In the complex and rapidly evolving field of data security, accurate terminology is more than semantics—it defines how organizations understand, manage, and protect their information. However, many vendors blur the lines between terms like “data classification,” “categorization” and “identifiers,” often misusing them interchangeably. This confusion obscures the true value of advanced solutions, hindering businesses from achieving comprehensive data security.
In this article, I will clarify the differences between traditional data classification, context-driven categorization and subcategorization, and address how some vendors’ misapplication of terms like “data classes” and “identifiers” perpetuates outdated methodologies.
Misunderstanding Classification vs. Categorization
With traditional data classification, many vendors describe their data solutions as providing “classification,” but what they often mean is basic file labeling. This involves applying tags like “Confidential,” “PII” or “Public” to files based on simple rules or regex. This includes predefined patterns such as email addresses or credit card numbers which trigger specific labels; file metadata where labels are assigned based on attributes like file type or storage location; and manual tagging where users manually apply labels, which is resource-intensive and prone to error.
While these approaches can meet basic regulatory requirements, they fall short in modern environments due to their inability to analyze data context or relationships.
On the other hand, when it comes to categorization and subcategorization, true categorization goes far beyond labeling. It involves semantic understanding, where AI can automatically discover and organize data into meaningful categories and subcategories. These include high-level categories like “customer data” or “intellectual property,” and subcategories such as “contracts,” “blueprints” or “marketing plans.”
Categorization not only identifies the type of data but also understands its role, context, and significance within the organization, enabling proactive risk management.
The Problem with Interchanging Terms
Some vendors compound the confusion by using terms like “data classes” or “identifiers” interchangeably with classification and categorization. Here’s why this is problematic:
When “data classes” is misused as categories, “data classes” should refer to broad groupings like “personal data” or “financial data.” Vendors sometimes use “data classes” as a catch-all term for both categories and their subsets, masking the need for nuanced classification that reflects organizational context.
Overemphasized identifiers are a problem because “identifiers” are specific patterns (e.g., social security numbers, credit card numbers) that are easily recognized by regex or keyword-based tools. Some vendors claim comprehensive classification by merely identifying these patterns, ignoring data that lacks obvious markers, such as intellectual property or strategic documents.
As a result, these practices perpetuate a narrow, surface-level view of data security that fails to address the challenges of unstructured data or complex regulatory environments.
Why Regex and Rule-Based Systems Fall Short
Many vendors still rely heavily on regex (regular expressions) or manual rule creation to classify data. While these methods can identify structured data with specific patterns, they struggle with unstructured data since documents, emails, and multimedia files often lack the consistent patterns regex relies on. In addition, manually updating rules to reflect new data types or regulatory changes makes them resource intensive in dynamic environments. These methods also result in false positives and negatives because without context these systems frequently misclassify or miss critical data.
For example, a regex-based tool might identify a string of numbers as a credit card but overlook its actual role in a financial report, missing the broader context and associated risks.
Semantic Intelligence: The Context Revolution
Semantic intelligence offers a solution to these limitations by combining contextual understanding with automation. It transforms data management in a number of ways.
For example, semantic intelligence understands data’s role beyond identifiers. Unlike tools fixated on identifiers, semantic intelligence interprets the meaning and usage of data. A document titled “Project Scope” is recognized as strategic business data, even without explicit patterns.
Semantic intelligence also offers categorization with depth. Instead of lumping files into broad data classes, it dynamically categorizes and subcategorizes data. For instance, for a category of customer data the subcategory could include contracts, purchase orders, or correspondence.
In addition, semantic intelligence delivers dynamic adaptation, as AI continuously learns and adapts to ensure that classifications evolve with the organization’s data and regulatory landscape. Finally, semantic systems analyze structured and unstructured data across cloud and on-premises environments, offering comprehensive coverage and complete visibility without manual effort.
The True Impact of Misused Terminology
When vendors misuse terms like “data classification” or “identifiers,” they create confusion that can lead to:
- Overestimated capabilities where businesses may believe they’re achieving comprehensive security when, in reality, they’re addressing only surface-level issues.
- Compliance risks since inadequate classification methods can lead to missed compliance requirements, especially as regulations grow stricter.
- Missed opportunities as organizations fail to unlock the full potential of their data, as traditional tools lack the depth to uncover meaningful insights.
What Businesses Should Seek Out
Organizations can leverage semantic intelligence to eliminate the limitations of traditional classification and discovery tools for autonomous, context-driven categorization. Here are some of the capabilities you should look for when evaluating a data security platform:
Instead of regex or rules, look for solutions that use advanced AI to dynamically understand data without predefined patterns to deliver fast and accurate results;
- Rich contextual categorization of data capabilities at multiple levels, from high-level classes to granular subcategories, ensuring actionable insights;
- Adaptable and scalable solutions that can handle petabyte-scale environments, analyzing structured and unstructured data with ease; and
- Proactive risk management functionality to flag risks like overly permissive sharing or misplaced sensitive data, enabling immediate remediation.
Conclusion: Clarity Leads to Security
In data security, precision matters. Misusing terms like “classification,” “categorization,” “data classes,” and “identifiers” obscures critical distinctions that impact organizational security. By embracing semantic intelligence and context-driven categorization, businesses can move beyond labels and regex to achieve a holistic understanding of their data.
