Updated On - May 30, 2024Definition of Data Classification
Data classification is the process of sorting data based on its importance and sensitivity. This involves labeling data with tags or metadata that indicate its level of confidentiality, such as public, internal, confidential, or restricted. By doing this, organizations can effectively manage and protect their data by applying appropriate security measures and access controls. For instance, highly sensitive data like financial records or personal information may be classified as confidential and require strict protection, while less sensitive data may be labeled as internal and have more relaxed access controls. Data classification helps organizations prioritize security efforts, allocate resources efficiently, and ensure compliance with relevant laws and regulations, ultimately enhancing data governance and streamlining data management processes.
Types of Data Classification
There are there types of data classification, which include Content-based, Context-based and User-based Classification.
Content-based Classification
This type of classification involves reviewing and categorizing data based on its content, including the physical process of filing and organizing paperwork.
Context-based Classification
In this approach, data is classified based on its context, which includes metadata such as:
- The software or program used to create the file
- The author’s name or identity
- The physical location where the document was created or edited
User-based Classification
This method involves assigning categories to data based on the manual decisions of an individual with expertise in the field. Examples of user-based classification include:
- Designating confidentiality status for sensitive documents
- Classifying documents at the time of creation
- Reclassifying documents after significant edits or reviews
- Final classification before publication
Benefits of Data Classification
There are many benefits of data classification, some of which include:
Improved Data Management
Organizations can improve their data management capabilities, allowing them to efficiently store, retrieve, and manage large amounts of data. This is especially important for businesses that rely on data to make informed decisions, as it enables them to quickly locate specific data and reduce the risk of data loss or misplacement.
Enhanced Data Security
By categorizing data based on its sensitivity, organizations can implement the necessary security controls to protect it. For example, highly sensitive data such as financial information or personal identifiable information (PII) may require additional security measures, such as encryption, access controls, and user authentication. By classifying data, organizations can ensure that the right level of security is applied to protect it from unauthorized access or breaches.
Better Decision-Making
Data classification can also help organizations make better decisions by providing a clearer understanding of their data. By categorizing data based on its relevance, importance, and sensitivity, organizations can identify trends, patterns, and relationships that may not be immediately apparent. This can help businesses make informed decisions, identify opportunities for growth, and optimize their operations.
Cost Savings
Data classification can also help organizations reduce costs associated with data management. By classifying data, organizations can identify and eliminate redundant or irrelevant data, which can help reduce storage costs and improve data retrieval efficiency. Additionally, data classification can help organizations identify areas where data is not being used effectively, allowing them to redirect resources to more valuable areas.
Compliance with Regulations
Data classification is essential for compliance with regulatory requirements. Many industries, such as finance and healthcare, are subject to strict regulations regarding data storage, security, and access. By classifying data, organizations can ensure that they are meeting these regulatory requirements, which can help prevent costly fines and reputation damage. By classifying data, organizations can demonstrate their commitment to data protection and security, which can help build trust with customers and stakeholders.
What is Data Classification Software?
Data classification software simplifies the data classification process for enterprises by automating many steps and reducing the need for human intervention. This automation enables organizations to significantly cut down on the time and effort required for data classification, allowing them to focus on more critical tasks. Furthermore, by automating as many processes as possible in the data classification workflow, the software increases efficiency and reduces the likelihood of errors, resulting in a more streamlined and accurate data classification process.
The Need for Data Classification Software
Manual data classification is a time-consuming, labor-intensive and thus costly process. One of the main challenges in manual data classification is the sheer volume of data, which can be overwhelming and prone to errors.
Additionally, human analysts may not have the same level of expertise or knowledge in a particular domain, leading to inconsistencies and inaccuracies in the classification process. The complexity of data types, such as unstructured data, text, and images, can make it difficult to develop effective classification rules.
Understanding exactly what data a business collects, where it is stored within the company, and who has access to it at any given time, are also major challenges associated with manually classifying data. This information can be gathered from a variety of sources, including contact forms filled out by potential customers, which may contain personally identifiable information such as name, place of employment, job title, and email address.
Point of sale transactions also contribute to the pool of data, which may include sensitive information like physical addresses and financial data like credit card numbers. The data can be captured and fed into the system through various channels, including network devices and content sharing platforms.
While large datasets that are anonymized or do not contain sensitive information may present little to no risk, other datasets may be high-risk due to their in-demand status or attractive value to hackers, making it crucial for businesses to effectively classify and manage their data.
How Data Classification Software Enhances Data Security and Compliance
Data classification software helps organizations effectively manage and protect sensitive information by automating the process of assigning tags to each piece of data, allowing for the application of specific storage, management, usage, and sharing rules based on corporate and regulatory requirements. Below are some of the most notable benefits of using data classification software:
Reduced Financial Risk
By automating the task of identifying and tracking sensitive data, cybersecurity teams can free up resources to focus on higher-level, strategic thinking. This can lead to improved data classification accuracy, which can in turn significantly reduce financial risk. Furthermore, reducing human error, which is a leading cause of data breaches, can also have a substantial impact on financial risk.
Minimized Data Breach Risk
With real-time monitoring, you can detect new datasets and points of data capture as they enter your system, classify the data, and direct it to the appropriate protected areas. This level of visibility provides a robust framework for safeguarding your organization’s data, enabling you to identify and mitigate risks of data breaches or loss. You can track user access and activity to prevent unauthorized use, and establish protocols and processes for accessing and using data to prevent vulnerabilities and gaps in your security from occurring and being exploited.
Increased Compliance
If your data or location requires compliance with data privacy laws, data classification software can be a crucial tool in ensuring regulatory compliance. This includes companies handling sensitive data such as health, financial, or personal consumer information. Data classification software can segment and protect sensitive data in a controlled environment, mitigating the risk of non-compliance and potential fines. The consequences of non-compliance are severe, with the EU’s General Data Protection Regulation (GDPR) imposing fines of up to €20 million or 4% of annual global turnover, whichever is higher. Similarly, the California Attorney-General can pursue civil penalties for non-compliance with the California Consumer Privacy Act (CCPA), including fines of hundreds of millions of dollars. However, businesses that take a proactive and reasonable approach to compliance may be able to avoid penalties altogether. Data classification tools can demonstrate a company’s commitment to compliance and maintain the necessary protections required by data privacy laws, even in the event of a minor violation.
Data Sensitivity Levels
Three levels of data sensitivity have been established to ensure the proper handling and protection of organizational and individual information.
High-Sensitivity Data
Compromise or destruction of this data could have a catastrophic impact on the organization or its individuals due to an unauthorized transaction. Examples of high-sensitivity data include financial records, intellectual property, and authentication information.
Medium-Sensitivity Data
This data is intended for internal use within the company. If compromised or destroyed, it would not cause significant harm to the organization or its employees. Examples of medium-sensitivity data include emails and documents that do not contain sensitive information.
Low-Sensitivity Data
This data is publicly accessible and intended for use by the general public. Examples of low-sensitivity data include content on publicly accessible websites.
Key Features to Look for in Data Classification Tools
When choosing a data classification solution, you should look for the following features:
Data discovery
The software should be able to scan and identify data, applying a predefined schema to organise it into predetermined levels of sensitivity (public, internal, confidential, sensitive).
Real-time monitoring
The software should be able to continuously monitor and classify data in real-time, 24/7, across both on-premise and cloud-based environments.
Automatic policy and workflow implementation
The software should automate the application of data classification policies and workflows, minimizing human error and freeing up team members to focus on strategic planning.
In addition to these points you should also consider the following factors when choosing data classification software:
Scalability
When selecting data classification software, one crucial consideration is scalability. Does the tool have the ability to grow with your organization and accommodate an increasing volume of data? You’ll want to ensure that the software can handle large data sets and process them efficiently without sacrificing performance or accuracy.
Versatility
Another essential factor to consider is the versatility of the tool. Can it classify data across different file types, including PDFs, CSVs,.xls files,.doc files,.txt files, and any other file types used in your organization? Additionally, does the tool work across multiple systems, such as cloud repositories, Windows and macOS platforms, file servers, USB drives, and other systems used in your organization? A tool that is limited in its ability to classify data across different file types and systems may not be the best choice for your organization.
Overhead
You’ll also want to consider the potential impact of the data classification tool on your daily operations. Will it help reduce problems or create new ones? For example, will it automate processes and simplify data management, or will it introduce complexity and require additional resources to implement and manage? It’s essential to choose a tool that will streamline your operations and reduce the risk of human error.
Visibility
A data classification tool should provide a centralized console, enabling you to easily manage your data, track its movement, and respond quickly to changes or incidents. Furthermore, the tool should provide guidance for regulatory compliance, offer around-the-clock monitoring of incoming data, automate the implementation of your organization’s security protocols, and integrate with your business to become part of your overarching data security strategy.
Best Practices for Effective Data Classification
Below are five best practices for classifying data:
1. Establish clear classification policies
Establishing clear classification policies and guidelines helps to ensure that data is consistently classified and processed according to the organization’s requirements and regulatory requirements.
2. Involve stakeholders across departments
Involve stakeholders across departments to ensure that classification policies are aligned with business needs and that data is properly classified and managed across the organization.
3. Provide regular training and awareness programs
Providing regular training and awareness programs helps to ensure that employees understand the classification policies and guidelines, and are aware of the importance of accurate data classification for data security and compliance.
4. Implement ongoing monitoring and review processes
Implementing ongoing monitoring and review processes enables the organization to identify and address any deviations from the classification policies, and to ensure that data is properly monitored and reviewed for compliance and security.
5. Continuously refine and update classification criteria
Continuously refining and updating classification criteria ensures that data is properly classified as new data types and categories emerge, and that the organization’s classification policies remain relevant and effective in protecting sensitive data.
How Lepide Helps with Data Classification
The Lepide Data Security Platform provides a built-in data classification tool which uses advanced algorithms to identify and classify sensitive data in various formats such as files, emails, and databases. Lepide’s data classification software uses a combination of classification techniques, including rule-based classification, machine learning, and human-driven classification, to organize data into relevant categories, such as sensitive, confidential, or public. The tool offers a comprehensive library of pre-defined criteria sets for various sensitive data types and compliance standards. This makes it easy to identify and protect sensitive data, ensuring compliance with regulations like HIPAA, SOX, PCI, GDPR, and CCPA.
Lepide’s solution also provides:
- Easy detection of sensitive data breaches, such as GDPR file copying or unauthorized access to health records
- Automated threat response using pre-defined threat models
- Accurate detection of false positives through proximity scanning and contextual analysis
- Effective governance of access to sensitive data, including identifying excessive permissions and controlling access
- Real-time alerts and reporting on user behavior related to sensitive data, enabling prompt detection of risky activities
