Data Classification: Definition, Examples, and More
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Data security has gained utmost relevance in the wake of the CoVID-19 remote working culture. The precipitous amount of data that is in transit or stored at individual and corporate levels is spiraling as the whole world goes digital.
Companies generate around 2,000,000,000,000,000,000 bytes of data a day &
94% of enterprises say data is essential to business growth, but
63% of companies can’t gather insights from big data
Information overload is costing enterprises billions of dollars. With more data comes more responsibility!
Information security which was once taken for granted, is now the most critical aspect as ‘remote’ becomes the new norm of personal and business transactions. Data of all sizes and confidentiality levels are now floating on clouds, so to speak, as compared to being secured in private workstations previously.
Automation, cloud storage, and data-driven digital access technologies have enhanced efficiencies and improved operations. However, the unwieldy volume of information makes it difficult to protect sensitive data. With data safety at the center of user and business transactions and data management at the core of all operations, it is no surprise that companies require a strong and secure data classification system.
What is Data Classification?
Data classification is defined as the process by which structured and unstructured data from various documents is segregated into categories defined on the basis of file size, type, source, location, content, and more.
Data classification enables organizations to segregate their existing database into multiple subsections, making it easier to view the data and improve searchability, much like the windows group by action.
If your company is handling excessive data in business transactions, personal documentation, and emails, it is important to categorize it in terms of information sensitivity. Critical data must be secure and protected at all costs. Data Classification tools protect data by sorting them with appropriate security labels. The simplest solution is to automate existing data and use metadata to track and organize new information.
Data on your file servers and virtual storage systems are usually not categorized, arranged, or labeled. This, more often than not, leads to duplication of information, loss or difficulty in locating data, and vulnerability to risk. However, if your data is organized, protected, and categorized for sharing internally and externally, your company will have control over the information in your systems. As a result, it will be easier for you to locate and retrieve data. It will also improve manpower efficiency by eliminating data loss or duplication.
At Nanonets, we have designed a secure data classification solution that can quickly integrate a strong data security culture into your company’s work ethos. Even as automation is being implemented, your employees will be trained to sort and classify data at the source so that you have a secure, efficient, and sustainable data classification system in place. Data is generally classified on the basis of risk sensitivity, type of information, and its value to the business.
Data classification tools will protect data by applying appropriate security labels, together with helping to educate users on how to treat different types of data with different levels of classification according to the relative level of sensitivity applied to that document.
What is data classification based on?
With the volume of information growing exponentially, businesses are going all out to maintain data confidentiality, integrity, and availability (the CIA triad). As more types of data from varied sources are generated, stored, and shared, companies worldwide also face the challenge of protecting their data from non-authorized users, accidental loss, and internal mistakes and complying with a growing number of global protection regulations.
The first step to protecting your company’s data is defining and segregating what needs protection. To sort data, one needs to know what information to look for, where it is located and how it was created. The location of data to has become complex with multiple systems, including cloud, mobile devices, personal computers, and business networks storing information. Much of the data may not need security in terms of privacy, but some have to be protected, like:
- Controlled unclassified information (CUI)
- Payment card information (PCI)
- Personal health information (PHI)
- Personally identifiable information (PII)
Financial and personnel data and, trade secrets of businesses, classified government and military information are all high-sensitivity data. Risking such data can bring on quick and heavy penalties from regulatory authorities. With global regulatory compliance legislation growing continually, companies are obliged to find data protection solutions that work. An effective data classification solution can enable your company to secure sensitive data and conform to regulatory requirements.
What are the 4 types of data classification?
Your Data Security is built on Data Classification.
Apart from sensitivity and business value, data can be classified in relation to numerous parameters, including who can access it. Generally, a company’s data is categorized into four based on accessibility:
Public Access Data
As the name suggests, low-sensitivity information is freely available to anyone.
has restricted access and can be used only by that granted access, like employees of a company.
is moderately sensitive information, restricted to additional authorization, and is accessible to specified members of staff and authorized third parties. Misuse of this data can severely harm the company or individuals.
Data with Restricted Access
is susceptible and is used only with express clearance. If compromised, such data can cause the company or individuals, or assets damage beyond repair.
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What are the types of data classification based on data reliability?
There are three broad standard types of Data Classification based on data reliability and confidentiality. The classification is done using tags and labels to define the type of data and is based on:
where data is read, decoded, and sorted according to sensitivity.
wherein the location, creation, metadata, or application of the information reveals its sensitivity
where the user applies the knowledge of the sensitivity of the data to classify information at the time of creation, review, editing, or broadcasting
These basic types are only the starting point of data classification. Even within an ordinary business, the activities will require information to be classified further into various levels to match the company’s data security policy. Changes in the business environment often lead to new data protocols. For instance:
Broad classification labels may be divided into subcategories
- Introduction of new global data compliance regulations
- Varying reporting and retention requirements if your business has a presence in multiple jurisdictions
- Business structure changes with growth and development activities
- Policy changes demanded by diversity in business operations
- Changes in business processes or supply chain or integrating with partner’s classification levels
- Making sure that all end users can access and operate with ease
- Supports integration with new systems and toolsets.
What are the steps to data classification?
The process of classifying data depends on the size and activities of your company. Already existing data can be processed with automation, and a system put in place to automate further data that is generated daily. Depending on the business, there are various approaches to the data classification process:
Step 1: Clarify the purpose of the data classification process
The questions you will ask here are:
- why do you need the data classification process for your company
- which main business functions need classification and in what order
- which supplementary functions have to be sorted
- what are the compliances and regulations that apply to your company
Step 2: Group and label the types of data
- Depending on the nature of your business, your data can be classified into various types like product inventory, personnel records, financial records, client lists, etc.
- Categorize restricted and public data
- Any other regulated data like GDPR, CCPA, etc.
Step 3: Set the levels of classification
- Some businesses need more levels of data classification than others. Define the levels.
- Describe each level with sample data
- Educate users to classify data at the time of input
Step 4: Outline the classification process
- Create a pattern for the automated data classification process. Define priorities for scanning the data.
- Set the regularity and resources for the classification process.
Step 5: Define the paradigms for categorization
- Identify the high-level categories of data with examples
- Enable patterns for classification and apply labels
- Set up the process to assess and authenticate both user-classified and automated data.
Step 6: Determine the result and usage of the classified data
- Put in place risk mitigation steps and automation policies; for instance, automatically archive high-category data that is unused for a particular period of time; remove public profiles from folders with sensitive data.
- Establish a method to apply analytics to classification results.
- Define the outcomes expected from the data analytics
Step 7: Observe and sustain the classification process
- Create a pattern to classify new and updated data
- Assess and update the process to keep up with new regulations or developments in business.
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What are the data sensitivity levels? What are the examples of data classification?
The sensitivity level of data is based on the damage created if the data is breached, illegally accessed, or destroyed. A sample classification of your business data according to sensitivity would be:
High Sensitivity Data
- Personally identifiable information (PII)
- Credit card details (PCI)
- Intellectual property (IP)
- Protected healthcare information (including HIPAA-regulated data)
- Financial information
- Employee records
- ITAR materials
- Internal correspondence, including confidential data
Moderate Sensitivity Data
- Student education records
- Unpublished research data
- Operational data
- Information security information
- Supplier contact information
- Internal correspondence not containing confidential data
Low Sensitivity Data
- Public websites
- Public directories
- Publicly available research
- Press releases
- Job advertisements
- Marketing materials
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Why is data classification important for your organization?
Automated data classification is the basis of a company’s security culture. But when new data is added by the minute, the employees who input further information have to be aware of the company’s data protection requirements and policy. Security awareness workshops have to be held at regular intervals to educate the staff to integrate the security culture into every aspect of their activities. It is critical to have a strong data protection protocol at the heart of the business. Along with training and the combined use of technology and automation, effective data identification and classification tools enable your business to:
- Search, recognize and identify sensitive information in documents, emails, and systems
- Ensure optimal levels of protection through embedded metadata, automated safety rules, and reminders to staff to handle such data with care.
- Build layers of security for large businesses with worldwide operations with encryption technology, ERM (enterprise rights management) software, DRM (digital rights management) software, CASB (cloud access security brokers), and advanced firewalls.
Combining stable data protection technology with human skills and processes generates significant benefits for your business. When people, processes, and technology work together, your CISO can effectively orchestrate key data protection and control requirements. In addition to ensuring a clear understanding and proper management of data, wider security coverage is needed on a local and remote basis and ensuring its suitability for all stakeholders.
Data Classification also helps with Data Loss Prevention.
Data classification also serves as a starting point for your data loss prevention strategy. The key to ensuring the security of sensitive data is to know exactly where that data is located. Data discovery combined with a strong and logical data classification protocol should be used to group your sensitive data into categories that help prioritize risk.
Efficient data classification can help your security team focus its monitoring efforts through various categorization methods. For instance, your data can be classified according to the compliance regulations that your company follows. Or it could be categorized explicitly based on risk, and your concern would be how a breach of this data will affect your business from the security angle.
So, once you have determined where your data is located and categorized your most sensitive and at-risk data, you can decide whom to grant access to it and monitor the changes being made to it. This close watch will protect your most at-risk data
Enable Automated Data Classification to Benefit your Business
There is much to gain and inefficiency to lose. Data Classification enables your users to:
- track, identify and secure information across the business, irrespective of its location;
- facilitate data security solutions like DLP, Encryption, and DRM,
Support compliance with data protection systems like CCPA, CUI, ITAR, GDPR, HIPAA, CMMC, etc.
This basic categorization can be further classified to agree with your Company’s data security policy. Depending on the nature and size of your business
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What are the best practices in data classification?
Data Classification is an essential aspect of a company’s data security protocol. It helps you identify sensitive data and who has access to it so that you can protect it. Some best practices for the implementation of a secure data classification strategy:
- Knowledge of the privacy laws and compliance regulations applicable to your business will help you chart the best data classification strategy
- Beginning with a reasonable scope and clearly defined models
- Employing automated tools to process large amounts of data in the shortest time
- Renewing and adjusting classification rules when the need arises
- Authenticating the result of your data classification exercise
- Putting the results to best use and applying the classification process to all avenues of your business.
What makes an effective Data Classification Tool?
While selecting your data classification tools, keep the following pointers in mind.
The data classification tool will be used by teams across the organization. Using a no-code workflow-based software ensures that the learning curve for using the software is flat and improves the adoption rate of the new technology.
Files Supported by the tool
If the tool supports your file types, storage media, migration capabilities, and the total number of files. If the scope of the data classification tool is small and it cannot support a file type, some data may be unprotected, and you may lose it. If all media types are supported with migration capabilities, your company can identify, reclassify and reorganize data based on its security level.
Business should not stop if any of the data storage premises is affected. Ensure that your data classification tool has multiple online and offline storage units to ensure you're online 24x7. The ideal data classification tool can shut down data without interrupting business. It can also help prioritize risk by locating vulnerable information and who is accessing it.
Speed and Accuracy requirements
Speed and accuracy are two aspects of a data classification tool that your business can choose from. If you opt for high accuracy classification, you will lose speed. On the other hand, if the data is processed quickly, it will lack accuracy. Other aspects to consider in your data classification tool are unobtrusive End-User Interaction, practicality, and ease of integration with other tools, which will help manage data after they are classified.
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