Data classification is the process of organizing data into relevant categories to make it simpler to retrieve, sort, use, store, and protect.
A data classification policy, properly executed, makes the process of finding and retrieving critical data easier. This is important for risk management, legal discovery, and regulatory compliance. When creating written procedures and guidelines to govern data classification policies, it is critical to define the criteria and categories the organization will use to classify data.
Data classification can help make data more easily searchable and trackable. This is achieved by tagging the data. Data tagging allows organizations to label data clearly so that it is easy to find and identify. Tags also help you to manage data better and identify risks more readily. A data tag also enables it to be processed automatically and ensures timely and reliable access to data, as required by some state and federal regulations.
Most data classification projects help to eliminate duplication of data. By discovering and eliminating duplicate data, organizations can reduce storage and backup costs as well as reduce the risk of confidential data or sensitive data being exposed in case of a data breach.
Specifying data stewardship roles and responsibilities for employees inside the organization is part of data classification systems. Data stewardship is the tactical coordination and implementation of an organization's data assets, while data governance focuses on more high-level data policies and procedures.
Data classification increases data accessibility, enables organizations to meet regulatory compliance requirements more easily, and helps them to achieve business objectives. Often, organizations must ensure that data is searchable and retrievable within a specified timeframe. This requirement is impossible without robust classification processes for classifying data quickly and accurately.
To meet data security objectives, data classification is essential. Data classification facilitates appropriate security responses for data security based on the types of data being retrieved, copied, or transmitted. Without a data classification process, it is challenging to identify and appropriately protect sensitive data.
Data classification provides visibility into all data within an organization and enables it to use, analyze, and protect the vast quantities of data available through data collection. Effective data classification facilitates better protection for such data and promotes compliance with security policies.
Data classification tools are intended to provide data discovery capabilities; however, they often analyze data stores only for metadata or well-known identifiers. In complex environments, data discovery is ineffective if it can discover only dates but cannot identify whether they are a date of birth, a transaction date, or the dateline of an article. Without this additional information, these discovery tools cannot identify whether data is sensitive and therefore needs protection.
“The best DSPs will have semantic and contextual capabilities for data classification — judging what something really is, rather than relying on preconfigured identifiers.“ Gartner: 2023 Strategic Roadmap for Data Security Platform Adoption
Modern data security platforms must include semantic and contextual capabilities for data classification, to identify what a piece of data is rather than using preconfigured identifiers, which are less accurate and reliable. Because organizations are increasing the use of cloud computing services, more sensitive data is now in the cloud. However, a lot of the sensitive data is unstructured, which makes it harder to secure.
A data classification scheme enables you to identify security standards that specify appropriate handling practices for each data category. Storage standards that define the data's lifecycle requirements must be addressed as well. A data classification policy can help an organization achieve its data protection goals by applying data categories to external and internal data consistently.
Data discovery and inventory tools help organizations identify resources that contain high-risk data and sensitive data on endpoints and corporate network assets. These tools help organizations identify the locations of both sensitive structured data and unstructured data by analyzing hosts, database columns and rows, web applications, file shares, and storage networks.
Tagging or applying labels to data helps to classify data. This is an essential part of the data classification process. These tags and labels define the type of data, the degree of confidentiality, and the data integrity. The level of sensitivity is typically based on levels of importance or confidentiality, which aligns with the security measures applied to protect each classification level. Industry standards for data classification include three types:
While each approach has a place in data classification, user-based classification is a manual and time-consuming process, and extremely likely to be error-prone. It will not be effective at categorizing data at scale and may put protected data and restricted data at risk.
It is important for data classification efforts to include the determination of the relative risk associated with diverse types of data, how to manage that data, and where and how to store and send that data. There are three broad levels of risk for data and systems:
Automated tools can perform classification that defines personal data and highly sensitive data based on defined data classification levels. A platform that includes a classification engine can identify data stores that contain sensitive data in any file, table, or column in an environment. It can also provide ongoing protection by continuously scanning the environment to detect changes in the data landscape. New solutions can identify sensitive data and where it resides, as well as apply the context-based classification needed to decide how to protect it.
Classifying data as restricted, private, or public is an example of data classification. Like identifying risk levels, public data is the least-sensitive data and has the lowest security requirements. Restricted data receives the highest security classification, and it includes the most sensitive data, such as health data. A successful data classification process extends to include additional identification and tagging procedures to ensure data protection based on data sensitivity.
Security and risk leaders can only protect sensitive data and intellectual property if they know the data exists, where it is, why it is valuable, and who has access to use it. Data classification helps them to identify and protect corporate data, customer data, and personal data. Labeling data appropriately helps organizations to protect data and prevent unauthorized disclosure.
The General Data Protection Regulation (GDPR), among other data privacy and protection regulations, increases the importance of data classification for any organization that stores, transfers, or processes data. Classifying data helps ensure that anything covered by the GDPR is quickly identified so that appropriate security measures are in place. GDPR also increases protection for personal data related to racial or ethnic origin, political opinions, and religious or philosophical beliefs, and classifying these types of data can help to reduce the risk of compliance-related issues.
Organizations must meet the requirements of established frameworks, such as the GDPR, California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), Gramm-Leach-Bliley Act (GLBA), Health Information Technology for Economic and Clinical Health (HITECH), among others. To do so, they must evaluate sensitive structured and unstructured data posture across Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) environments and contextualize risk as it relates to security, privacy, and other regulatory frameworks.
Electronic Lab Notebooks (Electronic Laboratory Notebook or ELN) is the digital form of a paper lab notebook. In the pharmaceutical industry, it is used by researchers, scientists, and technicians to document observations, progress, and results from their experiments performed in a laboratory.
While ELN enables information to be documented and shared electronically, it also exposes proprietary information to malicious insiders or external hackers. As a result, ELN should be subject to appropriate security controls to prevent misuse or loss.
Defense Industrial Base (DIB) contractors are companies that conduct business with the US military and are part of the military industry complex responsible for research, production, delivery, and service.
DIB contractors are responsible for meeting compliance requirements set by government policies and frameworks including the the Department of Defense Instruction (DoDI) 5200.48 and Cybersecurity Maturity Model Certification.
According to DoDi 5200.48, safeguarding Controlled Unclassified Information is a shared responsibility between DIB contractors and the Department of Defense.