Data classification models provide organizations with a standard template for representing how data should be organized, categorized, and identified. Combined with statistical methods, they are valuable artifacts for data analytics and training automated systems, such as the algorithms used in machine learning applications.
What Is a Data Classification Model?
A data classification model is a framework used in IT-based systems to classify data into specific categories or classes to enhance data security and protection.
A classification model also helps predict the class or category for new data points. We’ll examine how organizations classify data based on their secrecy, sensitivity, or transparency degree.
Why Is It So Hard to Get a Data Classification Model?
Creating an effective data classification model can be challenging for several reasons:
Complexity of Data: Real-world data can be very complex, often with many features that interact unpredictably. The model needs to capture these relationships accurately.
Data Quality: If the data is full of errors, incomplete, or outdated, it can be challenging to build a reliable and accurate model. The model is only as good as the data it's built on.
Choosing the Right Model: Not all models are suitable for all types of tasks, and the choice of model can significantly impact the results. Identifying the most appropriate model requires a deep understanding of the problem and the model's assumptions.
Labeling Data: Each piece of data needs to be correctly labeled for supervised learning models. This can be time-consuming and requires domain expertise.
Overfitting and Underfitting: Striking the right balance between model complexity and accuracy is challenging. A model that is too complex may overfit the training data and perform poorly on new data.
Conversely, a model that is too simple may underfit the data and fail to capture essential patterns.
Feature Selection: Deciding which features to include in the model can be challenging. Irrelevant or redundant features can reduce the model's performance.
Computational Resources: Training complex models can require substantial computational power and time, especially for large datasets.
Evaluation: Assessing the model’s performance can be tricky, especially when the data is unbalanced, or there are many classes to predict.
Legal and Privacy Constraints: In certain industries, the use of certain types of data for classification can be legally restricted, making it harder to develop effective models.
Constant Maintenance: This requires resources and technical expertise. The model must be regularly updated and fine-tuned as new data enters the system and the environment changes.
How to Choose the Right Data Classification Model
Choosing the right data classification model depends on various factors. Here are some steps you can take:
- Understand the Problem: Assess the nature and type of data you are dealing with and what problem you are trying to solve. Some models work best with specific data types, like binary or continuous, and different types of problems, like detection, prediction, etc.
- Verify Data Quality: Ensure the dataset's quality is acceptable before choosing the model. Handle missing values, outliers, and erroneous data. Good data quality can improve the model's performance.
- Consider the Size and Type of Data: Some models are better suited for large datasets, while others work well with smaller datasets. Similarly, each model handles numerical, categorical, and text data differently.
- Analyze the Features: Understanding the relationship between features can guide model selection. Some models, like linear regression, are best for linearly separable data. In contrast, others, like support vector machines or neural networks, handle non-linearly separable data more effectively.
- Experiment: Finally, do not hesitate to try out multiple models and compare their performance. Sometimes, ensemble methods combine multiple models and offer the best performance.
Common Data Classification Models
Data classification models aren’t set in stone and are formulated at the discretion of each organization. Organizations such as educational institutions employ different data definitions that suit their needs, so there isn’t a standard model.
Data is typically classified based on its sensitivity levels, which ultimately influence the classification models in which it is classified.
Low Sensitivity Data
This consists of data available in the public domain. As such, it is low sensitivity to an organization because it doesn’t require discretion or protection. While the data disclosure might sometimes embarrass and inconvenience the organization, it poses no significant legal or financial risk.
This is because it primarily consists of non-sensitive, personally identifiable information (PII).
Examples of low-sensitivity data are public information like the following:
- A person’s first and last names
- An individual’s date of birth
- Contact addresses, email addresses, and phone numbers
- Company names, including the names of founders and board members
- Company’s date of incorporation
- Organization charts
- Job descriptions and job postings
- Vehicle license plate numbers
Public data has the lowest level of sensitivity because it can be easily accessed, shared, and distributed without imperiling the privacy, security, or confidentiality of those involved.
On the other hand, it’s wise to shield certain categories of what may be deemed private data from the public. This is because revealing this data may expose the confidentiality or privacy of those involved. A typical example includes geolocation data or eliminating the masking features that provide anonymizing protection to research subjects.
Here are some examples of private data that fall into such category:
- An individual’s online browsing history
- Geolocation data
- Employee or student identification numbers
Moderate Sensitivity Data
Organizations also have private data they must safeguard to maintain their competitive advantage.
This data category's illegal leakage or unauthorized disclosure to the public poses a moderate risk to the impacted organization. It typically impacts an organization’s assets, such as employees, customers, and operations.
Therefore, a data breach at this level will likely adversely affect an organization. Although a wide range of users can usually access this data, it is typically designated as moderately sensitivity and protected by internal access controls.
- Proprietary data includes intellectual property such as business secrets, trade secrets, and business processes that an organization uses to maintain its competitive advantage.
- Strict contractual agreements containing business details.
- Internal-only data, such as company emails, memos, and other communications, are restricted to its employees.
High sensitivity data
As this categorization warrants, highly sensitive data must be strictly secured because of the danger it poses. Consequently, organizations must protect highly sensitive data due to the severe and catastrophic impact of its breach on customers, assets, and operations.
Moreover, unauthorized disclosure of sensitive information can damage an organization’s reputation and revenue engine.
High-sensitivity data are typically divided into confidential and restricted data, although they are often used interchangeably.
Confidential
- Medical health records and similar protected health information (PHI)
Social security numbers - Banking details and financial records
- Cardholder account information and related transaction records
- Credit card information, including PIN numbers, CSV numbers, and expiration dates
- Biometric identification, such as fingerprints and retina scans.
Restricted data
This data designation is commonly used in government agencies. Restricted data typically requires only those with authorization to access it, such as having a government security clearance.
U.S. national classification scheme
Due to its mandate to preserve order, protect the nation’s secrets, and safeguard national security, the government is incentivized to maintain comprehensive data classification models.
Under the U.S. government's national classification scheme, high-sensitivity data is likely to fall under the following categories:
- Confidential: Unauthorized disclosure of information will likely damage national security.
- Secret: This data breach could reasonably lead to severe damage to national security.
- Top secret: Unauthorized disclosure of this information will likely cause exceptionally grave damage to national security interests.
In its peculiar classification model, the United States government refers to data that doesn’t fall within the above three categories as unclassified.
District of Columbia Data Policy
Unlike the U.S. government’s information classification standards, Washington D.C. implements a five-tier model listed below:
- Open data
This designated Level 0 data represents publicly available information on government websites without logins or access restrictions. - Public data
Although Level 1 is public data, unlike Level 0, it isn’t proactively released. Nevertheless, this data isn’t protected from the public nor subject to any regulation, law, or contract. However, the exposure or publication of this data on public platforms like the Internet could jeopardize the privacy, safety, and security of those identified in it. - For government use
Although this Level 2 data isn’t highly sensitive, it belongs to a category meant for daily government operations. Therefore, it may be distributed within government circles without restriction. - Confidential
Confidential data is protected and restricted from disclosure to the public by law because Level 3 data is privacy-related. Examples of this data category include personally identifiable information (PII), PHI, payment card industry data security standard (PCI DSS), and federal tax information (FTI). - Restricted confidential
This is the highest data restriction. Confidential data is protected from illegal disclosure by law because it is highly sensitive and could cause injury and damage, including the death of those identified.
Furthermore, its unauthorized disclosure can seriously downgrade and impair the operational capabilities of the agency impacted, making it unable to perform its statutory functions.
Explore Fortra’s Data Classification Solutions
When it comes to data classification, there's no one-size-fits-all model. Therefore, choosing a suitable model depends on the problem, the nature of the data, and other factors.
At Fortra, we understand the unique features of various statistical models and how they can be used to solve different types of data classification problems.
Request a demo today to observe our expertise in action.
