Data Classification

What is Data Classification?

Organizations use data classification to protect and handle data assets with security measures appropriate to the data’s level of sensitivity, importance, or other criteria. Although data classification is complex process, it is nevertheless essential to protect data across networks, clouds, email, endpoints, and the web. Advanced technologies like Artificial Intelligence (AI) and large language models (LLMs) are making the process more efficient, reducing false alerts, and better preventing data loss.

Data classification’s primary goal is to assign a category or label to each data instance, enabling better understanding, analysis, and decision-making. Which classification model an organization chooses depends on specific problems, data characteristics, and the trade-off between various performance aspects, such as accuracy vs. interpretability.

Importance of Data Classification

The data classification process is vital to safeguarding sensitive information, mitigating risks, and ensuring compliance. Identifying and classifying your organization’s most critical and sensitive assets allows you to implement appropriate security measures, such as encryption, access controls, and monitoring, to achieve the greatest level of protection for valuable and sensitive information. Organizations can uses different types of data classification security to align their efforts to industry-specific regulations and legal requirements.

How Data Classification Improves Data Security

Data classification organizes information into groups based on how sensitive or important it is. Security teams can then decide what security measures are needed to protect the information.

• Risk assessment identifies the most critical assets. It prioritizes protecting sensitive data, helping organizations focus on areas requiring the most attention.
  
• Access control ensures that only those who need sensitive data for their tasks are granted access. For instance, highly sensitive data might only be made accessible to a small group of authorized users. In contrast, less sensitive information is made accessible to a broader group.
  
• Data encryption is typically applied to highly sensitive data at rest and in transit, while less sensitive information might only be encrypted at rest.
  
• Data backup and recovery are performed based on data sensitivity. Highly sensitive data is often backed up daily and stored in secure off-site or cloud environments; less sensitive data might only need backup weekly.
  
• Compliance with GDPR, HIPAA, CCPA, and PCI DSS is enhanced by implementing specific security measures for protecting sensitive data.

Types of Data Classification

Data classification includes three types:

• Content-based classification relates to sensitive information like PII and financial records.
  
• Context-based classification analyzes data based on location, creator, application, and more as indirect sensitive information indicators.
  
• User-based classification employs user knowledge and discretion to determine whether sensitive documents should be flagged during the creation, editing, review, and distribution processes.

Other classifications include automated, manual, and regulatory. Which of these classifications an organization uses depends on its business needs and data type. Many businesses use a combination of these methods, tailoring them, for instance, to regulatory environments to ensure minimal risk and maximum sensitive data protection.

Key Components of Data Classification

The five levels of data classification are public, internal use, restricted, archived, and confidential. Confidential is the most sensitive category and typically includes personally identifiable information (PII) and trade secrets. PII data classification is crucial for meeting compliance and regulatory requirements, minimizing data exposure, and improving data access management. It also builds customer and stakeholder trust.

PII classification levels can vary, ranging from simple labels to more detailed categories based on industry standards and regulations. Examples include:

1. Confidential data. This is the most sensitive information an organization handles, including items like social security numbers and business trade secrets. Unauthorized access to confidential data could lead to severe consequences, including financial losses and reputational damage. For example, a document detailing plans for a new product yet to be patented would be classified as confidential to protect competitive advantage.
   
2. Internal use. This data is intended for use within the company. While not publicly sensitive, its unauthorized disclosure could be inappropriate or harmful. This category can include internal communications, procedural documents, and operational data that don’t need the highest level of security but should still not be available to the outside world. An example of internal use data is a company’s internal performance reports, which are shared among department managers to assess progress but are not for public dissemination.
   
3. Restricted data. This data classification requires stricter controls than internal data, mainly due to its potential impact on privacy or business operations if disclosed. Restricted data access is typically limited to individuals who need it to perform their job functions, such as personal health information (PHI), that must comply with HIPAA regulations. Another example is employee personal information, which is accessible only to HR and payroll departments to ensure privacy and compliance.
   
4. Public data. This includes information that can be freely shared with the public without any risk of harm to the organization. This classification is often used for marketing materials, press releases, and other documents intended for wide distribution. A company’s press release about a community engagement event designed to enhance public relations is a good example of public data.
   
5. Archived data. This classification refers to information that’s no longer actively used but must be retained for legal, historical, or regulatory reasons. The data is stored securely to ensure it remains intact and accessible when needed, but it is not part of the day-to-day business processes. For instance, an organization might archive tax records for the required number of years stipulated by law, ensuring the data is available for audits or historical analysis but not used in current operations.

Applications of Data Classification

Adopting and implementing data classification is essential, no matter which compliance mandates an organization must adhere to. It significantly enhances security in a targeted and efficient manner, allowing organizations to allocate resources more effectively and prioritize security measures accordingly.

Beyond compliance, data classification plays a crucial role in preventing security breaches, identifying and protecting sensitive data to mitigate the risk of unauthorized access and potential breaches. It is a proactive best practice that safeguards valuable information and ensures the integrity and trustworthiness of an organization’s data assets.

Types of data that should be classified for adequate protection against unauthorized access, theft, or loss include:

• PII, including full names, Social Security numbers, driver’s licenses, and passport numbers.
  
• Financial information such as accounts, transactions, credit card numbers, investment information, and bank account numbers.
  
• Confidential business information or proprietary data that gives a business a competitive advantage. This can include everything from trade secrets to market research and business plans.
  
• Employee information such as payroll information, disciplinary records, and job performance assessments.
  
• Health information, including an individual’s medical history, health status, diagnoses, treatment plans, and prescriptions.
  
• Intellectual property such as trademarks, copyrights, and patents.
  
• Government information classified or restricted by agencies, including law enforcement records, national security data, and classified military information.

Which types of data an organization should classify varies based on its security requirements. The ultimate goal of data classification is understanding the level of sensitivity and determining appropriate security measures to protect it. An advanced data security platform integrates seamlessly with an organization’s existing infrastructure to enforce modern data protection policies, monitor access, and detect anomalies in real-time, ensuring all classified data, regardless of type, has the appropriate level of security based on its sensitivity and potential risk.