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Data classification is the critical process of tagging data according to its type, sensitivity, and value to the organization. This helps organizations understand the value of their data, determine whether the data is at risk, and implement controls to mitigate risks. Data classification also helps organizations comply with relevant industry-specific regulatory mandates such as SOX, HIPAA, PCI DSS, and GDPR.
One of the key challenges in implementing data privacy is nailing down discovery and classification first. This is where Data Sentinel comes in. Our proprietary deep learning discovery technology illuminates the true nature of an organization's data across all sources and systems, monitoring, measuring, and remediating the data to ensure compliance with company policies and evolving data management and privacy regulations.
There are several methods for classifying data, each with their own set of advantages and disadvantages. In this article, we will discuss the most common data classification techniques and best practices for implementing them in your organization.
Data classification is important for several reasons. Firstly, it helps organizations to identify and protect sensitive information from unauthorized access or breaches. This is especially important in today's digital landscape, where data breaches and cyber-attacks are becoming more common.
Secondly, data classification helps organizations to comply with industry regulations and laws, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). By classifying and protecting sensitive data, organizations can ensure that they are meeting the requirements set out by these regulations.
Finally, data classification can improve overall productivity and efficiency within an organization. By having a clear understanding of the different types of data and how they should be handled, employees can more effectively access, share, and use the data they need to do their job.
Data classification can be a complex and time-consuming process, especially for organizations with large amounts of data. Some of the challenges organizations face include:
- Identifying sensitive data: With the sheer volume of data that organizations collect, it can be difficult to identify which data is sensitive and requires protection.
- Classifying data accurately: It's not always clear how to classify data, and mistakes can lead to data breaches or other security incidents.
- Keeping data classification up to date: As data changes, the classification should be reviewed and updated as needed to ensure it remains accurate.
- Maintaining compliance: Organizations must comply with various regulations and standards, such as HIPAA and PCI-DSS, that have specific requirements for data classification and protection.
Data classification has several key purposes, including:
Risk Mitigation: By limiting access to personally identifiable information (PII) and controlling location and access to intellectual property (IP), data classification can reduce the attack surface area for sensitive data. Additionally, by integrating classification into data loss prevention (DLP) and other policy-enforcing applications, organizations can better protect their data.
Governance/Compliance: Data classification can help organizations identify data that is governed by regulations such as GDPR, HIPAA, CCPA, PCI, SOX, and future regulations. By applying metadata tags to protected data, organizations can enable additional tracking and controls, as well as facilitate actions such as quarantining, legal hold, archiving, and Data Subject Access Requests (DSARs).
Efficiency and Optimization: Data classification can enable efficient access to content based on type, usage, etc. It can also help organizations discover and eliminate stale or redundant data, as well as move heavily utilized data to faster devices or cloud-based infrastructure.
Analytics: By enabling metadata tagging, data classification can help organizations optimize business activities and gain insights into the location and usage of their data.
The data classification process typically involves the following steps:
- Identify the purpose of the data classification process, including what types of data need to be classified and why.
- Determine which systems are in scope for the initial classification phase.
- Identify any compliance regulations that apply to the organization.
- Identify any other business objectives that need to be addressed through data classification.
- Identify the different types of data that the organization creates and manages.
- Delineate proprietary data vs. public data.
- Identify any data that is governed by regulations such as GDPR, CCPA, or other regulations.
- Determine how many classification levels are needed.
- Document each level and provide examples.
- Train users on how to classify data if manual classification is used.
- Determine who will have access to classified data and what level of access they will have.
- Establish policies for how data can be shared
Data sensitivity levels are used to classify data based on the potential impact of its unauthorized disclosure, alteration, or destruction. Common sensitivity levels include:
Low sensitivity data | Public: Data that is not considered sensitive and can be freely shared with anyone.
Medium sensitivity | Internal: Intended for internal use only, but if compromised or destroyed, would not have a catastrophic impact on the organization or individuals. Examples include emails and documents with no confidential data.
High sensitivity data | Confidential: Data that is considered sensitive and should only be shared on a need-to-know basis.
Extreme sensitivity data | Highly Confidential: Data that is considered extremely sensitive and should only be accessed by a small number of authorized individuals. If compromised or destroyed in an unauthorized transaction, would have a catastrophic impact on the organization or individuals. Examples include financial records, intellectual property, and authentication data.
To effectively classify and manage sensitive data, follow best practices such as:
- Regularly reviewing and updating data classification levels to ensure they are accurate and up-to-date.
- Implementing access controls to restrict access to sensitive data to only authorized individuals.
- Regularly monitoring and auditing data access to detect and prevent unauthorized access.
- Providing training and education to employees on data classification and data protection best practices.
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While the high, medium, and low labels are somewhat generic, a best practice is to use labels for each sensitivity level that make sense for your organization. Data Sentinel's platform allows for custom labels and categorization to fit the unique needs of each business.
Additionally, it's important to regularly review and update your data classification system to ensure it remains accurate and relevant. Data Sentinel's continuous monitoring and remediation capabilities ensure that your data classification system is always up-to-date.
Data classification can be performed based on content, context, or user selections:
Content-based classification — refers to the process of categorizing data based on its meaning or content. This can include things like text classification, image classification, and video classification.
Context-based classification — involves classifying files based on meta data such as the application that created the file, the person who created the document, or the location in which files were authored or modified.
User-based classification — involves classifying files according to a manual judgement of a knowledgeable user. Individuals who work with documents can specify how sensitive they are, and can do so when they create the document, after a significant edit or review, or before the document is released.
Data states and formats refer to the different ways in which data can be represented or stored.
Data states — data exists in one of three states: at rest, in process, or in transit. Regardless of state, data classified as confidential must remain confidential.
Data format — data can be either structured or unstructured. Structured data is usually human readable and can be indexed, while unstructured data is usually not easily readable or indexable. Classifying structured data is less complex and time-consuming than classifying unstructured data. For example, data can be stored in structured formats like tables or unstructured formats like text documents. Data can also be represented in different forms such as numerical values, text, images, or videos.
So, the classification process and data states/formats can be related in the sense that the classification process determines how the data is grouped, and the data states/formats determine how the data is represented or stored.
A data classification policy is a set of guidelines that outlines how an organization handles and classifies its data. The policy should address the types of data the organization handles, the sensitivity levels of the data, and who is responsible for classifying the data. The policy should also outline how data will be classified, how access to sensitive data will be controlled, and how data classification will be monitored and audited.
1. Establish a clear data classification policy: Organizations should establish a clear data classification policy that outlines the method for classifying data, the responsibilities of employees, and the consequences for non-compliance.
2. Involve all relevant stakeholders: The process of classifying data should involve all relevant stakeholders, including IT, legal, and business departments. This ensures that all perspectives are considered and that the classification is accurate and relevant.
3. Provide employee training: Employees should be trained on the data classification policy, including how to classify data and handle sensitive information.
4. Regularly review and update the classification: Data classification should be reviewed and updated regularly to ensure that it remains accurate and relevant.
5. Implement appropriate security controls: Once data has been classified, appropriate security controls should be implemented to protect it, such as encryption, access controls, and monitoring.
In conclusion, data classification is an important aspect of data management and security. It helps organizations ensure that sensitive information is handled and protected appropriately. A data classification policy outlines the responsibilities, methods, and guidelines for classifying and protecting data. Effective implementation of a data classification policy requires involvement of all relevant stakeholders, employee training, regular reviews, and implementation of appropriate security controls. By establishing a clear and comprehensive data classification policy, organizations can better manage and protect their sensitive information, reducing the risk of data breaches and unauthorized access.
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