TL;DR

The massive amount of data that organisations generate makes having an effective data classification strategy essential. This article will explain the importance of data classification, cover the fundamental concepts behind data classification and outline the most important steps to implement a quality data classification program. We'll then dive into the main challenges behind creating a functional data classification infrastructure and list the possible solutions, including the top data classification tools on the market today.

Introduction

Researchers predict that the world will create over 180 zettabytes of data by 2025. The massive volume of data means that businesses must systematically organise their structured and unstructured data. Otherwise, they may find themselves with an enlarged threat surface and may get bogged down by the weight of their data. They may even fall out of compliance. 

Enter data classification — the process of labelling, categorizing and processing data according to its sensitivity and risk level. Data classification is a key part of any company's efforts to keep track of its digital assets. Those assets may range from documents containing low-risk public knowledge to mission-critical trade secrets and records. Each one must be handled appropriately to ensure efficient business processes and avoid a security incident. Data classification is the first step in this process. 

In this article, we'll dive into the fundamentals of data classification, including the core concepts that it entails. Then we'll get practical and show you the main steps involved in developing your data classification system and some key challenges and their solutions. From basic questions like, "What is data classification?" to in-depth questions on implementing your classification environment, we've got the answers below.

Key Takeaways

• Data classification is the process of labelling and organising your data according to its data type, sensitivity level and risk level according to any applicable standards or regulatory requirements. It also involves the development of policies and procedures to guide current and future data transfer, storage, editing and accessing processes. 
• Some of the main benefits of data classification include stronger data security, reduced risk,  more efficient data management practices, improved compliance and a better brand image.
• Organisations should start by inventorying their available data assets, conducting a risk assessment for each and staying current on the standards and protocols that apply to their business processes.
• The top tools to assist with overcoming the hurdles related to data classification are Netwrix Data Collection, ManageEngine Data Security Plus, Informatica Enterprise Data Catalog, Safetica Data Discovery and Classification and Varonis Data Classification Engine. 
• The primary challenges behind data classification are processing unstructured data, minimising human error and balancing security requirements with the need for data accessibility. 
• Implementing best practices such as creating employee resources, regularly updating your policies and integrating automation can also help overcome your data classification challenges.

Key Elements of Data Classification

Before you can create a systematic data classification policy, you need to understand the core components of organising your data. That means grasping these classification concepts: 

• Data types: Structured or Unstructured 
• Sensitivity level: Public, Internal, Private, Confidential, Sensitive, Restricted 
• Risk level: Low, Medium, High 
• Classification method: Content, context, or user-based 
• Standards: GDPR, CCPA, ISO:27001, NIST 800-53, PCI DSS, HIPAA, among others 

The purpose of data classification goes beyond data discovery, however. An insightful data classification policy not only helps you label and organise your data, but it also goes a step further to secure your most valuable digital assets. 

Data Types

Data can come in many different forms. Spreadsheet figures, intellectual property, survey responses, employee records and trade secrets — these are just a few data assets that your organisation may possess. The disparate nature of your data stack means that you will likely have to assign classification levels to each item according to its type.  

There are two main data types: structured data and unstructured data. Structured data has a highly standardised format and is therefore easier for software to process. Examples of structured data include credit card numbers, social security numbers, bank account numbers, or other tabulated information. This type of data is well-suited for automation and AI/ML-powered analytics. 

Unlike structured data, unstructured data lacks a standardised format and may consist of less quantifiable information. Some examples include handwritten documentation, social media reactions, or personal, protected health information (PHI) such as medical images or prescriptions. AI/ML algorithms have greater difficulty analysing unstructured data, so it often requires greater human involvement and more manual analysis. 

Understanding the differences between these data types can help you classify your data in a more organised manner, but can also impact your data management processes. For example, unstructured data may require more time to clean and wrangle than structured data, giving it a different priority level depending on its content. Before you begin to classify your data, make sure you know its type. 

Sensitivity Level

Not all data is created equal. Intellectual property, trade secrets and competitive analysis all have a greater impact on your business operations than employee or vendor records, so your data classification system should attempt to organise your data assets accordingly. 

Sensitivity level refers to the urgency with which each data asset should be treated, with a view to the effect it would have on your organisation if it was improperly disclosed. Businesses may use different terms and levels to classify their data assets, but in increasing order of importance, the most common ones are: 

• Public: This data is considered readily available knowledge and would not hinder company operations if it were disclosed. Examples include news stories, pre-disclosed market research data, current branding slogans, etc. 
• Internal: Consisting of information that's largely kept inside the company, such as corporate emails, memos, or employee records, internal data is typically a low-level security priority, but could still impact operations if it was released. 
• Private: Disclosure of private data may or may not have legal consequences, giving it a higher sensitivity level than public or internal data. Examples include some personally identifiable information (PII), vendor records, etc. 
• Confidential: Confidential data could cause significant harm to the company if it was disclosed. Examples include supply chain information, trade secrets, some financial records, etc.
• Sensitive: Sensitive data could have legal ramifications if disclosed and could prove highly disruptive to your business operations. Examples include intellectual property, personal health information (PHI) and some PII. 
• Restricted: This is the highest sensitivity level. Often used by governmental agencies to guard the most important information, disclosure of restricted data has severe legal consequences, could be catastrophic for businesses and could result in complete business shutdowns, compromises to national security and danger to human life. Examples include the status of military operations, certain government contracts, etc. 

Organisations may prioritise their data assets according to these sensitivity levels. They can then create a more thoughtful data classification system and apply the appropriate security protocols to each sensitivity level. 

Risk Levels

The sensitivity level denotes the criticality of each data asset, but the risk level considers factors such as the difficulty of data recovery and the likelihood that a data asset will be compromised. Risk level categories are more straightforward than sensitivity levels and include:

• Low Risk, or data that is easy to recover, is unlikely to be exposed and would create minimum damage if it was (e.g., public data).  
• Medium Risk, or data that could create some hindrance if it was exposed, but is relatively simple to secure and recover (e.g., internal and some private data).
• High Risk, or data that is highly vulnerable to attack, would be difficult to recover and requires greater effort to safeguard (e.g., confidential, sensitive, or restricted data). 

Identifying the risk level of each data asset can aid your data risk management efforts by revealing where your greatest vulnerabilities lie. This will allow you to allocate your data resources more strategically, giving proper attention where it's due first.

Classification Methods

Once you've assessed the nature of each data asset, the next step is to decide which method you'll use to classify it. There are three data classification methods available and they vary based on the tool used to label and organise your data. They are: 

• Content-based classification: Software using this schema classifies data based on the information contained within each data asset it scans. 
• Context-based classification: Rather than evaluating the actual contents, context-based classification software uses metadata criteria such as document creator, date of creation, or document type to organise your data assets. 
• User-based classification: Instead of employing software, user-based classification is done manually, with human analysts combing through your data items to place them where they belong. 

Content and context-based classification software are substantially faster than user-based classification methods, but both depend upon AI/ML algorithms to identify the data they must organise. They are therefore susceptible to errors if some data items fall outside the boundaries of their criteria, which is especially common for unstructured data. That means you'll likely need to employ a combination of content, context and user-based classification methods, with the former handling the brunt of structured datasets and the latter processing the hard-to-classify items. 

Standards and Frameworks

Multiple industry standards and regulatory requirements exist that dictate how organisations store, process and classify their data. Adherence to these standards is vital if companies hope to avoid costly compliance violations, so you should closely follow the requirements listed in the relevant industry standards. Others may apply as well, but the most common industry data classification standards are: 

• General Data Protection Regulation (GDPR) 
• National Institute of Standards and Technologies (NIST) 800-53
• Health Insurance Portability and Accountability Act (HIPAA)
• Payment Card Industry Data Security Standards (PCI DSS)
• International Organization for Standardization (ISO:27001)

Depending on your industry, you may need to comply with multiple industry standards. 

Step-by-Step Guide To Implementing Data Classification

Once you know the core principles that form the foundation of a data classification system, you can take the practical steps needed to implement your own. The exact process you follow may vary with your application, but a general data classification outline includes:

1. Set the stage: Define the scope of your data classification system, so that you know which assets you'll be devoting your attention to classifying.
2. Create a team: From IT and R&D to Finance and Legal, identify the key role players within your data environment and bring all parties on board. 
3. Take an inventory: You won't know how to classify your data until you know what you have. 
4. Define your terms: Establish criteria for each sensitivity level, so that you know where each data item belongs.
5. Know your risks: Different data types require different security measures, so conduct a risk assessment to see which ones should be the highest priority. 
6. Select a standard: The GDPR, NIST 800-53, ISO 27001, HIPAA, and PCI DSS all have specific requirements that may impact your data classification system, so construct it around the standard that applies to you. 
7. Establish guidelines: From encryption methods to least-privilege principles, set up clear criteria for how data types at each sensitivity level should be handled.
8. Create a labelling system: Your system should be clear, consistent and concise, so create labels that each team member can follow. 
9. Train your team: Each team member must know where your data assets should go, so give them ample training and resources so that they can follow the system you create. 
10. Be prepared: The threat landscape is evolving at an alarming rate, so create an incident response plan that will restore your operations should a data breach ever occur.
11. Create documentation: From cheat sheets and manuals to policies and procedures, draft educational resources that your team can use as a reference.
12. Report, review, repeat: Use audits and spot checks to periodically evaluate the status of your data classification system and allow room for improvement by receiving input from your team and making revisions as needed. 

Choosing the proper standard is a particularly important step when formulating your data classification environment, as it can help guide you through the steps that come next. Each standard offers plenty of resources to help organisations classify their data according to their compliance requirements, making it easier to execute the remaining steps in the process. That means you must first assess your data environment, but you can then consult external frameworks for reference as you craft the rest of your data classification pipeline.

Tools and Technologies: The Top 5 Data Classification Tools 

Part of implementing an effective data classification system is choosing a high-functioning tool that plays well with the rest of your stack. There are many tools on the market to choose from, with plenty of reviewers having their take on which one is best. We won't rate them in any particular order since only you know which one will work best for your business, but these data classification tools regularly rank near the top.

Netwrix Data Collection

Netwrix is a cybersecurity company whose tools empower clients to discover and lock down their data. Its Data Collection software employs high-fidelity classification algorithms that can identify specific data sets with maximum precision, making it well-suited for companies with a high data volume. It also features vulnerability remediation functionalities and can be configured to comply with multiple regulatory standards, including the GDPR, HIPAA, PCI DSS, and more. 

Netwrix's Data Collection platform also integrates into many common data stores such as SharePoint, Oracle Database and SQL Server. Its compatibility, user-friendliness and advanced capabilities let companies take the next step beyond data discovery, and into data security — the true purpose of classification. 

ManageEngine Data Security Plus

Like Netwrix, ManageEngine's solutions go beyond classification and can benefit a company's entire data management pipeline. The automated portion of their Data Security Plus tool combines content and context-based classification methods to detect, label and classify a wide range of data types, while still allowing users to manually organise their data as well. 

The result is a highly versatile platform that can assimilate into multiple IT environments, scan and sort highly disparate data types and manage your data in the cloud. If your company is seeking an all-around data management tool, ManageEngine is a good place to start. 

Informatica Enterprise Data Catalog

Informatica employs AI-powered algorithms to drive its suite of data management solutions. Its capabilities include: 

• Data profiling
• Data lineage
• Data scorecard generation
• Data classification 
• Data analysis

The tool also uses AI to scan for similarities between datasets, evaluate metadata to better organise data assets and even make recommendations when key data items appear to be missing from analytics reports. The result is a highly insightful data classification tool that can empower data-driven decision-making, though the steep learning curve can hinder users from taking full advantage of all its capabilities. 

Safetica Data Discovery and Classification

Safetica's data classification tool specialises in securing intellectual property and other highly sensitive data. Its Optical Character Recognition (OCR) enables it to scan unstructured data types such as drawings, blueprints, or other images to identify crucial information and apply the appropriate access controls. Its data-in-motion capabilities work well for IT environments where data is constantly transmitted from one repository to another, and its content, context and property-based classification method further enhances its ability to detect a wide range of data types. 

Varonis Data Classification Engine

Combining data classification and data security, the Varonis platform uses automated classification tools to discover sensitive data. Its risk visualisation capabilities give a clear picture of where a company's greatest vulnerabilities lie and its greatest strength is its ability to detect private information such as PII, PHI and purchase card data. The Universal Database Connector enables the Varonis platform to track, classify and secure data as it moves across multiple data silos. Plus, its automatic updates coupled with widespread compatibility make it a highly functional yet user-friendly tool. 

Data Classification Challenges and Solutions

With the right tools in place, you'll be better equipped to tackle the main challenges associated with implementing a strategic data classification system. Here are the primary obstacles you're likely to face, along with some simple solutions:

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Handling unstructured data: Unstructured data can take many forms and can be difficult to classify systematically. some text

Solution: Search specifically for a data classification solution with AI/ML algorithms that can effectively process unstructured data. 

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Minimising human error: Even with AI advancements, some tools still struggle to identify unstructured data. This makes manual classification necessary and introduces the possibility of human error, so multiple checks are a must.

‍Solution: Provide your staff with ample educational resources on your data classification system and offer plenty of training to help them gain proficiency with your tool. 

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Balancing accessibility with security: A company's data assets must be readily available when their employees need them, but only on a need-to-know basis.

‍Solution: Implement identity and access management (IAM) controls and the least-privilege principle to ensure that only authorised users have access to sensitive data assets. 

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Keeping up-to-date with regulatory changes: Some frameworks evolve regularly, making it challenging to stay in compliance.‍

Solution: Use a tool with automatic updates that can comply with your industry standard to help you stay abreast of any regulatory changes. 

Best Practices for Data Classification

Having the right tool can help, but you'll need an equally strategic set of policies and procedures to maintain your data classification system. Consider implementing these best practices:

• Keep the number of classification categories you use to a minimum while retaining the necessary priority levels
• Empower employees to actively participate in creating and maintaining your classification system
• Store any critical physical documents in a secure location
• Regularly conduct audits and spot checks, and enforce consequences for violating your policies
• Encrypt your most sensitive data assets 

Alongside the right data classification tools, these steps can help companies not only identify and track their digital assets but also take active steps toward securing them and remediating their most glaring vulnerabilities. 

Conclusion

The amount of data that businesses generate every day is becoming overwhelming. Implementing an insightful data classification infrastructure can reduce the risk of a security incident or compliance violation, streamline data management processes, and facilitate data-driven decision-making to create a competitive edge. Leveraging the right tools, adhering to the relevant standards and following best practices can make your data classification system a success.

FAQ

How does a multi-tiered classification level system impact data breach incident response?

A well-defined classification level system can significantly streamline incident response by prioritising actions based on the sensitivity of the compromised data. For instance, a breach involving confidential or highly sensitive information triggers an immediate and rigorous response, focusing on data security containment and regulatory compliance notification requirements.

What are the challenges of applying classification levels to mixed datasets containing both personal and public data?

Applying classification levels to datasets with mixed sensitivity presents unique challenges. It requires a careful balance to ensure that personal data is sufficiently protected under data protection laws like GDPR, while still allowing the necessary accessibility to public data. Striking this balance is crucial for both regulatory compliance and operational efficiency.

Could you describe the role of data classification in the development of AI-driven data loss prevention strategies?

In AI-driven data loss prevention (DLP) strategies, data classification serves as the foundation for training models to understand the type of data they need to protect. By categorising data effectively, DLP systems can better identify and apply security controls to sensitive information and personally identifiable information, enhancing overall data protection.

How do recent changes in compliance regulations affect the re-classification of existing data in large databases?

Recent updates in compliance regulations often necessitate the re-evaluation and re-classification of existing data within large databases. This process ensures that data sensitivity is aligned with the latest legal standards, particularly for personally identifiable information and other forms of sensitive data. As a result, security controls may need adjustment to stay in line with the enhanced requirements.

What best practices should businesses adopt to maintain classification consistency for unstructured data across different platforms?

Businesses should adopt best practices such as implementing standardized classification schemas that remain consistent across all platforms. This involves the use of metadata tagging, regular training on information security policies, and the deployment of advanced data classification tools capable of handling unstructured data, ensuring that all data is categorised according to its sensitivity and compliance needs.

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