What is Data Classification?

Data classification is a fundamental process that involves categorizing data based on its level of sensitivity, importance, or other predefined criteria. It is crucial to information management and security, allowing organizations to effectively organize, protect, and handle their data assets. By assigning classification levels for data, businesses can prioritize their resources and apply appropriate security measures based on the specific requirements of each data category. By following data classification best practices, this proactive approach helps mitigate risks, streamline data-handling processes, and ensure compliance with regulatory standards. 

Data classification empowers organizations to make informed decisions about data storage, access controls, data sharing, and retention periods. Ultimately, it enables businesses to optimize data management practices and protect sensitive information from unauthorized access, reducing the likelihood of data breaches and other security incidents.

Why Does Data Classification Matter?

Data classification is of paramount importance in the realm of digital security. For the audience of Digital Security professionals, understanding the significance of data classification is crucial in safeguarding sensitive information and mitigating potential risks. Security experts can identify the most critical and sensitive assets within an organization’s data ecosystem by classifying data. This knowledge allows them to allocate appropriate security measures, such as encryption, access controls, and monitoring, to the highest-risk data categories. 

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Using data classification, organizations can target security protocols in the most efficient way to achieve the greatest protection of their valuable and sensitive information. Beyond security, there are different types of data classification that aid compliance efforts, enabling organizations to align their security efforts to industry-specific regulations and legal requirements.

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What is PCI?

Organizations across industries grapple with the formidable Payment Card Industry (PCI) standards. These standards, established by major credit card companies, serve as a bulwark safeguarding cardholder data during payment transactions. Enter the Payment Card Industry Data Security Standard (PCI DSS), a robust framework that imposes guidelines and requirements on businesses handling, processing, or storing payment card information. Compliance with PCI is non-negotiable for entities involved in accepting, transmitting, or housing cardholder data—think merchants, financial institutions, and service providers. The PCI DSS unleashes a barrage of security measures: fortifying network security, employing encryption, tightening access controls, and conducting regular vulnerability assessments. 

What is PII?

When it comes to sensitive information, another area of concern is data that identifies a person, otherwise known as Personally Identifiable Information (PII). This term broadly covers a wide variety of data, including but not limited to: 

• names
• social security numbers (SSN)
• addresses
• phone numbers
• email addresses
• financial account details
• biometric data

PII holds significant value for individuals and organizations, as it is easily exploitable for identity theft, fraud, or other malicious activities. Identifying and safeguarding PII is crucial for privacy protection and regulatory compliance. Organizations must implement robust security measures, such as encryption, access controls, and data anonymization, to ensure the confidentiality and integrity of PII. 

What is PHI?

In the medical field, Protected Health Information (PHI) covers all sensitive data related to an individual’s health, medical conditions, or treatments, often including PII. This valuable information covers a wide range of data, including: 

• medical records
• diagnostic results
• prescriptions
• health insurance details
• any other personally identifiable health-related data

Managing PHI in the US is challenging as it is highly regulated under the Health Insurance Portability and Accountability Act (HIPAA), which ensures the privacy and security standards that care providers must follow. They must safeguard the confidentiality of PHI to protect patients’ privacy, prevent unauthorized access, and comply with legal requirements. Healthcare providers and organizations must implement robust security measures, including access controls, encryption, and audit trails, to safeguard PHI and prevent potential breaches. 

Challenges of GDPR

For any organizations that store data of citizens or residents of the European Union (EU), they have a more significant data privacy challenge than just identifying specific data types. They must comply with the General Data Protection Regulation (GDPR), which sets strict requirements for organizations handling personal data, and ensure transparency, accountability, and control over how personal information is collected, processed, and stored. As an incentive to comply, GDPR also imposes significant penalties for non-compliance, with fines reaching up to 4% of a company’s global annual revenue or €20 million, whichever is higher, making it extremely cost prohibitive for companies to ignore the mandate.

On top of this, it grants EU citizens and residents various rights, including the right to access their data, the right to be forgotten, and the right to data portability. Each of these rights must be facilitated by organizations storing their data, requiring them to at all times know where the corresponding data is stored, along with who can access it to maintain GDPR compliance. They must also include processes for deleting this data for an individual upon request, which relies upon knowing where the relevant data resides. 

Data Classification Levels

Data classification can be done manually or automatically, using a combination of human judgment and advanced algorithms. The data classification levels can vary, ranging from simple labels such as “public,” “confidential,” and “sensitive” to more detailed categories based on specific regulations and industry standards.

Example of data classification levels:

1. Confidential Data: This is the most sensitive category and includes data that must be protected at all costs, such as trade secrets, financial information, personally identifiable information (PII), and confidential business information.
2. Internal Use Only: This category includes sensitive data but is not as critical as confidential data, such as employee payroll information, internal memos, and project plans.
3. Restricted Data: This category includes sensitive data but is not as critical as confidential data, such as customer information, marketing plans, and pricing information.
4. Public Data: This category includes data that is not sensitive and can be freely shared with the public, such as company press releases and marketing materials.
5. Archived Data: This category includes data that is no longer actively used but still needs to be retained for legal, regulatory, or historical reasons, such as old financial reports and personnel records.

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Data Classification Use Cases

Regardless of the number of compliance mandates an organization must follow, embracing data classification is essential. Implementing data discovery as a best practice can significantly enhance security in a targeted and efficient manner. By understanding the sensitive data within their ecosystem and categorizing it accordingly, organizations can allocate resources more effectively and prioritize security measures accordingly. 

Data classification not only aids in compliance efforts but also plays a crucial role in preventing security breaches. By identifying and protecting sensitive data, organizations can mitigate the risks of unauthorized access and potential breaches, avoiding the negative consequences of compromised security. Embracing data classification and utilizing discovery techniques is a proactive step toward safeguarding valuable information and ensuring the integrity and trustworthiness of an organization’s data assets.

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‍What are Some Data Classification Examples?

There are several types of data that must be classified for better data security, as they are considered sensitive and require protection from unauthorized access, theft, or loss. 

Here are some data classification examples essential in many organizations:

1. Personal Identifiable Information (PII): This includes data that can be used to identify an individual, such as full name, Social Security number, driver's license number, or passport number.
2. Financial Information: This includes data related to financial transactions and accounts, such as credit card numbers, bank account numbers, and investment information.
3. Confidential Business Information: This includes data that is proprietary to a company and gives it a competitive advantage, such as trade secrets, business plans, and market research.
4. Health Information: This includes data related to a person's health status and medical history, such as diagnoses, treatment plans, and prescription information.
5. Intellectual Property: This includes data related to patents, trademarks, copyrights, and trade secrets.
6. Government Information: This includes data that is classified or restricted by government agencies, such as national security information, law enforcement records, and classified military information.
7. Employee Information: This includes data related to employees, such as payroll information, job performance evaluations, and disciplinary records.

These are just a few examples of the classification data that is vital for better data security. The specific types of data that must be classified will vary based on the needs and security requirements of each organization. However, the goal of data classification is always to help organizations better understand the level of sensitivity of their data and determine the appropriate security measures needed to protect it.

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How does Data Classification Improve Data Security?

Data classification is vital to data security as a means of organizing and categorizing data based on sensitivity, value, and criticality to the organization. This information is then used to prioritize and determine the appropriate security measures that need to be applied to protect the data from unauthorized access, theft, or loss. There are many key ways data classification is used in data security, including:

1. Risk Assessment: Data classification is used to identify the most critical assets and prioritize protecting sensitive data. This helps organizations to focus their cybersecurity efforts on the areas that require the most attention.
2. Access Control: Data classification helps organizations to determine who should have access to sensitive data and what level of access they should have. For example, highly sensitive data may only be accessible by a small group of authorized personnel, while less sensitive data may be accessible by a wider group of employees.
3. Data Encryption: Data classification helps organizations determine which data requires encryption and the necessary level of encryption. For example, some highly sensitive data might require encryption both at rest and in transit, while less sensitive data may only need to be encrypted at rest.
4. Data Backup and Recovery: Data classification helps organizations determine which data needs to be backed up and how often. For example, highly sensitive data may need to be backed up daily and stored in secure off-site locations, while less sensitive data may only need to be backed up weekly.
5. Compliance: Data classification is also used to ensure compliance with data protection regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI DSS). These regulations often require organizations to implement specific security measures for protecting sensitive data, and data classification is the first step in determining which data falls into this category.

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How Dig Security Drives Data Discovery

Dig is more than just a data discovery and classification platform; it is an entire Data Security Posture Management (DSPM) suite combined with Data Detection and Response (DDR).

Dig Security’s platform incorporates a powerful DSPM component that leverages advanced data discovery techniques. By scanning and analyzing data stored in structured and unstructured data in the cloud, the platform gains valuable insights into the content and context of the information. Potential risks are identified and prioritized through a data classification process and risk analysis, allowing organizations to assess their multi-cloud environment comprehensively. Organizations can proactively identify and address data loss risks by establishing a security baseline, enhancing their overall data protection strategy, and fortifying their security posture. 

In tandem with the DSPM component, the Dig Security platform incorporates the DDR feature to enhance security. DDR enables real-time detection of unusual patterns of data interaction, enabling swift identification of potential security threats. The system continuously monitors user behavior and data interactions, promptly recognizing changes that may indicate data at risk. 

By unifying static and dynamic monitoring, Dig Security’s platform reduces the chance of and minimizes the impact of data breaches. It enhances existing security controls to help organizations protect sensitive data and prevent potential breaches or ransomware attacks. With its advanced technologies, Dig Security’s platform provides significant advantages over traditional security solutions while reducing the burden on IT and security teams.

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FAQs

What is data classification?

Data classification is the process of categorizing and labeling data based on its level of importance and sensitivity. It helps to identify and protect sensitive information, such as personally identifiable information (PII), protected health information (PHI), and financial data.

What are the types of data classification?

The types of data classification include public, internal use, restricted, and confidential which is the most sensitive category, typically including personally identifiable information (PII) and trade secrets.

What are some data classification examples?

Data classification examples include Personal Identifiable Information (PII), Protected Health Information (PHI), financial data, intellectual property such as trade secrets and patents, and government information.