Understanding the different data classification levels

Different types of data require different levels of storage and protection. For example, credit card numbers and medical records are inherently more sensitive than names or job titles – and are therefore higher-risk.

Data classification allows a business to determine the risk level of every type of data it handles, whether it belongs to businesses or individuals. Appropriate data classification levels enhance a business’s security, compliance, and data management efficiency.

Data breaches can happen to any business, regardless of size, and can have a range of impact. For example, more than 39 million people were affected by healthcare data breaches in a hack that revealed names, social security numbers, lab test results, diagnoses, and radiology reports.1

What are the four levels of data classification?

Level 1: Unclassified/public data

Unclassified data is publicly available and not protected by law or regulation. This data is unlikely to affect the individual’s or business’s safety or privacy if revealed. First and last names, company names, dates of birth, and public addresses, phone numbers, and email addresses fall under the unclassified data definition.

Level 1 data does not require protection measures.

Level 2: Internal use data

Internal only data is private business information and should be protected from public access. Internal data can also be restricted internally based on seniority, and can include business strategies, internal emails, budgets and projections, and IP addresses.

Level 2 data should have restricted internal access to maintain confidentiality.

Level 3: Confidential data

Typically, only selected individuals in a business have access to level 3 information with a confidential data classification.

This includes social security numbers, ID numbers, credit card data, and other financial records. If exposed, this data is highly likely to damage a business or individual’s privacy and security.

Encryption and strict access controls are required.

Level 4: Highly confidential/restricted data

The highest level of data classification is the most sensitive and prone to risk, which includes confidential health data, tax-related information, intellectual property, and any data protected by state and federal regulations. Exposure can lead to severe fines and legal sanctions, as well as breaches of privacy and security standards.

Level 4 data demands multi-factor authentication and advanced encryption.

How to classify data effectively

  1. Identify data types and sources: It’s best practice to aware of the data classification levels of the information an organization handles, its storage requirements, and its risk levels. Data mapping visualizes data flows, identifies the lawful basis for processing it, and shows who or what has access. This illustrates whether or not current data processes and environments are appropriate for the risk levels.
  2. Assign classification labels: Labeling enables organizations to use machine learning and AI solutions to automatically store level 1-4 data appropriately. A label can summarize the data’s sensitivity level, control access to it, and assign it to an encrypted database. Organizations using AI can identify and contain a breach 100 days faster than other businesses, reducing the associated costs by nearly $1.8 million.2
  3. Implement access controls: Levels 2, 3, and 4 demand role-based access control, two-factor authentication, and access monitoring. These measures make it more difficult for external actors to gain access and create an audit trail if a cyberattack does occur. Regular audits reduce privilege misuse and can cut the average cost of a breach by $180,3582.
  4. Train employees: Data breaches can involve a human element such as stolen credentials, phishing, or employee error3. All staff members should understand the four levels of data classification and their access privileges, and receive regular training to highlight proper data handling and storage. The most effective training sessions are customizable depending on access level, give employees the opportunity to identify data hacking threats and generate measurable results.

Benefits of data classification for businesses

  • Enhanced data security: The most sensitive data types should receive the appropriate level of protection. Data-breach motivation is financial gain. Any potential data breach for profit is significantly less damaging to a business if data is tightly controlled and less accessible to hackers. Other common triggers for data theft include espionage and disgruntled employees. Role-based data access can also reduce internal privilege misuse and abuse.
  • Regulatory compliance: One of the main benefits of data classification is to help businesses comply with regulations at industry, state, national, and international levels. This can reduce penalties, costly audits, and legal action.
  • Improved data management: 80% of data management decision-makers say they struggle to understand what data they have, how it is used, and who owns it3. Streamlined data storage, retrieval, and disposal saves time for internal teams. Data labeling can be more accurate and up to date, maintenance costs can be lower, and the value of data can be leveraged more effectively.
  • Better decision-making: Classified data is easier to search and segment, helping team leaders make informed choices grounded in evidence. The most effective data management automation tools can also produce workflows, allowing teams to take action if data needs to be quarantined, reorganized, or removed.

Safeguarding success through data classification

With PayPal Business, your organization can proactively adopt data classification to safeguard its valuable information and customer data. Our fraud monitoring tools help detect threats 24/7 for businesses of all sizes from payment fraud.

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