Key points

• Proper data classification is crucial for safeguarding sensitive information, preventing data breaches, and ensuring regulatory compliance.‍
• Data is typically classified into levels (e.g., public, internal, confidential, restricted) and types (content-based, context-based, user-based, sensitivity-based).‍
• Data classification strengthens security, improves access control, enhances data management, and reduces risk.‍
• Overcoming challenges like data volume, policy clarity, user resistance, integration, resource constraints, and evolving regulations requires a combination of automation, clear policies, user education, and adaptable technology.‍
• AI data classification tools like Metomic can automate classification, handle large datasets, refine classification rules, and improve real-time monitoring, making the process more efficient and accurate.

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 are the 4 key classification levels for data?

There are 4 typical classification levels based on the sensitivity of the information:

1. ‍Public: Data intended for broad sharing, requiring minimal security.‍
2. Internal: Data used within the organisation, requiring moderate controls.‍
3. Confidential: Sensitive data that could harm the organisation if exposed, requiring strong encryption and restricted access.‍
4. Highly Confidential or Restricted: Data that could cause severe damage if exposed, requiring the strictest security measures, with access restricted to a minimal number of trusted individuals.

What are the 4 types of data classification?

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

1. ‍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.‍
2. 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.‍
3. 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.‍
4. 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.

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.

🎙️Interview: Everything You Need To Know About Data Classification

In this interview with Metomic's VP of Engineering, Artem Tabalin, we dig deep into how data classification can transform your business' data security

6 Challenges of Data Classification and How to Overcome Them

Data classification plays a crucial role in safeguarding sensitive information, but it comes with its fair share of challenges. Many IT teams struggle to implement classification systems that are accurate, scalable, and integrated with existing workflows.

Here are six key challenges IT teams face with data classification and strategies to address them.

1. Volume and Complexity of Data

Modern businesses generate massive amounts of data across multiple systems and applications. For IT teams, the sheer volume of data creates a formidable challenge to classify it all effectively. Compounding this issue is the growing diversity of data formats—structured and unstructured—which can include anything from spreadsheets and emails to multimedia files and complex datasets.

Manual data classification at scale is mission impossible, even for the most security conscious organisation. That’s why automation is the key to managing large data volumes. Machine learning and artificial intelligence can streamline the process by automatically tagging and categorising data based on patterns and content analysis. Automated tools can quickly analyse large data sets and identify sensitive information, making it possible for teams to classify data in real-time as it’s created or modified. To maximise effectiveness, businesses should select classification tools designed to handle a wide range of data formats, both structured and unstructured.

2. Lack of Clear Classification Policies

Even with the best tools, data classification efforts can fall short without a clear framework. When classification policies are vague or poorly defined, inconsistencies in data handling can arise, potentially leading to security gaps or compliance issues. Teams need a solid understanding of what constitutes sensitive, confidential, or restricted data to categorise information consistently.

As such, developing clear, comprehensive classification policies is essential. These policies should outline specific categories based on data sensitivity, business impact, and compliance requirements. Classifications such as "Public," "Internal Use," "Confidential," and "Restricted" offer a foundational approach. Involving stakeholders from IT, legal, compliance, and business units ensures the framework aligns with organisational needs. Regular policy reviews are also necessary to adapt to new types of data, changing regulations, or evolving business requirements.

3. User Resistance

Employee cooperation is critical to a successful data classification program. However, some users, or realistically the majority of users, may perceive classification tasks as burdensome or unnecessary, leading to low compliance or errors. This resistance can be especially prevalent when employees lack a full understanding of data classification’s importance or feel the added steps slow down their workflow. And, let’s be honest, asking people to classify each and every asset they just won’t fly in most organisations. 

While education and empowerment are the best strategies to overcome user resistance, having tools in place to classify at scale can significantly reduce reliance on employees. Training sessions that emphasise the importance of data security and the role of classification in protecting sensitive information can increase buy-in.

Additionally, deploying user-friendly AI-powered tools that minimise, or even remove, the effort required for manual tagging or allow for real-time prompts can make the process seamless. When employees understand how classification contributes to overall data security, they are more likely to engage actively and mindfully with the process.

4. Integration with Existing Systems

One major challenge IT teams face is integrating data classification into the organisation’s existing IT infrastructure, especially when legacy systems are involved. Older systems may lack compatibility with modern classification tools, creating friction and limiting visibility into sensitive data across the organisation.

That’s why businesses need to seek out data classification tools designed for integration with various platforms, including cloud storage services, SaaS applications, and on-premises systems. Additionally, consider using API-based solutions that facilitate integration across diverse environments.

In cases where legacy systems don’t support seamless integration, gradual migration to newer, more compatible systems may be necessary. Taking a phased approach allows organisations to adopt classification systems without compromising existing operations or security protocols.

5. Resource Constraints

Effective data classification requires more than just the right technology—it also demands skilled personnel and ongoing financial investment. Smaller IT teams or companies with limited budgets may struggle to implement and maintain a robust classification system, which can lead to missed opportunities for improved data management and security.

As such, businesses need to prioritise automation to reduce the manual burden on IT staff. Automated classification tools can significantly lower operational costs while ensuring consistent application of classification policies. Additionally, consider phased implementation, beginning with the most sensitive data and expanding as resources allow. Partnering with a managed service provider (MSP) can also be a cost-effective way to access expertise and technology without having to build a dedicated in-house team.

6. Evolving Data Regulations

Data protection regulations such as GDPR, CCPA, and HIPAA require strict data handling and classification to protect sensitive information. However, keeping up with evolving regulatory requirements can be a challenge, especially for organisations operating in multiple jurisdictions. Failure to stay compliant not only poses security risks but can also result in substantial legal penalties.

By implementing a classification system that supports regulatory compliance from the start, businesses can save time and resources. Classification tools that map specific data types to regulatory requirements are invaluable. For example, some tools are designed to recognise personally identifiable information (PII) and health-related data, which are often subject to stringent protection standards. Regular compliance audits and updates to classification policies will also ensure that businesses stay aligned with the latest regulatory requirements.

Data classification may seem like it presents a host of challenges to businesses, but the benefits are well worth the effort. By implementing a clear framework, investing in automation, and securing buy-in from employees, organisations can overcome these obstacles and create a data environment that is both secure and efficient. As the digital landscape continues to evolve, data classification will only grow in importance as a foundational component of modern data security strategies.

With the right approach, businesses can effectively manage data across diverse environments, reduce risk, and stay ahead of the curve in an increasingly complex regulatory landscape. Whether you’re starting from scratch or enhancing an existing framework, a strong data classification system is a key driver in building trust and resilience in the modern workplace. 

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.

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.

Here are the some key best practises for effective data classification:

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 to start your Data Classification process

Data classification is a critical first step in safeguarding sensitive information, enabling organisations to identify, categorise, and protect data according to its level of sensitivity.

However, starting a data classification project can seem daunting, especially for businesses that are new to this practice.

This guide will walk you through the essential steps to kickstart your data classification process effectively.

1. Understand the Importance of Data Classification

Before diving into the steps you’ll need to take, it's crucial to understand why data classification is necessary. Data classification helps businesses manage and protect their data more efficiently by categorising it based on its sensitivity, importance, and access needs.

Proper classification allows you to:

• Improve data security by applying appropriate protection measures.
• Ensure regulatory compliance with standards like GDPR, HIPAA, PCI DSS 4.0 and CCPA.
• Enhance data management by enabling easier retrieval and use of information.
• Reduce the risk of data breaches and their associated costs.

With these benefits in mind, you’re better equipped to understand why a structured approach to data classification is essential.

2. Define Your Data Classification Objectives

Every data classification project should begin with clear objectives. Ask yourself:

• What do you hope to achieve with data classification?
• Are you focusing on compliance, data security, or improving data management?
• Which data types are most critical to your business?

Defining these objectives will help you tailor your approach to the specific needs of your organisation, ensuring that the classification process aligns with your overall business goals.

3. Identify and Involve Stakeholders

Data classification is not just an IT responsibility; it requires input from across the organisation. Identify key stakeholders, including:

• IT and security teams who will implement the classification.
• Compliance officers who understand regulatory requirements.
• Department heads who manage the data on a daily basis.
• Legal teams to ensure that classification meets legal standards.

Involving these stakeholders early ensures that the classification process is comprehensive and considers all necessary perspectives.

4. Conduct a Data Inventory

Before you can classify data, you need to know what data you have. A data inventory is a comprehensive list of all the data assets within your organisation. This inventory should include:

• Data types (e.g., customer information, financial records, intellectual property).
• Data sources (e.g., databases, cloud storage, physical files).
• Data location (where the data is stored, whether on-premises or in the cloud).
• Data access points (who has access to the data and from where).

Conducting a thorough data inventory provides a clear picture of your data landscape and is crucial for effective classification.

5. Develop a Classification Framework

A classification framework is a set of guidelines that dictate how data will be categorised. Typically, data is classified into several levels, such as:

• Public: Data that can be freely shared without any risk.
• Internal: Data that is used within the organisation but not shared publicly.
• Confidential: Data that is sensitive and requires protection, such as customer information.
• Restricted: Data that is highly sensitive and access is limited to a few authorised individuals.

Your framework should include clear criteria for each classification level, ensuring consistency across the organisation.

6. Implement Classification Policies and Procedures

Once your framework is established, it’s time to create and implement policies and procedures that support the classification process. These policies should cover:

• Data labelling: How will classified data be labelled and marked?
• Access controls: Who has access to different levels of classified data?
• Data handling: How should data be handled based on its classification level?
• Retention policies: How long will classified data be retained, and when should it be deleted?

Ensure that these policies are communicated clearly to all employees and that training is provided where necessary.

7. Leverage Technology for Automation

Manual data classification can be time-consuming and prone to errors. Leveraging technology can streamline the process and improve accuracy.

Modern automated data classification solutions, like those offered by Metomic, can automatically classify data based on predefined rules and patterns. These tools can also monitor data in real-time, ensuring that it remains protected according to its classification.

8. Monitor and Review the Classification Process

Data classification is not a one-time task; it requires ongoing monitoring and review. Regularly audit your classification process to ensure that it remains effective and that policies are being followed. Additionally, review your data inventory periodically to account for new data types or changes in the business environment.

9. Continuously Educate and Train Employees

The success of your data classification project depends largely on the awareness and cooperation of your employees. Regular training sessions should be conducted to educate staff on the importance of data classification, how to handle classified data, and how to report any issues.

10. Start Small and Scale Gradually

Starting with a pilot project can be a good approach to data classification. Choose a specific department or data type to classify first, learn from the process, and then gradually expand the classification efforts across the entire organisation. This approach allows you to refine your framework and policies before applying them on a larger scale.

How does data classification help an organisations’ DLP strategy?

Data classification is an integral part of any organisation’s DLP strategy as it helps the security team understand how sensitive certain types of data are, allowing them to apply the necessary protective measures.

It can help to strengthen a DLP strategy by:

1. Identifying critical data

Classifying data as public, internal, confidential, or highly confidential enables teams to understand where their most sensitive data is stored, and which data needs the most protection.

2. Implementing security policies

With data correctly classified, DLP systems can be configured to enforce specific security policies, tailored to the organisation’s requirements. For instance, highly confidential data can trigger stricter controls, such as encryption or restricted access.

3. Complying with industry regulations

Data classification can help organisations stay compliant with regulations such as GDPR or HIPAA, by ensuring sensitive data is properly identified and handled appropriately, reducing the risk of violations and potential fines.

4. Preventing data breaches

Classification helps DLP systems, and security teams, understand where the most critical company or customer data is stored. Applying rules to this data can prevent accidental or intentional breaches, by restricting access and downloads.

5. Reducing false positives

Focusing on specific types of data allows DLP systems to operate more efficiently, reducing false positives and ensuring that security resources are spent on protecting the most critical data.

How can DLP solutions help with data classification?

DLP solutions help organisations classify their data by automating the identification and labeling of sensitive data in real-time. Using predefined rules and policies, DLP solutions are able to classify sensitive data automatically, reducing the need for team resources.

Integrating with SaaS applications like Slack, Google Drive, and ChatGPT, DLP solutions help organisations manage data across multiple platforms, without compromising employee productivity.

The ability to bulk-classify also helps save time and ensures human error is minimised, making data protection more efficient. Ultimately, DLP solutions strengthen an organisation’s data security by ensuring sensitive data is properly classified and protected.

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 data 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 or get in touch to speak directly to our team. 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.