Data Classification

What is Data Classification?
Data classification is the process of organizing data into categories or classifications based on certain characteristics. Data classification helps to organize data into meaningful subsets that can be used to make decisions, streamline processes, and protect sensitive information. It enables organizations to identify, classify, and manage data in order to reduce the risks associated with unauthorized access or inappropriate use of information.

What are the different types of data classification?

1. Unclassified: Data that is not labeled or categorized according to any particular criteria.
2. Public: Data that is available for anyone to access, use, and share.
3. Private: Data that is only available to a limited number of individuals or organizations and is usually kept confidential.
4. Confidential: Data that is sensitive and needs to be protected from unauthorized access and disclosure, usually requiring special measures such as encryption or authentication mechanisms.
5. Restricted: Data that has specific restrictions on its use, such as data related to national security or financial records.
6. Sensitive: Data that could potentially harm an individual if it were released without permission, such as medical records or personal information.
7. Protected: Data that is subject to specific legal or regulatory requirements, such as data related to copyright or intellectual property.

How to classify your data?

1. Group data into categories: Start by breaking down the data into distinct categories based on the criteria you are looking to classify. This could include grouping similar items together, dividing the data by age or location, or any other criteria that would help you group related items together.
2. Create a classification system: Once you have identified the categories, create a system to classify each item within its respective category. This could include assigning numerical values to each category or assigning words or symbols to each group of data.
3. Test and adjust: Once you have created your classification system, test it out on a sample of your data and make sure it accurately groups items accordingly. If needed, adjust your system as necessary in order to get more accurate results before applying it to the entire
4. Implement your system: Once you have a classification system that is accurate and easy to use, apply it to the entire dataset. This will help you quickly and easily identify patterns and draw conclusions from your data.

Best Practices in Data Classification

1. Establish a data classification policy: Establish a data classification policy that outlines the objectives and principles of the organization’s data classification system, and provides guidance on how to classify data.
2. Define Data Categories: Define categories of data based on the sensitivity, value, and criticality to the organization’s operations.
3. Educate Employees: Ensure all employees are trained on the importance of properly classifying their data and how to apply the correct labels or tags to it.
4. Automate Data Classification: Automate as much of the data classification process as possible to ensure consistency and accuracy in labeling.
5. Monitor Access Controls: Monitor access controls regularly to ensure only authorized personnel have access to sensitive data.
6. Revisit Classification Periodically: Periodically review and update data classification policies and labels to ensure they are still relevant and up to date.

List of Data Classification Tools

1. Axon Data Governance
2. Alation
3. IBM Guardium
4. Imperva SecureSphere
5. Symantec Data Loss Prevention
6. Forcepoint DLP
7. Boldon James Classifier Suite
8. Varonis Data Classification Framework
9. McAfee Total Protection for Data Loss Prevention (DLP)
10. Netwrix Data Classification

List of automated classification tools

1. Weka (Waikato Environment for Knowledge Analysis)
2. RapidMiner
3. KNIME (Konstanz Information Miner)
4. Orange
5. ELKI (Environment for Developing KDD-Applications Supported by Index-Structures)
6. ML-Flex
7. SVMlight
8. Scikit-Learn
9. TensorFlow
10. Apache Mahout