Blog
September 5, 2024

A Comprehensive Guide to Data Classification in Cyber Security

Protect sensitive business information with effective data classification. Learn how to organise and tag data based on its sensitivity and importance, apply appropriate security measures, and avoid data breaches.

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Key points

  • Data classification is essential for protecting sensitive business information.
  • Misclassification or lack of classification can lead to data breaches and compliance issues.
  • AI and automation help tackle the challenges of complex and large-scale data classification.
  • Metomic automates data discovery, provides custom classification and alerts, helps businesses protect sensitive data, and maintain compliance across digital platforms.

Data is one of the most valuable assets a business has—and one of the most vulnerable. That’s why proper data classification is so important, especially when it comes to cyber security.

By organising and tagging data based on its sensitivity and importance, businesses can apply the right security measures to keep their information safe.

Classifying data properly is essential for protecting sensitive information, avoiding data leaks, and staying compliant with regulations (particularly for finance and healthcare organisations).

This guide is here to help you navigate the world of data classification, offering you tips on how to make your organisation’s data more secure.

Whether it's financial records, personal details, or intellectual property, knowing how to handle and classify your data is key to keeping it secure.

What is data classification, in particular for cyber security?

Data classification involves organising data by its sensitivity and security needs, which helps in applying the right protections. This means labelling or tagging data into categories like public, internal-only, confidential, or restricted based on how sensitive the information is.

According to research by the Identity Theft Resource Center, there were 3,205 data compromises in the US in 2023, impacting over 353 million people - a staggering 72% increase over the previous year.

Whether it was a breach, leak, or accidental exposure, the end result was the same—sensitive data falling into the wrong hands.

Proper data classification can help reduce these incidents by ensuring the most critical information gets the protection it needs from unauthorised access.

What might data classification look like?

Proper classification ensures that sensitive information receives the appropriate level of protection, reducing the risk of unauthorised access. For example, public data might need minimal protection, while restricted data, such as personal health records or financial information, requires stricter security controls.

What does this look like in the real-world? In healthcare, incorrect classification could lead to patient privacy breaches, while in finance, misclassifying credit information could expose sensitive financial data to risks.

Effective data classification is crucial for maintaining security and compliance across various industries.

Why is it important for businesses? What are the benefits?

Data classification is crucial for strengthening your organisation’s security. By properly categorising your data based on its sensitivity, you’re ensuring that the most critical information gets the right level of protection.

This not only helps with compliance—think GDPR and HIPAA —but also boosts data protection, improves access control, and makes resource allocation more efficient.

The benefits are clear when it comes to risk management. Consider this: 75% of public sector organisations that don’t classify their data upon creation take days to detect data misuse.

In comparison, 25% of those that do classify their data spot misuse within minutes.

That’s a huge difference in response time, which can be crucial when dealing with potential security threats.

In short, data classification helps you know where to focus your efforts, so you can better protect what matters most and make smarter decisions when risks arise.

What are the challenges with data classification?

Data classification plays a crucial role in safeguarding sensitive information, but it comes with its fair share of challenges.

Organisations often encounter the following issues:

1. Classifying unstructured data

Managing and classifying unstructured data, like emails and documents, is complex and time-consuming. This type of data is often not easily searchable or indexable, making it harder to classify accurately.

2. Keeping classifications up to date

As data grows and changes, maintaining accurate and current classifications can be a continuous struggle. Outdated classifications can lead to gaps in data protection.

3. Balancing security with accessibility

Striking the right balance between comprehensive security and convenient access to data is challenging. Overly strict controls can hinder productivity, while lax measures may expose sensitive information.

4. Manual processes

‍86% of businesses still rely on mostly or fully manual methods to identify and tag data, which can delay responses to data breaches and increase the likelihood of human error.

These challenges highlight the need for automated tools to streamline data classification, improve accuracy, and enhance scalability.

How does AI enhance data classification?

Artificial Intelligence (AI) is revolutionising data classification by making the process smarter and more efficient. Here’s how AI is transforming the landscape:

1. Automating classification

AI takes over the tedious task of classifying data, reducing the risk of human error and ensuring consistent tagging across the board. This automation speeds up the process and improves accuracy.

2. Handling large volumes of data

AI excels at processing vast amounts of data quickly. It can sift through enormous datasets, identifying patterns and anomalies that might be missed manually.

3. Refining classification rules

AI systems use machine learning to continually refine their classification rules based on new data. This means that as data evolves, the AI adapts, enhancing both the accuracy and relevance of the classifications.

4. Improving real-time monitoring

AI provides real-time insights into data security, enabling immediate response to potential breaches. It also handles unstructured data effectively, learning and improving its classification capabilities over time.

Despite these advancements, only 48% of organisations have started adopting intelligent automation.

This leaves many still reliant on manual processes, which can be prone to errors and delays.

What are the types of data classification?

Data classification can be approached in several ways, depending on what works best for your organisation:

  • Content-based classification: This method involves analysing the actual content of files to determine their classification. It’s a thorough way to ensure sensitive information is correctly tagged and protected.
  • Context-based classification: Instead of examining the content, this approach relies on metadata, such as who created the file, where it was created, or which application was used. It’s a quicker method while still capturing essential context.
  • User-based classification: Here, knowledgeable users manually classify the data. This is particularly useful in specialised fields where users understand the sensitivity of the information.
  • Sensitivity levels: Data is typically classified into high, medium, or low sensitivity. High-sensitivity data, like financial records or personal information, requires the most protection, while medium and low-sensitivity data need fewer controls.

Interestingly, 75% of companies that use more than three levels of classification —such as Public, Internal, and Confidential—are more likely to experience one or more data breaches. Clearly, there’s a classification tightrope between detailed and overly complex that needs walking.

How can data classification help prevent data leaks?

Data classification isn’t just about organising information; it’s a vital strategy for preventing data leaks and ensuring good data security.

1. Protecting sensitive information

Data classification plays a crucial role in safeguarding sensitive information and reducing the risk of data leaks. By clearly identifying and categorising your data, you ensure that the most critical information is protected and access is limited to only authorised users.

2. Managing access control

Proper classification allows businesses to manage access control more effectively. For instance, only certain team members might have access to highly sensitive data, while less critical information can be more widely accessible. This targeted approach reduces the chances of unauthorised users stumbling upon sensitive information.

3. Enforcing encryption policies

Classification helps enforce encryption policies. Data classified as highly sensitive can automatically trigger encryption protocols, ensuring that even if accessed unlawfully, it remains unreadable and secure.

4. Ensuring regulatory compliance

Finally, data classification is essential for regulatory compliance. By aligning your data management practices with privacy laws and industry standards, you can avoid hefty fines and reputational damage that often accompany data breaches.

In essence, data classification acts as a first line of defence in a comprehensive data security strategy, helping to prevent costly leaks and breaches before they happen.

How can Metomic help?

Metomic makes data classification easier by tackling common challenges with smart, automated tools. It helps businesses quickly find and label sensitive information in real-time, making data discovery and compliance much simpler.

Key features include:

  • Automatic discovery and classification: Metomic instantly identifies and classifies sensitive data across SaaS and GenAI platforms, including personal, financial, and health data.
  • Custom classification: You can create custom classifiers to match your organisation’s specific needs.
  • Comprehensive scanning: Metomic scans a wide range of files, from documents to spreadsheets, across both public and private channels.
  • Alerts and remediation: Detailed alerts show exactly where sensitive data is, who shared it, and whether rules were broken—plus, it automates redaction to protect your data.

With easy integration, scalability, and AI-driven insights, Metomic helps businesses stay on top of data security and compliance without the hassle.

Getting started with Metomic

Free risk assessment scans

Kick things off with a free risk assessment scan to uncover potential data risks across platforms like Slack, ChatGPT, and Google Drive. It’s a simple way to get a clear picture of your organisation’s data security and spot any weak points.

Book a personalised demo

Ready to dive deeper? Book a personalised demo with one of our security experts. We’ll walk you through how Metomic’s tools can help you classify and protect your data in real time, and how we can tailor everything to fit your organisation's needs perfectly.

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Table of contents
Table of contents
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Table of contents

Ben van Enckevort

CTO and Co-Founder

Ben van Enckevort is the co-founder and CTO of Metomic

Key points

  • Data classification is essential for protecting sensitive business information.
  • Misclassification or lack of classification can lead to data breaches and compliance issues.
  • AI and automation help tackle the challenges of complex and large-scale data classification.
  • Metomic automates data discovery, provides custom classification and alerts, helps businesses protect sensitive data, and maintain compliance across digital platforms.

Data is one of the most valuable assets a business has—and one of the most vulnerable. That’s why proper data classification is so important, especially when it comes to cyber security.

By organising and tagging data based on its sensitivity and importance, businesses can apply the right security measures to keep their information safe.

Classifying data properly is essential for protecting sensitive information, avoiding data leaks, and staying compliant with regulations (particularly for finance and healthcare organisations).

This guide is here to help you navigate the world of data classification, offering you tips on how to make your organisation’s data more secure.

Whether it's financial records, personal details, or intellectual property, knowing how to handle and classify your data is key to keeping it secure.

What is data classification, in particular for cyber security?

Data classification involves organising data by its sensitivity and security needs, which helps in applying the right protections. This means labelling or tagging data into categories like public, internal-only, confidential, or restricted based on how sensitive the information is.

According to research by the Identity Theft Resource Center, there were 3,205 data compromises in the US in 2023, impacting over 353 million people - a staggering 72% increase over the previous year.

Whether it was a breach, leak, or accidental exposure, the end result was the same—sensitive data falling into the wrong hands.

Proper data classification can help reduce these incidents by ensuring the most critical information gets the protection it needs from unauthorised access.

What might data classification look like?

Proper classification ensures that sensitive information receives the appropriate level of protection, reducing the risk of unauthorised access. For example, public data might need minimal protection, while restricted data, such as personal health records or financial information, requires stricter security controls.

What does this look like in the real-world? In healthcare, incorrect classification could lead to patient privacy breaches, while in finance, misclassifying credit information could expose sensitive financial data to risks.

Effective data classification is crucial for maintaining security and compliance across various industries.

Why is it important for businesses? What are the benefits?

Data classification is crucial for strengthening your organisation’s security. By properly categorising your data based on its sensitivity, you’re ensuring that the most critical information gets the right level of protection.

This not only helps with compliance—think GDPR and HIPAA —but also boosts data protection, improves access control, and makes resource allocation more efficient.

The benefits are clear when it comes to risk management. Consider this: 75% of public sector organisations that don’t classify their data upon creation take days to detect data misuse.

In comparison, 25% of those that do classify their data spot misuse within minutes.

That’s a huge difference in response time, which can be crucial when dealing with potential security threats.

In short, data classification helps you know where to focus your efforts, so you can better protect what matters most and make smarter decisions when risks arise.

What are the challenges with data classification?

Data classification plays a crucial role in safeguarding sensitive information, but it comes with its fair share of challenges.

Organisations often encounter the following issues:

1. Classifying unstructured data

Managing and classifying unstructured data, like emails and documents, is complex and time-consuming. This type of data is often not easily searchable or indexable, making it harder to classify accurately.

2. Keeping classifications up to date

As data grows and changes, maintaining accurate and current classifications can be a continuous struggle. Outdated classifications can lead to gaps in data protection.

3. Balancing security with accessibility

Striking the right balance between comprehensive security and convenient access to data is challenging. Overly strict controls can hinder productivity, while lax measures may expose sensitive information.

4. Manual processes

‍86% of businesses still rely on mostly or fully manual methods to identify and tag data, which can delay responses to data breaches and increase the likelihood of human error.

These challenges highlight the need for automated tools to streamline data classification, improve accuracy, and enhance scalability.

How does AI enhance data classification?

Artificial Intelligence (AI) is revolutionising data classification by making the process smarter and more efficient. Here’s how AI is transforming the landscape:

1. Automating classification

AI takes over the tedious task of classifying data, reducing the risk of human error and ensuring consistent tagging across the board. This automation speeds up the process and improves accuracy.

2. Handling large volumes of data

AI excels at processing vast amounts of data quickly. It can sift through enormous datasets, identifying patterns and anomalies that might be missed manually.

3. Refining classification rules

AI systems use machine learning to continually refine their classification rules based on new data. This means that as data evolves, the AI adapts, enhancing both the accuracy and relevance of the classifications.

4. Improving real-time monitoring

AI provides real-time insights into data security, enabling immediate response to potential breaches. It also handles unstructured data effectively, learning and improving its classification capabilities over time.

Despite these advancements, only 48% of organisations have started adopting intelligent automation.

This leaves many still reliant on manual processes, which can be prone to errors and delays.

What are the types of data classification?

Data classification can be approached in several ways, depending on what works best for your organisation:

  • Content-based classification: This method involves analysing the actual content of files to determine their classification. It’s a thorough way to ensure sensitive information is correctly tagged and protected.
  • Context-based classification: Instead of examining the content, this approach relies on metadata, such as who created the file, where it was created, or which application was used. It’s a quicker method while still capturing essential context.
  • User-based classification: Here, knowledgeable users manually classify the data. This is particularly useful in specialised fields where users understand the sensitivity of the information.
  • Sensitivity levels: Data is typically classified into high, medium, or low sensitivity. High-sensitivity data, like financial records or personal information, requires the most protection, while medium and low-sensitivity data need fewer controls.

Interestingly, 75% of companies that use more than three levels of classification —such as Public, Internal, and Confidential—are more likely to experience one or more data breaches. Clearly, there’s a classification tightrope between detailed and overly complex that needs walking.

How can data classification help prevent data leaks?

Data classification isn’t just about organising information; it’s a vital strategy for preventing data leaks and ensuring good data security.

1. Protecting sensitive information

Data classification plays a crucial role in safeguarding sensitive information and reducing the risk of data leaks. By clearly identifying and categorising your data, you ensure that the most critical information is protected and access is limited to only authorised users.

2. Managing access control

Proper classification allows businesses to manage access control more effectively. For instance, only certain team members might have access to highly sensitive data, while less critical information can be more widely accessible. This targeted approach reduces the chances of unauthorised users stumbling upon sensitive information.

3. Enforcing encryption policies

Classification helps enforce encryption policies. Data classified as highly sensitive can automatically trigger encryption protocols, ensuring that even if accessed unlawfully, it remains unreadable and secure.

4. Ensuring regulatory compliance

Finally, data classification is essential for regulatory compliance. By aligning your data management practices with privacy laws and industry standards, you can avoid hefty fines and reputational damage that often accompany data breaches.

In essence, data classification acts as a first line of defence in a comprehensive data security strategy, helping to prevent costly leaks and breaches before they happen.

How can Metomic help?

Metomic makes data classification easier by tackling common challenges with smart, automated tools. It helps businesses quickly find and label sensitive information in real-time, making data discovery and compliance much simpler.

Key features include:

  • Automatic discovery and classification: Metomic instantly identifies and classifies sensitive data across SaaS and GenAI platforms, including personal, financial, and health data.
  • Custom classification: You can create custom classifiers to match your organisation’s specific needs.
  • Comprehensive scanning: Metomic scans a wide range of files, from documents to spreadsheets, across both public and private channels.
  • Alerts and remediation: Detailed alerts show exactly where sensitive data is, who shared it, and whether rules were broken—plus, it automates redaction to protect your data.

With easy integration, scalability, and AI-driven insights, Metomic helps businesses stay on top of data security and compliance without the hassle.

Getting started with Metomic

Free risk assessment scans

Kick things off with a free risk assessment scan to uncover potential data risks across platforms like Slack, ChatGPT, and Google Drive. It’s a simple way to get a clear picture of your organisation’s data security and spot any weak points.

Book a personalised demo

Ready to dive deeper? Book a personalised demo with one of our security experts. We’ll walk you through how Metomic’s tools can help you classify and protect your data in real time, and how we can tailor everything to fit your organisation's needs perfectly.

Key points

  • Data classification is essential for protecting sensitive business information.
  • Misclassification or lack of classification can lead to data breaches and compliance issues.
  • AI and automation help tackle the challenges of complex and large-scale data classification.
  • Metomic automates data discovery, provides custom classification and alerts, helps businesses protect sensitive data, and maintain compliance across digital platforms.

Data is one of the most valuable assets a business has—and one of the most vulnerable. That’s why proper data classification is so important, especially when it comes to cyber security.

By organising and tagging data based on its sensitivity and importance, businesses can apply the right security measures to keep their information safe.

Classifying data properly is essential for protecting sensitive information, avoiding data leaks, and staying compliant with regulations (particularly for finance and healthcare organisations).

This guide is here to help you navigate the world of data classification, offering you tips on how to make your organisation’s data more secure.

Whether it's financial records, personal details, or intellectual property, knowing how to handle and classify your data is key to keeping it secure.

What is data classification, in particular for cyber security?

Data classification involves organising data by its sensitivity and security needs, which helps in applying the right protections. This means labelling or tagging data into categories like public, internal-only, confidential, or restricted based on how sensitive the information is.

According to research by the Identity Theft Resource Center, there were 3,205 data compromises in the US in 2023, impacting over 353 million people - a staggering 72% increase over the previous year.

Whether it was a breach, leak, or accidental exposure, the end result was the same—sensitive data falling into the wrong hands.

Proper data classification can help reduce these incidents by ensuring the most critical information gets the protection it needs from unauthorised access.

What might data classification look like?

Proper classification ensures that sensitive information receives the appropriate level of protection, reducing the risk of unauthorised access. For example, public data might need minimal protection, while restricted data, such as personal health records or financial information, requires stricter security controls.

What does this look like in the real-world? In healthcare, incorrect classification could lead to patient privacy breaches, while in finance, misclassifying credit information could expose sensitive financial data to risks.

Effective data classification is crucial for maintaining security and compliance across various industries.

Why is it important for businesses? What are the benefits?

Data classification is crucial for strengthening your organisation’s security. By properly categorising your data based on its sensitivity, you’re ensuring that the most critical information gets the right level of protection.

This not only helps with compliance—think GDPR and HIPAA —but also boosts data protection, improves access control, and makes resource allocation more efficient.

The benefits are clear when it comes to risk management. Consider this: 75% of public sector organisations that don’t classify their data upon creation take days to detect data misuse.

In comparison, 25% of those that do classify their data spot misuse within minutes.

That’s a huge difference in response time, which can be crucial when dealing with potential security threats.

In short, data classification helps you know where to focus your efforts, so you can better protect what matters most and make smarter decisions when risks arise.

What are the challenges with data classification?

Data classification plays a crucial role in safeguarding sensitive information, but it comes with its fair share of challenges.

Organisations often encounter the following issues:

1. Classifying unstructured data

Managing and classifying unstructured data, like emails and documents, is complex and time-consuming. This type of data is often not easily searchable or indexable, making it harder to classify accurately.

2. Keeping classifications up to date

As data grows and changes, maintaining accurate and current classifications can be a continuous struggle. Outdated classifications can lead to gaps in data protection.

3. Balancing security with accessibility

Striking the right balance between comprehensive security and convenient access to data is challenging. Overly strict controls can hinder productivity, while lax measures may expose sensitive information.

4. Manual processes

‍86% of businesses still rely on mostly or fully manual methods to identify and tag data, which can delay responses to data breaches and increase the likelihood of human error.

These challenges highlight the need for automated tools to streamline data classification, improve accuracy, and enhance scalability.

How does AI enhance data classification?

Artificial Intelligence (AI) is revolutionising data classification by making the process smarter and more efficient. Here’s how AI is transforming the landscape:

1. Automating classification

AI takes over the tedious task of classifying data, reducing the risk of human error and ensuring consistent tagging across the board. This automation speeds up the process and improves accuracy.

2. Handling large volumes of data

AI excels at processing vast amounts of data quickly. It can sift through enormous datasets, identifying patterns and anomalies that might be missed manually.

3. Refining classification rules

AI systems use machine learning to continually refine their classification rules based on new data. This means that as data evolves, the AI adapts, enhancing both the accuracy and relevance of the classifications.

4. Improving real-time monitoring

AI provides real-time insights into data security, enabling immediate response to potential breaches. It also handles unstructured data effectively, learning and improving its classification capabilities over time.

Despite these advancements, only 48% of organisations have started adopting intelligent automation.

This leaves many still reliant on manual processes, which can be prone to errors and delays.

What are the types of data classification?

Data classification can be approached in several ways, depending on what works best for your organisation:

  • Content-based classification: This method involves analysing the actual content of files to determine their classification. It’s a thorough way to ensure sensitive information is correctly tagged and protected.
  • Context-based classification: Instead of examining the content, this approach relies on metadata, such as who created the file, where it was created, or which application was used. It’s a quicker method while still capturing essential context.
  • User-based classification: Here, knowledgeable users manually classify the data. This is particularly useful in specialised fields where users understand the sensitivity of the information.
  • Sensitivity levels: Data is typically classified into high, medium, or low sensitivity. High-sensitivity data, like financial records or personal information, requires the most protection, while medium and low-sensitivity data need fewer controls.

Interestingly, 75% of companies that use more than three levels of classification —such as Public, Internal, and Confidential—are more likely to experience one or more data breaches. Clearly, there’s a classification tightrope between detailed and overly complex that needs walking.

How can data classification help prevent data leaks?

Data classification isn’t just about organising information; it’s a vital strategy for preventing data leaks and ensuring good data security.

1. Protecting sensitive information

Data classification plays a crucial role in safeguarding sensitive information and reducing the risk of data leaks. By clearly identifying and categorising your data, you ensure that the most critical information is protected and access is limited to only authorised users.

2. Managing access control

Proper classification allows businesses to manage access control more effectively. For instance, only certain team members might have access to highly sensitive data, while less critical information can be more widely accessible. This targeted approach reduces the chances of unauthorised users stumbling upon sensitive information.

3. Enforcing encryption policies

Classification helps enforce encryption policies. Data classified as highly sensitive can automatically trigger encryption protocols, ensuring that even if accessed unlawfully, it remains unreadable and secure.

4. Ensuring regulatory compliance

Finally, data classification is essential for regulatory compliance. By aligning your data management practices with privacy laws and industry standards, you can avoid hefty fines and reputational damage that often accompany data breaches.

In essence, data classification acts as a first line of defence in a comprehensive data security strategy, helping to prevent costly leaks and breaches before they happen.

How can Metomic help?

Metomic makes data classification easier by tackling common challenges with smart, automated tools. It helps businesses quickly find and label sensitive information in real-time, making data discovery and compliance much simpler.

Key features include:

  • Automatic discovery and classification: Metomic instantly identifies and classifies sensitive data across SaaS and GenAI platforms, including personal, financial, and health data.
  • Custom classification: You can create custom classifiers to match your organisation’s specific needs.
  • Comprehensive scanning: Metomic scans a wide range of files, from documents to spreadsheets, across both public and private channels.
  • Alerts and remediation: Detailed alerts show exactly where sensitive data is, who shared it, and whether rules were broken—plus, it automates redaction to protect your data.

With easy integration, scalability, and AI-driven insights, Metomic helps businesses stay on top of data security and compliance without the hassle.

Getting started with Metomic

Free risk assessment scans

Kick things off with a free risk assessment scan to uncover potential data risks across platforms like Slack, ChatGPT, and Google Drive. It’s a simple way to get a clear picture of your organisation’s data security and spot any weak points.

Book a personalised demo

Ready to dive deeper? Book a personalised demo with one of our security experts. We’ll walk you through how Metomic’s tools can help you classify and protect your data in real time, and how we can tailor everything to fit your organisation's needs perfectly.

Ben van Enckevort

CTO and Co-Founder

Ben van Enckevort is the co-founder and CTO of Metomic

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