Top 4 Data Classification Levels Every Organization Should Use
Top 4 Data Classification Levels Every Organization Should Use
Riley Walz
Riley Walz
Riley Walz
Mar 26, 2025
Mar 26, 2025
Mar 26, 2025
Consider your business has a data breach, and you don't know which files were accessed. You panic and call your IT department. They get to work and discover that the hackers accessed your customer database containing personally identifiable information (PII). They also found that the files were unencrypted and the information was outdated. Not only would you breathe a sigh of relief at the news that it wasn't an active project leaked, but you'd also be embarrassed at the poor security practices that led to the breach. What if I told you you could have avoided this scenario with proper data classification? By classifying customer data according to its sensitivity, you could have ensured that files like these were encrypted and even isolated from your current operations to prevent any breach impact on your organization.
AI data classification levels help organizations secure their data against unauthorized access and help businesses comply with various privacy regulations, such as HIPAA and the GDPR. Let’s explore the top four data classification levels every organization should use so that you can start securing your data today. With the right tools, creating a data classification structure can be simple. Numerous spreadsheet AI tool can help you get started by scanning your spreadsheets for sensitive information, automatically classifying data, and applying your organization’s policies quickly.
Table Of Contents
Top 4 Levels of Data Classification Every Organization Should Use
Additional Data Classification Levels Used in Some Organizations
Common Challenges in Data Classification (And How to Overcome Them)
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
What is Data Classification?
Data classification is the systematic process of organizing and labeling data based on its level of sensitivity, business importance, and regulatory requirements. This process ensures that appropriate security measures, access controls, and handling policies are applied to different data types. Instead of treating all data the same, classification allows organizations to:
Identify which information is critical or sensitive.
Apply the proper security controls and compliance policies.
Automate workflows to improve efficiency.
Prevent unauthorized access or accidental data leaks.
Data classification helps businesses, governments, and organizations structure their data intelligently and securely.
The Importance of Classification Levels in Data Security
Organizations face significant risks such as data breaches, non-compliance fines, and inefficient data management without a proper classification system. Below are the key reasons why data classification is essential:
Enhancing Data Security
Organizations store vast amounts of information, from customer records to financial statements. Not all of it needs the same level of protection. For example, a company’s internal policy documents may be less sensitive than customer credit card details, which require encryption and strict access control. Organizations can protect sensitive data by classifying data while allowing easy access to public or non-sensitive data.
Ensuring Regulatory Compliance
Governments and industry regulators impose strict rules on handling certain data types. Regulations like GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and PCI-DSS (Payment Card Industry Data Security Standard) require businesses to identify and protect sensitive data (e.g., personal identifiable information, health records, credit card data). Businesses risk violating these regulations without classification, leading to heavy fines and legal consequences.
Controlling Data Access and Preventing Insider Threats
Not all employees should have access to every piece of company information. For example, a marketing assistant shouldn’t have access to executive payroll data. Classification ensures that data is only accessible to the right people, reducing the risk of insider threats, human errors, and accidental leaks.
Improving Operational Efficiency
Without classification, employees spend excessive time searching for data, verifying access rights, or applying inconsistent security rules. By labeling and structuring data properly, businesses can:
Speed up decision-making with clearly identified, accessible information.
Reduce redundancy by avoiding duplication of storage of the same data.
Optimize storage costs by archiving or deleting low-priority data when necessary.
Enabling Smarter Automation and AI-Driven Protection
Manual classification is time-consuming and inconsistent. AI-powered tools like Numerous can automatically detect and classify sensitive data based on patterns, keywords, and regulatory rules. For example, “If Column B contains email addresses, classify the row as Confidential.” “If Column F includes a 16-digit number, label it as Highly Confidential.” This removes human error and allows businesses to scale classification efficiently.
How Data Classification Works
The data classification follows three key steps:
1. Data Discovery
Identify where all business data is stored (spreadsheets, databases, cloud storage, emails, etc.). Scan for sensitive content using AI-driven tools to detect and tag key information like personal data, financial records, or trade secrets.
2. Categorization and Labeling
Assign classification levels (Public, Internal, Confidential, Highly Confidential). Apply labels and metadata to ensure policies are enforced consistently across files, documents, and datasets.
3. Policy Enforcement
Implement role-based access control (RBAC) so only authorized employees can view or modify classified data. Encrypt highly sensitive information and apply real-time monitoring to detect unauthorized access or modifications.
Common Data Classification Methods
Organizations use different approaches to classify data depending on their industry, security needs, and compliance requirements:
1. Content-Based Classification Definition
Examining the content of files or data records to classify them based on sensitivity. Example: Detecting credit card numbers or customer names inside a spreadsheet and automatically applying a Confidential label. Best for financial institutions, eCommerce, and customer service teams.
2. Context-Based Classification Definition
Classifying data based on metadata, source, or usage patterns. For example, a document uploaded to an HR database may automatically be labeled Internal Use Only, while a financial report stored in an executive folder may be classified as Highly Confidential. This method is best for Enterprise businesses with large datasets.
3. User-Driven Classification Definition
Employees manually tag files or documents as they create and handle them. Example: A project manager marks an internal roadmap as Confidential before sharing it with leadership. Best for creative teams, content development, and marketing.
4. Automated Classification with AI (The Smartest Approach) Definition
AI tools like Numerous scan, detect, and classify data automatically based on patterns and predefined rules. Example: “If Column A contains customer names and Column B has phone numbers, classify as Confidential.” “If a document includes ‘contract terms’ or ‘invoice,’ tag as Highly Confidential.” Best for: Organizations that handle large volumes of structured and unstructured data.
Why Spreadsheets and Business Data Need Automated Classification
Many businesses store and analyze sensitive commercial data in spreadsheets, which makes classification even more critical.
Challenges with Unclassified Spreadsheets Unrestricted access → Anyone can view or edit sensitive records.
Version control issues → Employees may copy, email, or modify critical files without tracking.
Data leaks → Without classification, teams might share documents externally by mistake.
How Numerous Solves Spreadsheet Classification Challenges.
Automates classification directly in Google Sheets and Excel.
Scans entire datasets instantly to apply security tags.
Reduces human error and ensures compliance across all departments.
Example prompt in Numerous
“If spreadsheet contains the words ‘client contract’ or ‘financial forecast,’ label as Confidential.” By integrating AI-driven classification into daily workflows, organizations can protect sensitive data effortlessly without slowing down operations.
Related Reading
• Why Data Classification Is Important
• Data Classification Scheme
• Sensitive Data Classification
• Data Classification Standards
• Confidential Data Classification
• How to Do Data Classification
• Data Classification Process
Top 4 Levels of Data Classification Every Organization Should Use
1. Understanding Public Data and Its Risks
Public data is information that is intended for unrestricted access. This type of data poses no risk if shared externally.
When to Use It
Content that has been approved for external publication.
Data that does not contain proprietary, confidential, or personal information.
Examples of Public Data
Published blog posts and marketing materials.
Company press releases.
Social media posts.
Job postings.
Public financial reports for investors.
Security Considerations
No encryption or access control is required.
Version control should be maintained to prevent outdated information from circulating.
Public data should still be reviewed for accuracy before release.
How Numerous Helps Automate Public Data Classification
Businesses can set up Numerous tags to tag all spreadsheet rows marked as “Final” or “Published” as Public in content tracking sheets.
Example prompt: “If Column B contains the word ‘published’ and Column C includes ‘blog post,’ classify as Public.”
2. Internal Use Only Data: What It Is and Why It Matters
Internal data is information that should remain within the organization but does not contain highly sensitive details. It would not cause significant harm if exposed externally, but it should still be restricted.
When to Use It
Information that should not be publicly available but does not pose a significant risk if accessed by internal employees.
Content that is still under review or in development.
Examples of Internal Use Data
Employee training manuals. Internal company policies that do not contain sensitive information.
Project management files and work-in-progress documents.
Draft marketing and PR materials.
Team performance analytics and general business reports.
Security Considerations
Should be stored on internal servers and not shared externally.
Implement basic access controls (i.e., restrict access to employees only).
Version control should be strictly enforced to avoid leaks of unfinished content.
How Numerous Helps Automate Internal Use Classification
Example prompt: “If Column A contains ‘draft’, ‘internal’, or ‘for internal use only’, classify as Internal Use.”
This helps prevent unfinished business documents from being mistakenly sent outside the organization.
3. The Lowdown on Confidential Data
Confidential data includes sensitive business, employee, or customer information restricted to authorized personnel only. If exposed, this data could cause the organization financial, reputational, or operational harm.
When to Use It
Data that could negatively impact business operations if disclosed.
Information that contains personally identifiable details or proprietary insights.
Examples of Confidential Data
Customer names, emails, and phone numbers.
Financial reports and revenue projections.
Business strategies and marketing campaign details.
Contracts, vendor agreements, and invoices.
Employee salary, HR records, and performance evaluations.
Security Considerations
Encrypt confidential data in storage and transit to prevent unauthorized access.
Restrict access to specific departments or employees based on role.
Implement audit logs to track data access and modifications.
Mask or redact sensitive information before sharing externally.
How Numerous Helps Automate Confidential Data Classification
Example prompt: “If Column B contains an email address or Column F includes ‘contract’ or ‘agreement’, classify as Confidential.”
This ensures that private business agreements and customer data are automatically protected.
4. Highly Confidential Data: The Most Sensitive Type
Highly confidential data is the most sensitive type of information within an organization. Exposure could result in legal penalties, revenue loss, or severe reputational damage.
When to Use It
Data that must remain private due to regulatory, legal, or strategic concerns.
Information that can only be accessed by select personnel, such as executives, legal teams, or security officers.
Examples of Highly Confidential Data
Intellectual property, such as trade secrets, source code, and patents.
Customer payment details, including credit card numbers and banking information.
Legal case files and regulatory compliance records.
Merger and acquisition strategy documents.
Personally identifiable information (PII), such as Social Security numbers.
Security Considerations
Encrypt at rest and in transit to ensure that data remains protected even if stolen.
Use multi-factor authentication (MFA) to restrict access to authorized personnel only.
Store in highly secure environments with limited network access or zero-trust security models. Apply strict role-based access controls (RBAC) so that only the necessary employees can access. Monitor file access and enforce usage tracking.
How Numerous Helps Automate Highly Confidential Data Classification
Example prompt: “If Column A contains ‘bank details’ or ‘SSN’, classify as Highly Confidential.”
This ensures that spreadsheets containing highly sensitive financial or personal data are adequately tagged and secured.
Related Reading
• Data Classification Types
• Data Classification Examples
• Commercial Data Classification Levels
• HIPAA Data Classification
• Data Classification PII
• GDPR Data Classification
• Data Classification Framework
• Data Classification Benefits
Additional Data Classification Levels Used in Some Organizations
Understanding Restricted or Classified Data
Restricted or classified data is sensitive information protected by legal or national security regulations. Unauthorized access to restricted data can lead to severe legal or financial repercussions. Smooth access to sensitive data can also threaten individual safety or national security. Organizations such as government agencies, defense contractors, and critical infrastructure sectors typically handle classified data. Examples of restricted data include military strategy documents, national security intelligence reports, and classified communications. Access to restricted data requires high-level security clearance, robust encryption, and strict role-based access controls.
Numerous automated systems detect and classify restricted data by scanning sensitive documents for predefined keywords such as “confidential” or “national security.” For instance, if a document contains “classified” or “confidential briefing,” the system automatically labels it as restricted.
Personally Identifiable Information (PII) and Protected Health Information (PHI)
PII refers to any data that can be used to identify an individual, while PHI includes medical and health-related data protected under laws such as HIPAA. Exposure of PII or PHI can lead to identity theft, fraud, and regulatory fines. Organizations that handle PII and PHI include healthcare organizations, financial institutions, retail and eCommerce platforms, and legal firms.
Examples of PII and PHI include full names, addresses, credit card details, patient health records, and insurance policy numbers. Security measures for PII and PHI include encryption, data masking, strict access limitations, and regulatory compliance. Failure to protect PII or PHI can result in severe legal and financial penalties. Numerous tools help automate the classification of PII and PHI by using AI to detect personal information such as names, birthdates, Social Security numbers, and medical terms. For example, if a spreadsheet contains full names and phone numbers, the system will classify it as PII.
Proprietary Data and Intellectual Property (IP)
Proprietary or intellectual property data refers to business-critical information that provides a competitive advantage or is protected under patents, copyrights, or trade secret laws. Theft or unauthorized disclosure of IP can result in significant financial losses and legal disputes. Organizations dealing with proprietary data include technology, pharmaceutical, manufacturing, engineering, and media and entertainment companies. Examples of proprietary data include software source code, product blueprints, R&D reports, and confidential production plans. IP security measures include strict access controls, non-disclosure agreements, data leakage prevention tools, and legal protections.
Numerous helps automate classification of proprietary data by identifying and classifying keywords related to patents, source code, and confidential projects. For instance, if a document contains “patent pending” or “proprietary algorithm,” the system will classify it as intellectual property.
Common Challenges in Data Classification (And How to Overcome Them)
Inconsistent Classification Across Teams
Data classification levels reduce risk by helping organizations consistently identify, manage, and protect sensitive data. However, different teams within an organization may define classification levels differently. For example, the marketing team might label a customer contact list as "Public," while the legal team considers it "Confidential." Inconsistent labeling leads to confusion, improper data handling, and security gaps.
Manual Classification Is Time-Consuming and Prone to Errors
Classifying data manually is like trying to find a needle in a haystack. It requires employees to review and tag files individually, which is slow and inefficient. When faced with mounting pressure to find and analyze sensitive data, employees often skip classification or apply the incorrect labels to save time. Large organizations generate thousands of new files, emails, and spreadsheets daily, making manual classification impractical.
Data Is Constantly Growing and Changing
Businesses collect vast amounts of data daily, making it hard to classify everything in real-time. Files and spreadsheets are frequently updated, copied, and shared, leading to classification inconsistencies. Employees rarely reclassify old documents, meaning outdated classifications remain in place even when data becomes more or less sensitive.
Employees Do Not Know How to Identify Sensitive Data
Many employees struggle to recognize what qualifies as sensitive data (e.g., contract terms, personal information, internal pricing). Different roles in a company have different understandings of security risks. As a result, employees accidentally share restricted documents without realizing the consequences.
Keeping Up with Changing Compliance Regulations
GDPR, HIPAA, CCPA, and PCI-DSS constantly update data protection requirements. Many businesses fail to adjust their classification policies to stay compliant. Regulatory audits can expose classification gaps, leading to fines and legal actions.
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
Numerous is a data classification tool that helps businesses make decisions at scale. This AI-powered tool helps content marketers, Ecommerce businesses, and beyond categorize data, generate content, and even write blog posts. Numerous is incredibly easy to use. Users simply type a prompt into a Google Sheets or Microsoft Excel spreadsheet, and the AI returns the data classification results in seconds. For instance, to categorize products based on customer reviews, type a prompt like “Organize these customer reviews into positive, negative, and neutral categories.”
Numerous will return any spreadsheet function, simple or complex, within seconds. The capabilities of Numerous are endless, and this versatile tool can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI, in both Google Sheet and Microsoft Excel. Use Numerous AI spreadsheet AI tool to make decisions and complete tasks at scale.
Related Reading
• Data Classification Tools
• Data Classification Best Practices
• Data Classification and Data Loss Prevention
• Automated Data Classification Tools
• Data Classification Methods
• Automated Data Classification
• Imbalanced Data Classification
• Data Classification Matrix
Consider your business has a data breach, and you don't know which files were accessed. You panic and call your IT department. They get to work and discover that the hackers accessed your customer database containing personally identifiable information (PII). They also found that the files were unencrypted and the information was outdated. Not only would you breathe a sigh of relief at the news that it wasn't an active project leaked, but you'd also be embarrassed at the poor security practices that led to the breach. What if I told you you could have avoided this scenario with proper data classification? By classifying customer data according to its sensitivity, you could have ensured that files like these were encrypted and even isolated from your current operations to prevent any breach impact on your organization.
AI data classification levels help organizations secure their data against unauthorized access and help businesses comply with various privacy regulations, such as HIPAA and the GDPR. Let’s explore the top four data classification levels every organization should use so that you can start securing your data today. With the right tools, creating a data classification structure can be simple. Numerous spreadsheet AI tool can help you get started by scanning your spreadsheets for sensitive information, automatically classifying data, and applying your organization’s policies quickly.
Table Of Contents
Top 4 Levels of Data Classification Every Organization Should Use
Additional Data Classification Levels Used in Some Organizations
Common Challenges in Data Classification (And How to Overcome Them)
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
What is Data Classification?
Data classification is the systematic process of organizing and labeling data based on its level of sensitivity, business importance, and regulatory requirements. This process ensures that appropriate security measures, access controls, and handling policies are applied to different data types. Instead of treating all data the same, classification allows organizations to:
Identify which information is critical or sensitive.
Apply the proper security controls and compliance policies.
Automate workflows to improve efficiency.
Prevent unauthorized access or accidental data leaks.
Data classification helps businesses, governments, and organizations structure their data intelligently and securely.
The Importance of Classification Levels in Data Security
Organizations face significant risks such as data breaches, non-compliance fines, and inefficient data management without a proper classification system. Below are the key reasons why data classification is essential:
Enhancing Data Security
Organizations store vast amounts of information, from customer records to financial statements. Not all of it needs the same level of protection. For example, a company’s internal policy documents may be less sensitive than customer credit card details, which require encryption and strict access control. Organizations can protect sensitive data by classifying data while allowing easy access to public or non-sensitive data.
Ensuring Regulatory Compliance
Governments and industry regulators impose strict rules on handling certain data types. Regulations like GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and PCI-DSS (Payment Card Industry Data Security Standard) require businesses to identify and protect sensitive data (e.g., personal identifiable information, health records, credit card data). Businesses risk violating these regulations without classification, leading to heavy fines and legal consequences.
Controlling Data Access and Preventing Insider Threats
Not all employees should have access to every piece of company information. For example, a marketing assistant shouldn’t have access to executive payroll data. Classification ensures that data is only accessible to the right people, reducing the risk of insider threats, human errors, and accidental leaks.
Improving Operational Efficiency
Without classification, employees spend excessive time searching for data, verifying access rights, or applying inconsistent security rules. By labeling and structuring data properly, businesses can:
Speed up decision-making with clearly identified, accessible information.
Reduce redundancy by avoiding duplication of storage of the same data.
Optimize storage costs by archiving or deleting low-priority data when necessary.
Enabling Smarter Automation and AI-Driven Protection
Manual classification is time-consuming and inconsistent. AI-powered tools like Numerous can automatically detect and classify sensitive data based on patterns, keywords, and regulatory rules. For example, “If Column B contains email addresses, classify the row as Confidential.” “If Column F includes a 16-digit number, label it as Highly Confidential.” This removes human error and allows businesses to scale classification efficiently.
How Data Classification Works
The data classification follows three key steps:
1. Data Discovery
Identify where all business data is stored (spreadsheets, databases, cloud storage, emails, etc.). Scan for sensitive content using AI-driven tools to detect and tag key information like personal data, financial records, or trade secrets.
2. Categorization and Labeling
Assign classification levels (Public, Internal, Confidential, Highly Confidential). Apply labels and metadata to ensure policies are enforced consistently across files, documents, and datasets.
3. Policy Enforcement
Implement role-based access control (RBAC) so only authorized employees can view or modify classified data. Encrypt highly sensitive information and apply real-time monitoring to detect unauthorized access or modifications.
Common Data Classification Methods
Organizations use different approaches to classify data depending on their industry, security needs, and compliance requirements:
1. Content-Based Classification Definition
Examining the content of files or data records to classify them based on sensitivity. Example: Detecting credit card numbers or customer names inside a spreadsheet and automatically applying a Confidential label. Best for financial institutions, eCommerce, and customer service teams.
2. Context-Based Classification Definition
Classifying data based on metadata, source, or usage patterns. For example, a document uploaded to an HR database may automatically be labeled Internal Use Only, while a financial report stored in an executive folder may be classified as Highly Confidential. This method is best for Enterprise businesses with large datasets.
3. User-Driven Classification Definition
Employees manually tag files or documents as they create and handle them. Example: A project manager marks an internal roadmap as Confidential before sharing it with leadership. Best for creative teams, content development, and marketing.
4. Automated Classification with AI (The Smartest Approach) Definition
AI tools like Numerous scan, detect, and classify data automatically based on patterns and predefined rules. Example: “If Column A contains customer names and Column B has phone numbers, classify as Confidential.” “If a document includes ‘contract terms’ or ‘invoice,’ tag as Highly Confidential.” Best for: Organizations that handle large volumes of structured and unstructured data.
Why Spreadsheets and Business Data Need Automated Classification
Many businesses store and analyze sensitive commercial data in spreadsheets, which makes classification even more critical.
Challenges with Unclassified Spreadsheets Unrestricted access → Anyone can view or edit sensitive records.
Version control issues → Employees may copy, email, or modify critical files without tracking.
Data leaks → Without classification, teams might share documents externally by mistake.
How Numerous Solves Spreadsheet Classification Challenges.
Automates classification directly in Google Sheets and Excel.
Scans entire datasets instantly to apply security tags.
Reduces human error and ensures compliance across all departments.
Example prompt in Numerous
“If spreadsheet contains the words ‘client contract’ or ‘financial forecast,’ label as Confidential.” By integrating AI-driven classification into daily workflows, organizations can protect sensitive data effortlessly without slowing down operations.
Related Reading
• Why Data Classification Is Important
• Data Classification Scheme
• Sensitive Data Classification
• Data Classification Standards
• Confidential Data Classification
• How to Do Data Classification
• Data Classification Process
Top 4 Levels of Data Classification Every Organization Should Use
1. Understanding Public Data and Its Risks
Public data is information that is intended for unrestricted access. This type of data poses no risk if shared externally.
When to Use It
Content that has been approved for external publication.
Data that does not contain proprietary, confidential, or personal information.
Examples of Public Data
Published blog posts and marketing materials.
Company press releases.
Social media posts.
Job postings.
Public financial reports for investors.
Security Considerations
No encryption or access control is required.
Version control should be maintained to prevent outdated information from circulating.
Public data should still be reviewed for accuracy before release.
How Numerous Helps Automate Public Data Classification
Businesses can set up Numerous tags to tag all spreadsheet rows marked as “Final” or “Published” as Public in content tracking sheets.
Example prompt: “If Column B contains the word ‘published’ and Column C includes ‘blog post,’ classify as Public.”
2. Internal Use Only Data: What It Is and Why It Matters
Internal data is information that should remain within the organization but does not contain highly sensitive details. It would not cause significant harm if exposed externally, but it should still be restricted.
When to Use It
Information that should not be publicly available but does not pose a significant risk if accessed by internal employees.
Content that is still under review or in development.
Examples of Internal Use Data
Employee training manuals. Internal company policies that do not contain sensitive information.
Project management files and work-in-progress documents.
Draft marketing and PR materials.
Team performance analytics and general business reports.
Security Considerations
Should be stored on internal servers and not shared externally.
Implement basic access controls (i.e., restrict access to employees only).
Version control should be strictly enforced to avoid leaks of unfinished content.
How Numerous Helps Automate Internal Use Classification
Example prompt: “If Column A contains ‘draft’, ‘internal’, or ‘for internal use only’, classify as Internal Use.”
This helps prevent unfinished business documents from being mistakenly sent outside the organization.
3. The Lowdown on Confidential Data
Confidential data includes sensitive business, employee, or customer information restricted to authorized personnel only. If exposed, this data could cause the organization financial, reputational, or operational harm.
When to Use It
Data that could negatively impact business operations if disclosed.
Information that contains personally identifiable details or proprietary insights.
Examples of Confidential Data
Customer names, emails, and phone numbers.
Financial reports and revenue projections.
Business strategies and marketing campaign details.
Contracts, vendor agreements, and invoices.
Employee salary, HR records, and performance evaluations.
Security Considerations
Encrypt confidential data in storage and transit to prevent unauthorized access.
Restrict access to specific departments or employees based on role.
Implement audit logs to track data access and modifications.
Mask or redact sensitive information before sharing externally.
How Numerous Helps Automate Confidential Data Classification
Example prompt: “If Column B contains an email address or Column F includes ‘contract’ or ‘agreement’, classify as Confidential.”
This ensures that private business agreements and customer data are automatically protected.
4. Highly Confidential Data: The Most Sensitive Type
Highly confidential data is the most sensitive type of information within an organization. Exposure could result in legal penalties, revenue loss, or severe reputational damage.
When to Use It
Data that must remain private due to regulatory, legal, or strategic concerns.
Information that can only be accessed by select personnel, such as executives, legal teams, or security officers.
Examples of Highly Confidential Data
Intellectual property, such as trade secrets, source code, and patents.
Customer payment details, including credit card numbers and banking information.
Legal case files and regulatory compliance records.
Merger and acquisition strategy documents.
Personally identifiable information (PII), such as Social Security numbers.
Security Considerations
Encrypt at rest and in transit to ensure that data remains protected even if stolen.
Use multi-factor authentication (MFA) to restrict access to authorized personnel only.
Store in highly secure environments with limited network access or zero-trust security models. Apply strict role-based access controls (RBAC) so that only the necessary employees can access. Monitor file access and enforce usage tracking.
How Numerous Helps Automate Highly Confidential Data Classification
Example prompt: “If Column A contains ‘bank details’ or ‘SSN’, classify as Highly Confidential.”
This ensures that spreadsheets containing highly sensitive financial or personal data are adequately tagged and secured.
Related Reading
• Data Classification Types
• Data Classification Examples
• Commercial Data Classification Levels
• HIPAA Data Classification
• Data Classification PII
• GDPR Data Classification
• Data Classification Framework
• Data Classification Benefits
Additional Data Classification Levels Used in Some Organizations
Understanding Restricted or Classified Data
Restricted or classified data is sensitive information protected by legal or national security regulations. Unauthorized access to restricted data can lead to severe legal or financial repercussions. Smooth access to sensitive data can also threaten individual safety or national security. Organizations such as government agencies, defense contractors, and critical infrastructure sectors typically handle classified data. Examples of restricted data include military strategy documents, national security intelligence reports, and classified communications. Access to restricted data requires high-level security clearance, robust encryption, and strict role-based access controls.
Numerous automated systems detect and classify restricted data by scanning sensitive documents for predefined keywords such as “confidential” or “national security.” For instance, if a document contains “classified” or “confidential briefing,” the system automatically labels it as restricted.
Personally Identifiable Information (PII) and Protected Health Information (PHI)
PII refers to any data that can be used to identify an individual, while PHI includes medical and health-related data protected under laws such as HIPAA. Exposure of PII or PHI can lead to identity theft, fraud, and regulatory fines. Organizations that handle PII and PHI include healthcare organizations, financial institutions, retail and eCommerce platforms, and legal firms.
Examples of PII and PHI include full names, addresses, credit card details, patient health records, and insurance policy numbers. Security measures for PII and PHI include encryption, data masking, strict access limitations, and regulatory compliance. Failure to protect PII or PHI can result in severe legal and financial penalties. Numerous tools help automate the classification of PII and PHI by using AI to detect personal information such as names, birthdates, Social Security numbers, and medical terms. For example, if a spreadsheet contains full names and phone numbers, the system will classify it as PII.
Proprietary Data and Intellectual Property (IP)
Proprietary or intellectual property data refers to business-critical information that provides a competitive advantage or is protected under patents, copyrights, or trade secret laws. Theft or unauthorized disclosure of IP can result in significant financial losses and legal disputes. Organizations dealing with proprietary data include technology, pharmaceutical, manufacturing, engineering, and media and entertainment companies. Examples of proprietary data include software source code, product blueprints, R&D reports, and confidential production plans. IP security measures include strict access controls, non-disclosure agreements, data leakage prevention tools, and legal protections.
Numerous helps automate classification of proprietary data by identifying and classifying keywords related to patents, source code, and confidential projects. For instance, if a document contains “patent pending” or “proprietary algorithm,” the system will classify it as intellectual property.
Common Challenges in Data Classification (And How to Overcome Them)
Inconsistent Classification Across Teams
Data classification levels reduce risk by helping organizations consistently identify, manage, and protect sensitive data. However, different teams within an organization may define classification levels differently. For example, the marketing team might label a customer contact list as "Public," while the legal team considers it "Confidential." Inconsistent labeling leads to confusion, improper data handling, and security gaps.
Manual Classification Is Time-Consuming and Prone to Errors
Classifying data manually is like trying to find a needle in a haystack. It requires employees to review and tag files individually, which is slow and inefficient. When faced with mounting pressure to find and analyze sensitive data, employees often skip classification or apply the incorrect labels to save time. Large organizations generate thousands of new files, emails, and spreadsheets daily, making manual classification impractical.
Data Is Constantly Growing and Changing
Businesses collect vast amounts of data daily, making it hard to classify everything in real-time. Files and spreadsheets are frequently updated, copied, and shared, leading to classification inconsistencies. Employees rarely reclassify old documents, meaning outdated classifications remain in place even when data becomes more or less sensitive.
Employees Do Not Know How to Identify Sensitive Data
Many employees struggle to recognize what qualifies as sensitive data (e.g., contract terms, personal information, internal pricing). Different roles in a company have different understandings of security risks. As a result, employees accidentally share restricted documents without realizing the consequences.
Keeping Up with Changing Compliance Regulations
GDPR, HIPAA, CCPA, and PCI-DSS constantly update data protection requirements. Many businesses fail to adjust their classification policies to stay compliant. Regulatory audits can expose classification gaps, leading to fines and legal actions.
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
Numerous is a data classification tool that helps businesses make decisions at scale. This AI-powered tool helps content marketers, Ecommerce businesses, and beyond categorize data, generate content, and even write blog posts. Numerous is incredibly easy to use. Users simply type a prompt into a Google Sheets or Microsoft Excel spreadsheet, and the AI returns the data classification results in seconds. For instance, to categorize products based on customer reviews, type a prompt like “Organize these customer reviews into positive, negative, and neutral categories.”
Numerous will return any spreadsheet function, simple or complex, within seconds. The capabilities of Numerous are endless, and this versatile tool can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI, in both Google Sheet and Microsoft Excel. Use Numerous AI spreadsheet AI tool to make decisions and complete tasks at scale.
Related Reading
• Data Classification Tools
• Data Classification Best Practices
• Data Classification and Data Loss Prevention
• Automated Data Classification Tools
• Data Classification Methods
• Automated Data Classification
• Imbalanced Data Classification
• Data Classification Matrix
Consider your business has a data breach, and you don't know which files were accessed. You panic and call your IT department. They get to work and discover that the hackers accessed your customer database containing personally identifiable information (PII). They also found that the files were unencrypted and the information was outdated. Not only would you breathe a sigh of relief at the news that it wasn't an active project leaked, but you'd also be embarrassed at the poor security practices that led to the breach. What if I told you you could have avoided this scenario with proper data classification? By classifying customer data according to its sensitivity, you could have ensured that files like these were encrypted and even isolated from your current operations to prevent any breach impact on your organization.
AI data classification levels help organizations secure their data against unauthorized access and help businesses comply with various privacy regulations, such as HIPAA and the GDPR. Let’s explore the top four data classification levels every organization should use so that you can start securing your data today. With the right tools, creating a data classification structure can be simple. Numerous spreadsheet AI tool can help you get started by scanning your spreadsheets for sensitive information, automatically classifying data, and applying your organization’s policies quickly.
Table Of Contents
Top 4 Levels of Data Classification Every Organization Should Use
Additional Data Classification Levels Used in Some Organizations
Common Challenges in Data Classification (And How to Overcome Them)
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
What is Data Classification?
Data classification is the systematic process of organizing and labeling data based on its level of sensitivity, business importance, and regulatory requirements. This process ensures that appropriate security measures, access controls, and handling policies are applied to different data types. Instead of treating all data the same, classification allows organizations to:
Identify which information is critical or sensitive.
Apply the proper security controls and compliance policies.
Automate workflows to improve efficiency.
Prevent unauthorized access or accidental data leaks.
Data classification helps businesses, governments, and organizations structure their data intelligently and securely.
The Importance of Classification Levels in Data Security
Organizations face significant risks such as data breaches, non-compliance fines, and inefficient data management without a proper classification system. Below are the key reasons why data classification is essential:
Enhancing Data Security
Organizations store vast amounts of information, from customer records to financial statements. Not all of it needs the same level of protection. For example, a company’s internal policy documents may be less sensitive than customer credit card details, which require encryption and strict access control. Organizations can protect sensitive data by classifying data while allowing easy access to public or non-sensitive data.
Ensuring Regulatory Compliance
Governments and industry regulators impose strict rules on handling certain data types. Regulations like GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and PCI-DSS (Payment Card Industry Data Security Standard) require businesses to identify and protect sensitive data (e.g., personal identifiable information, health records, credit card data). Businesses risk violating these regulations without classification, leading to heavy fines and legal consequences.
Controlling Data Access and Preventing Insider Threats
Not all employees should have access to every piece of company information. For example, a marketing assistant shouldn’t have access to executive payroll data. Classification ensures that data is only accessible to the right people, reducing the risk of insider threats, human errors, and accidental leaks.
Improving Operational Efficiency
Without classification, employees spend excessive time searching for data, verifying access rights, or applying inconsistent security rules. By labeling and structuring data properly, businesses can:
Speed up decision-making with clearly identified, accessible information.
Reduce redundancy by avoiding duplication of storage of the same data.
Optimize storage costs by archiving or deleting low-priority data when necessary.
Enabling Smarter Automation and AI-Driven Protection
Manual classification is time-consuming and inconsistent. AI-powered tools like Numerous can automatically detect and classify sensitive data based on patterns, keywords, and regulatory rules. For example, “If Column B contains email addresses, classify the row as Confidential.” “If Column F includes a 16-digit number, label it as Highly Confidential.” This removes human error and allows businesses to scale classification efficiently.
How Data Classification Works
The data classification follows three key steps:
1. Data Discovery
Identify where all business data is stored (spreadsheets, databases, cloud storage, emails, etc.). Scan for sensitive content using AI-driven tools to detect and tag key information like personal data, financial records, or trade secrets.
2. Categorization and Labeling
Assign classification levels (Public, Internal, Confidential, Highly Confidential). Apply labels and metadata to ensure policies are enforced consistently across files, documents, and datasets.
3. Policy Enforcement
Implement role-based access control (RBAC) so only authorized employees can view or modify classified data. Encrypt highly sensitive information and apply real-time monitoring to detect unauthorized access or modifications.
Common Data Classification Methods
Organizations use different approaches to classify data depending on their industry, security needs, and compliance requirements:
1. Content-Based Classification Definition
Examining the content of files or data records to classify them based on sensitivity. Example: Detecting credit card numbers or customer names inside a spreadsheet and automatically applying a Confidential label. Best for financial institutions, eCommerce, and customer service teams.
2. Context-Based Classification Definition
Classifying data based on metadata, source, or usage patterns. For example, a document uploaded to an HR database may automatically be labeled Internal Use Only, while a financial report stored in an executive folder may be classified as Highly Confidential. This method is best for Enterprise businesses with large datasets.
3. User-Driven Classification Definition
Employees manually tag files or documents as they create and handle them. Example: A project manager marks an internal roadmap as Confidential before sharing it with leadership. Best for creative teams, content development, and marketing.
4. Automated Classification with AI (The Smartest Approach) Definition
AI tools like Numerous scan, detect, and classify data automatically based on patterns and predefined rules. Example: “If Column A contains customer names and Column B has phone numbers, classify as Confidential.” “If a document includes ‘contract terms’ or ‘invoice,’ tag as Highly Confidential.” Best for: Organizations that handle large volumes of structured and unstructured data.
Why Spreadsheets and Business Data Need Automated Classification
Many businesses store and analyze sensitive commercial data in spreadsheets, which makes classification even more critical.
Challenges with Unclassified Spreadsheets Unrestricted access → Anyone can view or edit sensitive records.
Version control issues → Employees may copy, email, or modify critical files without tracking.
Data leaks → Without classification, teams might share documents externally by mistake.
How Numerous Solves Spreadsheet Classification Challenges.
Automates classification directly in Google Sheets and Excel.
Scans entire datasets instantly to apply security tags.
Reduces human error and ensures compliance across all departments.
Example prompt in Numerous
“If spreadsheet contains the words ‘client contract’ or ‘financial forecast,’ label as Confidential.” By integrating AI-driven classification into daily workflows, organizations can protect sensitive data effortlessly without slowing down operations.
Related Reading
• Why Data Classification Is Important
• Data Classification Scheme
• Sensitive Data Classification
• Data Classification Standards
• Confidential Data Classification
• How to Do Data Classification
• Data Classification Process
Top 4 Levels of Data Classification Every Organization Should Use
1. Understanding Public Data and Its Risks
Public data is information that is intended for unrestricted access. This type of data poses no risk if shared externally.
When to Use It
Content that has been approved for external publication.
Data that does not contain proprietary, confidential, or personal information.
Examples of Public Data
Published blog posts and marketing materials.
Company press releases.
Social media posts.
Job postings.
Public financial reports for investors.
Security Considerations
No encryption or access control is required.
Version control should be maintained to prevent outdated information from circulating.
Public data should still be reviewed for accuracy before release.
How Numerous Helps Automate Public Data Classification
Businesses can set up Numerous tags to tag all spreadsheet rows marked as “Final” or “Published” as Public in content tracking sheets.
Example prompt: “If Column B contains the word ‘published’ and Column C includes ‘blog post,’ classify as Public.”
2. Internal Use Only Data: What It Is and Why It Matters
Internal data is information that should remain within the organization but does not contain highly sensitive details. It would not cause significant harm if exposed externally, but it should still be restricted.
When to Use It
Information that should not be publicly available but does not pose a significant risk if accessed by internal employees.
Content that is still under review or in development.
Examples of Internal Use Data
Employee training manuals. Internal company policies that do not contain sensitive information.
Project management files and work-in-progress documents.
Draft marketing and PR materials.
Team performance analytics and general business reports.
Security Considerations
Should be stored on internal servers and not shared externally.
Implement basic access controls (i.e., restrict access to employees only).
Version control should be strictly enforced to avoid leaks of unfinished content.
How Numerous Helps Automate Internal Use Classification
Example prompt: “If Column A contains ‘draft’, ‘internal’, or ‘for internal use only’, classify as Internal Use.”
This helps prevent unfinished business documents from being mistakenly sent outside the organization.
3. The Lowdown on Confidential Data
Confidential data includes sensitive business, employee, or customer information restricted to authorized personnel only. If exposed, this data could cause the organization financial, reputational, or operational harm.
When to Use It
Data that could negatively impact business operations if disclosed.
Information that contains personally identifiable details or proprietary insights.
Examples of Confidential Data
Customer names, emails, and phone numbers.
Financial reports and revenue projections.
Business strategies and marketing campaign details.
Contracts, vendor agreements, and invoices.
Employee salary, HR records, and performance evaluations.
Security Considerations
Encrypt confidential data in storage and transit to prevent unauthorized access.
Restrict access to specific departments or employees based on role.
Implement audit logs to track data access and modifications.
Mask or redact sensitive information before sharing externally.
How Numerous Helps Automate Confidential Data Classification
Example prompt: “If Column B contains an email address or Column F includes ‘contract’ or ‘agreement’, classify as Confidential.”
This ensures that private business agreements and customer data are automatically protected.
4. Highly Confidential Data: The Most Sensitive Type
Highly confidential data is the most sensitive type of information within an organization. Exposure could result in legal penalties, revenue loss, or severe reputational damage.
When to Use It
Data that must remain private due to regulatory, legal, or strategic concerns.
Information that can only be accessed by select personnel, such as executives, legal teams, or security officers.
Examples of Highly Confidential Data
Intellectual property, such as trade secrets, source code, and patents.
Customer payment details, including credit card numbers and banking information.
Legal case files and regulatory compliance records.
Merger and acquisition strategy documents.
Personally identifiable information (PII), such as Social Security numbers.
Security Considerations
Encrypt at rest and in transit to ensure that data remains protected even if stolen.
Use multi-factor authentication (MFA) to restrict access to authorized personnel only.
Store in highly secure environments with limited network access or zero-trust security models. Apply strict role-based access controls (RBAC) so that only the necessary employees can access. Monitor file access and enforce usage tracking.
How Numerous Helps Automate Highly Confidential Data Classification
Example prompt: “If Column A contains ‘bank details’ or ‘SSN’, classify as Highly Confidential.”
This ensures that spreadsheets containing highly sensitive financial or personal data are adequately tagged and secured.
Related Reading
• Data Classification Types
• Data Classification Examples
• Commercial Data Classification Levels
• HIPAA Data Classification
• Data Classification PII
• GDPR Data Classification
• Data Classification Framework
• Data Classification Benefits
Additional Data Classification Levels Used in Some Organizations
Understanding Restricted or Classified Data
Restricted or classified data is sensitive information protected by legal or national security regulations. Unauthorized access to restricted data can lead to severe legal or financial repercussions. Smooth access to sensitive data can also threaten individual safety or national security. Organizations such as government agencies, defense contractors, and critical infrastructure sectors typically handle classified data. Examples of restricted data include military strategy documents, national security intelligence reports, and classified communications. Access to restricted data requires high-level security clearance, robust encryption, and strict role-based access controls.
Numerous automated systems detect and classify restricted data by scanning sensitive documents for predefined keywords such as “confidential” or “national security.” For instance, if a document contains “classified” or “confidential briefing,” the system automatically labels it as restricted.
Personally Identifiable Information (PII) and Protected Health Information (PHI)
PII refers to any data that can be used to identify an individual, while PHI includes medical and health-related data protected under laws such as HIPAA. Exposure of PII or PHI can lead to identity theft, fraud, and regulatory fines. Organizations that handle PII and PHI include healthcare organizations, financial institutions, retail and eCommerce platforms, and legal firms.
Examples of PII and PHI include full names, addresses, credit card details, patient health records, and insurance policy numbers. Security measures for PII and PHI include encryption, data masking, strict access limitations, and regulatory compliance. Failure to protect PII or PHI can result in severe legal and financial penalties. Numerous tools help automate the classification of PII and PHI by using AI to detect personal information such as names, birthdates, Social Security numbers, and medical terms. For example, if a spreadsheet contains full names and phone numbers, the system will classify it as PII.
Proprietary Data and Intellectual Property (IP)
Proprietary or intellectual property data refers to business-critical information that provides a competitive advantage or is protected under patents, copyrights, or trade secret laws. Theft or unauthorized disclosure of IP can result in significant financial losses and legal disputes. Organizations dealing with proprietary data include technology, pharmaceutical, manufacturing, engineering, and media and entertainment companies. Examples of proprietary data include software source code, product blueprints, R&D reports, and confidential production plans. IP security measures include strict access controls, non-disclosure agreements, data leakage prevention tools, and legal protections.
Numerous helps automate classification of proprietary data by identifying and classifying keywords related to patents, source code, and confidential projects. For instance, if a document contains “patent pending” or “proprietary algorithm,” the system will classify it as intellectual property.
Common Challenges in Data Classification (And How to Overcome Them)
Inconsistent Classification Across Teams
Data classification levels reduce risk by helping organizations consistently identify, manage, and protect sensitive data. However, different teams within an organization may define classification levels differently. For example, the marketing team might label a customer contact list as "Public," while the legal team considers it "Confidential." Inconsistent labeling leads to confusion, improper data handling, and security gaps.
Manual Classification Is Time-Consuming and Prone to Errors
Classifying data manually is like trying to find a needle in a haystack. It requires employees to review and tag files individually, which is slow and inefficient. When faced with mounting pressure to find and analyze sensitive data, employees often skip classification or apply the incorrect labels to save time. Large organizations generate thousands of new files, emails, and spreadsheets daily, making manual classification impractical.
Data Is Constantly Growing and Changing
Businesses collect vast amounts of data daily, making it hard to classify everything in real-time. Files and spreadsheets are frequently updated, copied, and shared, leading to classification inconsistencies. Employees rarely reclassify old documents, meaning outdated classifications remain in place even when data becomes more or less sensitive.
Employees Do Not Know How to Identify Sensitive Data
Many employees struggle to recognize what qualifies as sensitive data (e.g., contract terms, personal information, internal pricing). Different roles in a company have different understandings of security risks. As a result, employees accidentally share restricted documents without realizing the consequences.
Keeping Up with Changing Compliance Regulations
GDPR, HIPAA, CCPA, and PCI-DSS constantly update data protection requirements. Many businesses fail to adjust their classification policies to stay compliant. Regulatory audits can expose classification gaps, leading to fines and legal actions.
Make Decisions At Scale Through AI With Numerous AI’s Spreadsheet AI Tool
Numerous is a data classification tool that helps businesses make decisions at scale. This AI-powered tool helps content marketers, Ecommerce businesses, and beyond categorize data, generate content, and even write blog posts. Numerous is incredibly easy to use. Users simply type a prompt into a Google Sheets or Microsoft Excel spreadsheet, and the AI returns the data classification results in seconds. For instance, to categorize products based on customer reviews, type a prompt like “Organize these customer reviews into positive, negative, and neutral categories.”
Numerous will return any spreadsheet function, simple or complex, within seconds. The capabilities of Numerous are endless, and this versatile tool can be used with Microsoft Excel and Google Sheets. Get started today with Numerous.ai so that you can make business decisions at scale using AI, in both Google Sheet and Microsoft Excel. Use Numerous AI spreadsheet AI tool to make decisions and complete tasks at scale.
Related Reading
• Data Classification Tools
• Data Classification Best Practices
• Data Classification and Data Loss Prevention
• Automated Data Classification Tools
• Data Classification Methods
• Automated Data Classification
• Imbalanced Data Classification
• Data Classification Matrix
© 2025 Numerous. All rights reserved.
© 2025 Numerous. All rights reserved.
© 2025 Numerous. All rights reserved.