment data. The categories for such information include public, internal, confidential, and restricted. PCI DSS, SOX, or such other compliance frameworks demand proper classification for audit and reporting purposes. Strategic data, like trade secrets, merger plans, etc., is highly restricted to reduce risk.
C. Government and Public Sector
Government and public sectors use schemas like unclassified, confidential, secret, and top secret, following the national security laws and executive directives. Sensitive government data is handled under the highest restriction, while public data is accessible to all.
D. Commercial Organizations
In the case of commercial organizations, data classification is generally less standardized. The customization is mostly based on business needs, proprietary data, and user privacy. The levels of classification in this case include public, internal, confidential, and highly confidential/restricted. Sometimes the data is customized for cloud security, marketing, and HR records. The process follows regulatory schemes for specific data categories and protection rules. Businesses must implement proper security protocols to safeguard private data like customer information.
The process depends on various aspects, including regulatory data compliance and other aspects as mentioned here. It is also essential to know the steps to implement types of data classification in organizations.
Endnote:
Data is everywhere, but it is essential to understand which data is important and needs protection and which is not. Data classification is used to classify information based on its importance and implement proper security protocols. It enables businesses and organizations to understand the importance of each data and formulate data management strategies.
Understand how classification can be used to classify and protect data according to the data protection laws for a better and secure data environment. Also, understanding how machine learning data annotation plays a major role in the process is crucial.
Frequently Asked Questions
What is data classification, and why is it important in businesses?
Data classification can be defined as the process of systematically categorizing an organization’s data on the basis of its sensitivity, importance, and other criteria used to determine appropriate security measures and handling policies.
The process is important for businesses because it helps maintain data security by enabling targeted sensitive information protection, supports regulatory compliance by helping to meet requirements like GDPR and HIPAA.
What is a data classification level?
A data classification level can be defined as the category that organizes and ranks data based on its security, value, and the risk associated with its unauthorized access, use, or disclosure.
What are the common types or levels of data classification?
There are 4 different types or levels of data classification. They are:
➞ Public data – This is intended for public access and needs minimal protection
➞ Internal (or private) – Internal data is for private use only
➞ Confidential – This is sensitive data that requires restricted access to protect privacy or business interests
➞ Restricted – This data demands the highest security controls because of its critical nature
How does data classification help protect sensitive information?
Categorizing data based on sensitivity helps organizations apply customized safeguards such as access controls, encryption, and monitoring. The process helps in effective data protection and also prevents unauthorized access. Also, it ensures that sensitive data like personal information or trade secrets is protected against breaches or leaks.
There are different types of data classification for protecting sensitive information. Availing the services offering data classification can be of great help.
How to classify data?
The three common methods used to classify data are:
➞ User-based data classification – The classification depends on the user while creating the data
➞ Context-based data classification – It uses metadata or environmental data, like origin or user role, to determine sensitivity
➞ Content-based data classification – In this method, the data is analyzed directly to classify automatically or manually
What happens if data is not properly classified?
If data is not properly classified, it can expose sensitive data, increase the risk of breaches, operational inefficiencies, regulatory violations, hamper data retention, and cause reputational damage. If there’s no clear classification, organizations may end up overspending on protecting less sensitive data or fail to allocate resources when they are most needed.
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