Since the beginning of business, data has been the backbone of operations. From sales and customer information to expenses and inventory, companies generate enormous amounts of data daily. Modern businesses rely on “data-driven” insights to make decisions, and managing that data effectively is more critical than ever.
Data generally falls into two categories: structured and unstructured. Structured data is neatly organized in databases or spreadsheets, making it easy to access and analyze. Unstructured data, however, exists in formats like emails, presentations, chat logs, and other qualitative content. It can be difficult to locate, secure, and organize, creating significant management challenges. This is where sensitive data discovery and data classification levels come into play.
Classifying data ensures unstructured information is organized, accessible, and manageable while also meeting compliance and security requirements.
What Is Data Classification?
Data classification is the process of organizing and storing data into categories based on specific criteria. The purpose of classification is to make data easier to locate, retrieve, and manage. By assigning categories and sensitivity levels, businesses can protect information more effectively and streamline operations.
What Are the Benefits of Classifying Data?
Proper data classification provides several key benefits:
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Support regulatory compliance: Laws like HIPAA, GDPR, and other regulations require certain standards for protecting sensitive data. Classification ensures your organization meets these requirements and adapts easily to new or revised rules.
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Reduce risk of data loss: Identifying and organizing private and sensitive data makes it easier to protect against breaches or accidental loss.
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Lower operational costs: Understanding and managing unstructured data efficiently saves on storage and retrieval costs.
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Empower employees: With organized data, employees spend less time searching and more time analyzing and optimizing data operations.
Four Types of Data Classification
While traditional approaches focus on context, content, and user, DryvIQ recommends adding risk-based classification for a more comprehensive approach.
Context-Based Classification
Context-based classification examines variables such as applications, locations, and data creators to identify sensitive information. It is particularly useful for large datasets originating from a single identifiable source.
Content-Based Classification
Content-based classification evaluates the files themselves, categorizing data based on type or sensitivity level, such as high, medium, or low.
User-Based Classification
User-based classification depends on manual selection by knowledgeable end-users. It can also include rules tied to specific users’ access levels and risk factors. While effective, it can be time-consuming without automation. DryvIQ leverages AI to automate much of this process.
Risk-Based Classification
Risk-based classification considers the sensitivity and potential impact of data exposure. It generally includes three levels:
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Low-risk data: Publicly available information, such as website content or press releases. Loss or exposure carries minimal impact.
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Medium-risk data: Internal data like budgets or operational documents. Loss is undesirable but not catastrophic.
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High-risk data: Highly sensitive information crucial to operations or security. Exposure could be devastating and may be difficult or impossible to recover.
Discover and Classify Your Data With DryvIQ
All organizations work with massive amounts of data, much of it unstructured. DryvIQ makes data classification simple and effective. By leveraging proprietary AI models, DryvIQ discovers and classifies unstructured data at scale, minimizing manual effort while maximizing security and accessibility.
Contact us today to see how DryvIQ can help your organization discover, classify, and protect sensitive data efficiently.
