Data classification is the process of organizing data into categories that make it easy to retrieve, sort and store for future use. A well-planned data classification system makes essential data easy to find and retrieve. This can be of particular importance for risk management, legal discovery and regulatory compliance.
Written procedures and guidelines for data classification policies should define what categories and criteria the organization will use to classify data. They also specify the roles and responsibilities of employees within the organization regarding data stewardship.
Once a data classification scheme is created, security standards should be identified that specify appropriate data handling practices for each category. Storage standards that define the data's lifecycle requirements must be addressed as well.
Systematic classification of data helps organizations manipulate, track and analyze individual pieces of data. Data professionals often have a specific goal when categorizing data. The goal affects the approach they take and the classification levels and definitions they use.
Some common business goals for data classification projects include the following:
Data classification is an important part of data lifecycle management that specifies which standard category or grouping a data object should be assigned to. Once sorted, data classification can help ensure an organization adheres to its data handling guidelines, and to local, state and federal compliance regulations, such as the Health Insurance Portability and Accountability Act, or HIPAA, and the Federal Information Processing Standard that the National Institute of Standards and Technology oversees. Companies in highly regulated industries often implement data classification processes or workflows to aid in compliance audit and data discovery processes.
Data classification is typically used to categorize structured data, but it is especially important when applied to unstructured data. Unstructured data lacks clear labels, so classification makes this data more usable and easier to search or query. Data categorization also helps identify duplicate copies of data. Eliminating redundant data contributes to efficient use of storage and maximizes data security measures.
Not all data needs to be classified. In some cases, it isn't necessary to retain data, so destroying it is the prudent course of action. Understanding why data needs to be classified is an important part of the process.
Steps involved in developing a comprehensive set of policies to govern data include the following:
Standard data classification levels or categories include the following:
A number of different category lists can be applied to the information in a system. These lists of qualifications are also known as data classification schemes. For example, one way to classify data's level of sensitivity might include classes such as secret, confidential, business use only and public.
An organization might also use a system that classifies information based on the type of content in files, looking for certain common characteristics. For example, context-based classification examines applications, users, geographic location and creator info. User classification is based on what an end user chooses to create, edit and review.
In computer programming, file parsing is a method of splitting data packets into smaller subpackets that are easier to move, manipulate, categorize and sort. Different parsing styles determine how a system incorporates information. For instance, dates are split up by day, month or year, and words might be separated by spaces.
Some standard approaches to data classification using parsing include the following:
Various tools are used in data classification, including databases, data management systems and business intelligence software. Some examples of BI software tools that help simplify data classification include Databox, Google Looker Studio and SAP Lumira.
Developers and data scientists use these tools to pull specific kinds of data to complete classification tasks faster. Other methods can be used to assist in applying data classification. For example, a regular expression is an equation used to quickly pull data that fits a certain category, making it easier to categorize all information that falls within those particular parameters.
Data classification methods are useful to an organization for multiple reasons:
Data classification that's conducted with enough specificity ensures an organization pinpoints which data sets are public, confidential, sensitive and why. Classification lets an organization apply the proper security tools, such as encryption, access controls or data loss prevention, to ensure that restricted data isn't accessible to the wrong audiences and can't be tampered with. Additionally, classification ensures a trail documenting how data is used.
For unstructured data in particular, data classification makes it less vulnerable to breaches. For example, merchants and other businesses that accept credit cards are expected to comply with the data classification and other Payment Card Industry's Data Security Standards. PCI DSS is a set of 12 security requirements aimed at safeguarding customer financial information.
The European Union (EU) adopted the General Data Protection Regulation (GDPR) in 2016. The GDPR is a set of international guidelines created to help ensure that companies and institutions handle confidential and sensitive data carefully and respectfully. The regulation went into effect in early 2018. It's made up of seven guiding principles: fairness, limited scope, minimized data, accuracy, storage limitations, rights and integrity. The GDPR prescribes stiff penalties for not complying with these standards.
Implementing methodical data classification is a necessity to comply with the many parts of GDPR. It requires organizations handling data on EU citizens to assign specific security control levels to it to prevent unauthorized access or disclosure. Classifying data helps data security teams identify data that requires anonymization or encryption.
Another aspect of GDPR that requires effective data classification is that it gives individuals the right to access, change and delete their personal data. Data classification makes it possible for companies to quickly retrieve such data and fulfill a person's specific request.
To keep data classification systems as efficient as possible, it's important for an organization to continuously update the classification systems it uses. It might be necessary to reassign the values, ranges and outputs of these systems to more effectively meet the organization's evolving classification goals. There are a number of reasons why a business would need to engage in reclassification, including ensuring accuracy, mitigating risks, addressing security and cybersecurity concerns, and complying with local, state and federal regulations.
Implementing a policy to codify periodic reviews of data classification is a sound strategy to achieve this. Employees or managers delegated with data ownership can work with security and compliance officers to develop and enforce such a policy. It should address both internal changes and evolving compliance standards that would warrant data reclassification. It should also introduce new data categories as needed.
Data governance is important for organizations using data as part of their business. Find out more about data governance and how it lowers data risk, ensuring data is consistent, trustworthy and not misused.
15 Apr 2024