How to Classify, Protect, and Control Your Data: The Ultimate Guide to Data Classification

August 9, 2024
Srestha Roy

In our digital world, data fuels businesses. This power brings huge responsibility. Cyber threats are real and present dangers. One data breach can destroy a company causing money problems and long-lasting harm to its name. These breaches cost a lot – $4.45 million on average in 2023. This shows we need strong protection right away.

Data classification forms the base of this protection. When you grasp and use good data classification methods, you can guard your most important asset: your data.

Let’s look at how to change data from a weak spot into a strong point.

What is Data Classification?

Sorting data into groups based on type, content, and metadata helps companies understand their information better. This allows them to reduce risks and follow data governance policies effectively.

For example, a hospital may need to look at patient records with specific health problems for research purposes. A bank may also need to identify transactions associated with suspicious activities for compliance purposes.

Data classification standards and tools let companies find information that matters to them. It can help to show where your most valuable data sits or what types of sensitive data your users make most often.

By organizing data correctly, you can improve your organization’s security and compliance efforts.

Why is Data Classification Important?

With only 54% of companies knowing where they keep their sensitive data, calls for the need for a strong data classification policy. Knowing what data classification means helps protect important information from being lost, follow rules, and handle risks.

Protect Sensitive Information

Data classification is critical in information protection. Much data goes unsorted and unidentified within organizations, and we refer to this as dark data. This brings out the importance of a solid data classification policy.

Properly classifying data will be able to protect the confidential information of any business from unwanted eyeballs but also from possible data breaches. Using the appropriate sensitive data classification methods ensures the protection of data depending on the level of sensitivity, thus reducing the risks.

Compliance with Regulations

Classification of data helps companies to apply the laws. Laws, such as the GDPR, require that companies attain certain data classification standards.

Understanding data classification and using data categorization helps companies stay legal and avoid fines. This involves using examples and a matrix to organize data according to the law.

Risk Management

Data classification helps organizations assess and manage risks based on the types of data. This process supports applying the right security measures to reduce threats. Using data classification tools is important for effective risk management in cyber security.

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Types of Data Classification

Here is a view of the main types of data classification and their characteristics:

Public Data: This refers to data with no implied ownership and is freely available in the public domain. It does not require protection from unauthorized access but requires protection against unauthorized modification or destruction. Data classification examples for public data include market research data available without restriction on access or usage.

Internal Data: Information only for use by organization insiders, like memos, emails, and company rules. The categorization of internal data protects it and prevents moderate harm in case of its unauthorized disclosure. The data classification process for internal data uses reasonable security measures proportionate to the level of sensitivity.

Confidential Data: This includes sensitive information like employee reviews and vendor contracts. Deserve high protection so that this category may not be accessed by any unauthorized personnel to avoid potential damage. The methods of sensitive data classification treat this category under very strict security measures to avoid its exposure.

Restricted Data: This includes the most confidential and sensitive information, such as PHI and government-classified data. The data classification matrix has considered restricted data to be of the highest order of security with controlled access, for its unauthorized disclosure or change can cause substantial damage.

With respect to the healthcare sector, HIPAA (health insurance portability and accountability act) rules for classification mandate that organizations classify restricted data by sensitivity and potential impact if compromised.

This data classification policy shall ensure that such data has protection according to its critical nature and potential impact.

Data Classification Levels

Here’s a look at the main types of data classification levels showing why they matter and what protections they need:

1. High Sensitivity Data: This covers information that could lead to dire results for a company or people if it gets exposed. This kind of data needs tight access limits and safeguards because of how crucial it is and what the law requires, including GDPR data classification and other rules.

Data classification examples of sensitive info are money-related files, ideas protected by law, and login details. Putting strong data security classification steps in place is key to stop people who shouldn’t see this data from getting to it and to follow the rules.

2. Medium Sensitivity Data: This data is meant for internal use and, while it needs protection, its exposure wouldn’t be disastrous. Examples include non-confidential internal emails and documents, or blueprints for buildings in the works.

The data classification process for medium sensitivity data involves using sensible security measures to guard against unauthorized access while keeping it usable for internal needs. Good data classification methods make sure this data is protected without slowing down the organization’s work.

3. Low Sensitivity Data: This group includes information meant for the public and doesn’t need tight protection. Some examples are public website content, job listings, and blog posts.

To classify data at this level makes sure people can access it but can’t change it without permission. Using a data classification matrix helps companies sort and safeguard data based on how sensitive it is and how it’s meant to be used.

Having a clear data classification policy is important for organizing and protecting different types of data. This policy should use manual and automated techniques to ensure accuracy and efficiency.

Properly classifying data helps align security measures with the sensitivity of the information. This ultimately safeguards company assets and ensures compliance with regulations.

This process should use both manual and automated techniques to ensure accuracy and efficiency. Properly classifying data helps to align security measures with the sensitivity of the information, ultimately safeguarding company assets and complying with regulations.

Data Classification Examples

Here are some typical data classification examples that show different kinds of sensitive data and their classification levels:

PII (Identifiable Information): This data type includes info that can identify a person, like names social security numbers, addresses, and birth dates. Keeping PII safe is key to protect privacy and follow rules. The classification levels for PII give it a high sensitivity rating because unauthorized access or misuse could cause serious harm.
PHI (Protected Health Information): PHI includes medical records, health insurance info, and biometric identifiers. Keeping PHI safe is key to follow rules like HIPAA and keep patient info private. The data classification tagging for PHI marks it as sensitive, so it needs strong security to stop unauthorized access and keep data accurate.
PCI (Payment Card Information): PCI covers details linked to payment cards, like credit card numbers, names of cardholders when cards expire, and security codes. Companies must safeguard this data to stop money fraud and follow rules such as PCI DSS. The classification levels for PCI give it a high sensitivity rating, which means it needs tough security steps like encoding and limits on who can access it.

Protecting patient health data is crucial for healthcare providers in the U.S. HIPAA rules require tough security to stop data leaks and keep patient information private.

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The Data Classification Process

Here’s a look at the data classification process, including key ideas and terms:

Set Goals
- Set the aims of the data classification process.
- Pick out systems for initial classification.
- Make sure you follow rules like GDPR data classification.
Spot and Group Data Types
- Find data types (like customer lists, money records, PHI).
- Tell the difference between company data and public data.
- Spot data that laws control such as GDPR or CCPA data.
Create Classification Levels
- Choose how many classification levels you need.
- Write down each level with data classification examples.
- Teach staff how to classify data by manually if that's the plan.
Put Data Classification Rules into Action
- Create and use a full set of rules to classify data.
- Make sure everyone in the company classifies data the same way and follows the rules.
Pick Data Classification Methods
- Use both manual-sorting and computer-sorting to classify data.
- Sort by hand for data that needs a careful, case-by-case look.
- Let computers sort large amounts of data to keep things uniform.
Set Up Safety Measures
- Add safety steps based on how you've grouped your data.
- Use coding, limits on who can see what, and regular checks for weak spots to protect important data.
Spell Out Results and How to Use Them
- Write down steps to reduce risks and set up automatic rules.
- Use analytics on classification results to make better decisions.
Keep an Eye on Things and Fix as Needed
- Set up a regular process to classify new or updated info.
- Check and update the classification process often.

Methods for Classification of Data

There are basically two methods for classifying data with respect to its sensitivity and importance: manual classification and automated classification.

1. Manual Classification

Manual classification is the process where a human makes a judgement about data to be classified against predetermined criteria. The following are the key aspects:

Data Classification Tagging: Data gets tagged as sensitive, like PII, PHI, and PCI.

Compliance: This is useful in meeting specific compliance regulations, such as the GDPR data classification.

Examples: Applied for legal documents, sensitive business information, other forms of critical data.

2. Automatic Classification

Automatic classification uses technology to classify data quickly and consistently. The key aspects of this are:

Efficiency: It quickly processes huge volumes of data.

Consistency: Fewer human errors and uniformity in data security classification.

Tools: Leverage data classification tools with appropriate algorithms that support scalable, accurate classification.

Data Classification and Compliance

Data classification facilitates compliance with data protection regulations, such as the General Data Protection Regulation, the Health Insurance Portability and Accountability Act, or the Payment Card Industry Data Security Standard.

The majority of these regulations impose certain security measures within organizations on the protection of sensitive data, and data classification is a step that enables an organization to determine which data falls into the category.

For instance, the Cloud Security Alliance requests features like data type, jurisdiction, context, legal constraints, and sensitivity; its part, PCI DSS, does not require origin or domicile tags.

Let’s see how you can create your Data Classification Policy:

Data categorization: Know who the data was created by or who owns the data and which organizational unit can bring the most context to the data.

Data classification process: Define at what frequency classification will take place, types of suitable data classification, and technical means for data classification tagging.

Regulatory compliance: Check the applicable regulations (e.g., GDPR, PCI DSS) also, what risks are in place in case of no compliance.

Data Classification Challenges

Following are some data classification challenges that are often faced by organizations and that may bring inefficiency in managing and protecting data.

Finding Data and Location: Identification of sensitive data within an organization is typically constrained because of organizational silos and a variety of data storage systems.

Manual Classification: Manual classification of data is time-consuming, prone to errors, and labor-intensive.

Inconsistency: Incoherent methods and criteria of classification prevent adequate protection of organizational data.

Cost and Resource Constraints: A holistic program of data classification may involve substantial investments in technology, people, and time.

Compliance Complexity: The compliance landscape is increasingly complex with evolving data privacy regulations, and the need for maintaining accurate classification.

Organizational Resistance: Resistance faced from employees in implementing data classification initiatives and attempting to change organizational behavior towards a data protection culture.

Data Ownership and Responsibility: Lack of clear ownership and responsibility for data gives a way to confusion and can expose data.

Best Practices in Data Classification

Organizations need to follow the best practices in data classification to overcome and optimize the related challenges in data classification:

Automated, Real-Time Classification: Utilize available data classification tools to automate and make data classification easier. This also extends to real-time scanning and tagging of data classification based on predefined parameters.

Commit to Data Classification: Get management approval in order to emphasize that data classification is a must across the corporation. This commitment creates a culture in which data security classification and protection will be of main concern.

Establish a Culture of Compliance with Data Privacy: Train employees on their roles regarding the classification of data and protection of sensitive information. Regular training keeps privacy and security awareness part of everyday operations.

Collaborate with IT and Business Units: Collaborate with information technology and business teams in creating a standardized data classification framework. Such collaboration ensures consistency of advice, guidance, and approval through the process of data classification.

Minimize storage of any excess sensitive data: Apply data classification techniques to mark duplicate or out-of-date data for destruction, thus improving the relatively simplified protection of data.

Adoption of such best practices would manage data successfully in these organizations, ensure compliance, and maintain the organizations within better data security.

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Frequently Asked Questions

How can a data classification standard help with asset classification?

A data classification standard provides an organization with a structured approach to classifying data based on its sensitivity, value, and criticality. This will help in asset classification by:

Identification of critical assets: With data classification standards, an organization will be in a better position to identify and give priority to the most sensitive and valuable data assets.

Improved security: Assets will be classified in accordance with data sensitivity, improving proper security controls for better protection.

Simplification of compliance: Proper data categorization with the aim that data assets will comply with regulatory and industry standards such as GDPR and PCI DSS.

How to deal with imbalanced data in classification?

There are several strategies related to imbalanced data in classification.

Resampling techniques, either by oversampling using SMOTE or under sampling to rebalance class distribution.

Algorithm selection: Choose algorithms that work fine on imbalanced datasets; examples are decision trees or ensemble methods.

Performance metrics: Metrics like precision, recall, and the F1 score over accuracy in class imbalance problems.

Data augmentation: Generation of synthetic data to improve the minority class.

What is a data classification policy?

A data classification policy is a formal document outlining the structure for classifying data in an organization. It generally provides for the following:

Classification levels: This defines categories which would be something like public, internal, confidential, and restricted.

Responsibilities: Specify who is responsible for classifying data—this may be the data creators, the subject matter experts, or the data stewards.

Procedures: Details how often and how the data should be classified and what type of data needs classification; methods of classification.

Compliance requirements: It confirms that data classification abides by all regulatory and industry standards, such as GDPR and PCI DSS.
Security measures: Ensure that for each level of classification, a proper sensitive information security protocol is in place.

About Author

Srestha Roy

Srestha is a cybersecurity expert and passionate writer with a keen eye for detail and a knack for simplifying intricate concepts. She crafts engaging content and her ability to bridge the gap between technical expertise and accessible language makes her a valuable asset in the cybersecurity community. Srestha's dedication to staying informed about the latest trends and innovations ensures that her writing is always current and relevant.

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