Data classification is a fundamental element of any robust data security strategy. The process involves identifying, categorizing, and protecting sensitive information across various locations within an organization’s digital landscape. Generally, this process involves scanning files and databases to look for specific keywords, which are then used to classify those objects as sensitive.
The origins of data classification trace back to government and military efforts, where the need to protect national security information led to the development of classification levels and systems.
Early classification systems were manual, relying on individuals to assess and classify documents based on their content and sensitivity.
Over time, as organizations amassed larger volumes of data and regulatory landscapes became more intricate, manual classification methods have become impractical—if not impossible.
Organizations started using semi-automatic tools that combined human oversight with software to identify certain types of sensitive information based on predefined criteria. However, these tools have limitations, particularly their reliance on extensive rule sets and the potential for human error.
Today, platforms like Cyera offer advanced AI capabilities for sensitive data discovery and classification, enabling organizations to navigate the complexities of data security and privacy with greater efficiency and precision. The introduction of AI and Large Language Models (LLMs) has marked a significant turning point in the field of data classification. Unlike traditional methods, AI-powered classification systems can understand and interpret data contextually, much like a human analyst. These systems leverage vast amounts of training data to develop a nuanced understanding of language, patterns, and context, enabling them to classify data and assess its sensitivity with unprecedented precision.
Unlocking the Value of Data Classification
Security and risk leaders can only protect sensitive data if they know the data exists, where it is, why it’s valuable, and who can use it. Data discovery and classification helps you do just that, enabling the protection of corporate, customer, and personal data.
Increasing Data Visibility
Precise data classification facilitates better protection of data and promotes greater clarity into the implementation of security controls. This is done by providing visibility into all data within your organization as well as what security measures are in place to protect it. This enables you to meet data security objectives. Without a data classification process, it’s challenging to identify and appropriately protect sensitive data.
Reducing the Data Attack Surface
Successful data classification projects help eliminate the duplication of data. By discovering and eliminating duplicate data, organizations can reduce storage and backup costs while reducing their data attack surface, minimizing the risk of confidential information or sensitive data being exposed in case of a data breach.
Meeting Compliance Requirements
Organizations must meet the requirements of established frameworks, laws, and regulations, such as the General Data Protection Regulation (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.
The GDPR, among other data privacy and protection regulations, increases the importance of data classification for any organization that stores, transfers, or processes personal data. Classifying data helps ensure that any personal data in scope of the GDPR is quickly identified to determine if appropriate security measures are in place.
GDPR also requires specific stipulations for sensitive personal data related to racial or ethnic origin, political opinions, and religious or philosophical beliefs, and classifying these types of data can help reduce the risk of compliance-related issues.
Additionally, organizations must ensure that personal data is findable and retrievable in the case of a data subject access request. And if an organization is hit with a data breach, they need a quick way to determine exactly what data was impacted. Meeting these needs is impossible without robust classification processes for understanding data quickly and accurately.
Identifying Sensitive Data: What Needs Classification?
Data classification involves categorizing data based on the level of sensitivity and impact the unauthorized disclosure of highly sensitive data could have on the organization or individuals involved.
Here are some key types of data that organizations typically need to classify:
- Personal Identifiable Information (PII): This includes any private and public data that can be used on its own or with other information to identify, contact, or locate a single person. Examples of PII are names, addresses, phone numbers, Social Security numbers, email addresses, and more. Given its nature, PII is highly sensitive and requires stringent protection measures.
- Financial information: This category details financial status or activities. It includes bank account numbers, credit card numbers, transaction history, and credit scores. Financial information is not only sensitive because of its nature but also because of its potential for financial fraud and identity theft.
- Health information: Protected Health Information (PHI) under HIPAA in the United States, or similar categories under other jurisdictions, includes any confidential data about health status, provision of health care, or payment for health care that can be linked to an individual. This can range from medical records to laboratory test results and insurance information.
- Intellectual Property (IP): This refers to creations of the mind, such as inventions, literary and artistic works, designs, symbols, names, and images used in commerce. For organizations, IP is a valuable asset that needs to be protected from theft, infringement, or unauthorized disclosure.
- Corporate information: Beyond public information, this includes trade secrets, internal strategies, forecasts, methodologies, and any other proprietary information that offers a competitive edge. Unauthorized access to corporate information can lead to significant financial loss and damage to a company’s reputation.
Navigating Challenges in Data Classification
Data classification can present a myriad of challenges for organizations. These challenges range from the sheer volume of data to the evolving nature of data types and the critical need for maintaining classification precision.
Volume of Data
With the vast amounts of information generated every second, manually classifying each piece is an impossible mission. Add to this the diverse sources and repositories where data resides, making comprehensive coverage in classification efforts difficult.
A data security platform with data discovery and classification capabilities is highly recommended for handling large volumes of data across multiple environments.
Unstructured Data
Because organizations are increasingly using cloud computing services, more sensitive data is now in the cloud. However, much of the sensitive data is unstructured, making it harder to classify and secure. This unstructured data comes in many forms, such as email, text documents, and chat messages for example.
Evolving Data Types
The continuous evolution of data types—ranging from structured data in databases to unstructured data in documents, images, and other formats—make it harder to discover, classify, and understand data.
Each type of data requires different handling and classification strategies, complicating the process for organizations trying to keep their head above water.
As data types evolve, so too must the approaches to classification, necessitating flexible and adaptable frameworks that can accommodate new forms of data.
Maintaining Classification Accuracy
Ensuring accuracy in data classification is pivotal for data integrity and regulatory compliance. Mislabeling assets can lead to inadequate protection measures for sensitive data or unnecessarily stringent security controls on non-sensitive data.
Inaccuracy in data classification not only undermines information security but can also impact business operations, leading to employees that struggle to access the data they need to do their jobs or security teams consuming resources to protect data that doesn’t require high levels of security.
“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 protection tools must include semantic and contextual capabilities for data classification to identify what a piece of data is rather than using pre-configured identifiers, which are less accurate and reliable. Traditional data protection solutions rely heavily on static content-based detection algorithms, such as regular expressions (regex). While weak patterns, like names and addresses, often produce poor results, even stronger patterns (such as credit card numbers) may yield only acceptable results, still prone to false positives without additional context. This lack of contextual understanding significantly limits the accuracy of static detection methods, underscoring the need for more sophisticated solutions that can interpret data within its specific context.
Strategies for Effective Data Classification
While the challenges of data classification are significant, they’re not insurmountable. Organizations must adopt a pragmatic and strategic approach to data classification to navigate the challenges. Investing in automation and technology solutions can significantly enhance the efficiency and accuracy of data classification. Leverage tools and technological advancements in machine learning and artificial intelligence to help manage the volume of data and ensure classification accuracy, all while automating the classification process.
Cyera’s AI and LLMs: Achieving High Data Classification Accuracy
Cyera’s AI and Large Language Models (LLM) are meticulously designed to achieve exceptional accuracy in data classification. Once the models are trained —an ongoing process—they classify data by analyzing database metadata, file contents, and other contextual information. Cyera ensures that only high-precision classifications, supported by a large amount of training data, are presented within the platform, minimizing the risk of false positives. The journey to this high level of precision begins with the foundational architecture of Cyera’s proprietary models.
Discover how powerful Cyera’s data classification can be. Schedule a demo today or read the 'Redefining Data Classification' white paper now.
