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What is Data Classification?
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Data classification is incredibly important for organizations that deal with high volumes of data. Let’s break down what data classification actually means for your unique business.
Perhaps you're the CIO of a massive 100,000-employee enterprise that deals with data. Maybe you're a small tech startup owner that deals with hundreds of files and emails daily. Either way, you likely have a lot of data on your hands but may not have the best classification processes in place.
When you don't know what data you have and where it is, it might be nearly impossible to prioritize risk mitigation or comply with privacy rules. This is where the classification of data comes in.
In this guide, we’ll explore everything you need to know as an organization leader about data classification – from its definition to use cases to types of data to types of classification.
Let’s start this in-depth guide with a definition of data classification.
Organizations today generate, store, and manage more data than ever before, including sensitive information like spreadsheets holding Social Security numbers of customers, clients, and employees. Maintaining the privacy, security, and compliance of this vast amount of data necessitates a greater degree of data management and control than ever before. This necessitates the implementation of a variety of tools and techniques. Data categorization is one of the most often used privacy techniques and procedures.
The process of dividing and arranging data into appropriate categories based on their shared features, such as their level of sensitivity and the risks they pose, as well as the compliance requirements that protect them, is known as data classification or categorization.To keep sensitive information safe, it must first be found, then categorized and marked according to its level of sensitivity. Then, for each type of data, businesses must handle it in such a manner that only authorized persons have access to it, both internally and externally, and that it is always handled in complete conformity with all applicable legislations.
Organizations that don't know their data, including where it lives and how it needs to be secured, risk data security and privacy issues. Knowing where all "sensitive" data is housed across an organization is referred to as "knowing your data." Data privacy experts, such as Data Privacy Officers (DPOs), can't properly secure consumer, employee, and business information if they don't know the following:
Data categorization provides this knowledge by establishing a standard procedure for identifying and tagging all sensitive data throughout an organization, including networks, sharing platforms, endpoints, and cloud files. It works by allowing the development of data characteristics that specify how each group should be handled and secured in accordance with business and regulatory standards. Because the data is easily accessible, businesses may implement safeguards that decrease data exposure risks, reduce data footprints, eliminate data protection redundancy, and direct security resources to the most important tasks. Organizations' data privacy and security protection strategies are streamlined and strengthened as a result of categorization.
The advantages of data classification are virtually endless. A majority of business leaders out there don’t know exactly where their most sensitive data is stored, nor do they know how to properly protect that data. This is a major issue, as data breaches and cybersecurity crimes are at an all-time high. With a well-designed data classification program or process in place, business leaders can keep that valuable information safe. In addition to this, there are many more benefits to data classification that go beyond just knowing where one’s data resides.
Data classification makes it possible for organizations to protect internal and customer data by identifying a few key things. What data is available? How much of that data is extremely sensitive, such as social security numbers or payment accounts? Who can access that data, and how can a data leak affect the organization as a whole?
By identifying the answers to these questions through data classification, business leaders can do a number of things. They can reduce vulnerable data footprints, reduce overall access to sensitive data, grasp different types of data for the purpose of protecting it, and optimize the overall costs of managing unneeded or obsolete data.
Data categorization may assist companies in successfully securing, storing, and managing their data from the moment it is generated until it is destroyed. Data categorization may help companies get a better understanding of and control over the data they collect and distribute. Such procedures can help organizations get more efficient access to and use of protected data. Data categorization also aids risk management by assisting companies in determining the worth of their data as well as the consequences of it being lost, stolen, abused, or hacked.
Data categorization aids in locating regulated data within the organization, as well as ensuring that adequate security measures are in place and that the data is traceable and searchable, as needed by compliance standards. Data categorization guarantees that sensitive data, such as medical, credit card, and personally identifiable information, is handled effectively for various requirements. It also makes it easier to stay in compliance with all essential rules, regulations, and privacy laws on a daily basis. Data classification can also help satisfy modern compliance regulations by allowing for the speedy retrieval of specified information within a given deadline.
While data classification is quite important for the modern business, it does have its downsides.
Traditional data categorization methods are typically manual, expensive and generally inaccurate. This poses a number of difficulties. Sensitive information has the potential to get lost in data silos, where it will be unknown, unreachable and vulnerable. Mishandling regulated data can result in fines and penalties for businesses. Client data breaches can result in litigation, degrade an organization's brand, and reduce goodwill. The key to a successful data classification program is automation and the ability to scale with near perfect accuracy. This is where organizations like Data Sentinel come in.
Many firms have theoretical rather than operational data categorization policies. In other words, the corporate policy is either ignored or left to the discretion of business users and data owners. This problem originates from a variety of oversights, but a common discussion point it that the problem is too complex and large to be undertaken. Ultimately leaving the company's data exposed to undue risk.
A range of data security and privacy issues might occur as a result of poor data categorization execution. Typically, companies tend to start with the easier to manage and identify structured data sources, leaving the truly risky unstructured data to last or not at all. Data and privacy issues are then ultimately pushed to the back burner in favor of more important goals like sales growth and efficiency. Businesses overcomplicate data classification more because of legacy approaches to the problem, resulting in a lack of practical outcomes.
The sensitivity levels of data are used to classify it. The regulations established by various countries and states might categorize sensitivity differently, but in general we can simplify by saying that there are four categories of sensitive data: low, moderate, high, and restricted data sensitivity.
Low data sensitivity refers to information that provides little to no risk to the company. Because there are little or no constraints on who may access the data in this class, it can be viewed by anybody. In retrospect, this knowledge is public and may be discussed by anyone, anywhere. For an organization, data in this class include any publicly available information on the organization. Information on founders, business niches, and leadership might all be included.
Data in this category is subject to contractual agreements between the parties interested in the data. Notably, the loss of such data in this category usually results in significant consequences for the company. IT service information, internal staff information, and business processes details are examples of data that fall under this category.
This categorization contains sensitive information that should be kept private. A breach of this data collection might result in serious consequences for the company, including criminal responsibility and /or consumer litigation. Furthermore, a data breach might jeopardize the company's ability to operate. IT security information, controlled unclassified information, PHI, and PII are examples of this data category.
Data in this category is regarded as highly confidential and is frequently subject to a nondisclosure agreement (NDA). Industry-specific data, trade secrets, and clients' financial information are examples of limited, sensitive data. A compromise of this sort of sensitive data might result in the organization being shut down completely, as well as legal repercussions and unfathomable financial damages.
Data categorization is a hygiene practice for most firms. It increases data security and enables them to comply with regulatory requirements. It also implies that information can be more readily reviewed and examined, both in terms of correctness and how it is kept.
Customer information that is sensitive must be maintained securely and removed after a set length of time. These regulatory requirements can be accomplished by categorizing data and applying security rules to it. The advantages of data classification mostly stem from this premise, although there are also functional benefits. Instead of having to check each endpoint, businesses may grant central rights to manage who can read, alter, and remove essential intelligence by classifying data fields.
Permissions can be provided to different programs to guarantee that only the most correct records are updated in data fields. As a consequence, a system that can restrict access to sensitive data, track the usage of intellectual property assets, and maintain security has been created.
If done manually, you must establish sensitivity levels, teach your users to recognize each level, and offer a way for them to tag and categorize every new file they produce when you want them to classify their own data.
The majority of classification systems integrate with policy-enforcing solutions, such as data loss prevention (DLP)software, which tracks and secures sensitive data marked by users. The benefit of using user categorization is that people are quite competent at determining whether or not material is sensitive. Classification accuracy may be fairly excellent with the right equipment and simple rules, but it is extremely dependent on your users' vigilance and won't scale to keep up with data generation.
Manually marking data is time-consuming, and many users will forget or ignore it. Furthermore, getting people to go back and retrospectively annotate past data is a massive issue if you have significant volumes of pre-existing data (or machine-generated data).
To discover and understand data in systems, modern and automated data categorization engines use machine learning and other techniques to read and analyze the data. Automated classification is far more efficient than manual categorization, although accuracy is dependent on the engine's architecture, technology and configuration. The closeness of text, negative keywords, match ranges, and validation methods are all common characteristics in data categorization services or engines that assist validating findings and reducing false positives.
When choosing an automated categorization product, accuracy, efficiency, and scalability are all critical factors to consider. For situations with hundreds of huge data stores, you'll need a distributed, multi-threaded engine that can scan numerous systems at the same time without taking too much of the resources on the stores being scanned.
It can take a long time to conduct an initial categorization scan of a big multi-petabyte environment. Following scans can be sped considerably by using true incremental scanning. Some classification engines necessitate the creation of an index for each object they categorize. Look for an engine that doesn't require an index or only indexes items that meet a certain policy or pattern if storage space is an issue.
The process of data classification is both complex and somewhat simple in nature. Basically, most data classification processes follow these steps:
Implementation of a good data classification process can be difficult, which is why we recommend employing the help of a trusted partner like Data Sentinel to take on the task for your company. However, there are a few best practices for implementing good data classification:
If you need a bit of help starting the process of data classification or are interesting in automating the process now and on an ongoing basis, Data Sentinel is here to help.
Data Sentinel’s proprietary deep learning discovery technology illuminates the true nature of an organization’s data across all sources and systems, monitoring, measuring, and remediating the data to ensure compliance with company policies and evolving data management privacy regulations.
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Perhaps you're the CIO of a massive 100,000-employee enterprise that deals with data. Maybe you're a small tech startup owner that deals with hundreds of files and emails daily. Either way, you likely have a lot of data on your hands but may not have the best classification processes in place.
When you don't know what data you have and where it is, it might be nearly impossible to prioritize risk mitigation or comply with privacy rules. This is where the classification of data comes in.
In this guide, we’ll explore everything you need to know as an organization leader about data classification – from its definition to use cases to types of data to types of classification.
Let’s start this in-depth guide with a definition of data classification.
Organizations today generate, store, and manage more data than ever before, including sensitive information like spreadsheets holding Social Security numbers of customers, clients, and employees. Maintaining the privacy, security, and compliance of this vast amount of data necessitates a greater degree of data management and control than ever before. This necessitates the implementation of a variety of tools and techniques. Data categorization is one of the most often used privacy techniques and procedures.
The process of dividing and arranging data into appropriate categories based on their shared features, such as their level of sensitivity and the risks they pose, as well as the compliance requirements that protect them, is known as data classification or categorization.To keep sensitive information safe, it must first be found, then categorized and marked according to its level of sensitivity. Then, for each type of data, businesses must handle it in such a manner that only authorized persons have access to it, both internally and externally, and that it is always handled in complete conformity with all applicable legislations.
Organizations that don't know their data, including where it lives and how it needs to be secured, risk data security and privacy issues. Knowing where all "sensitive" data is housed across an organization is referred to as "knowing your data." Data privacy experts, such as Data Privacy Officers (DPOs), can't properly secure consumer, employee, and business information if they don't know the following:
Data categorization provides this knowledge by establishing a standard procedure for identifying and tagging all sensitive data throughout an organization, including networks, sharing platforms, endpoints, and cloud files. It works by allowing the development of data characteristics that specify how each group should be handled and secured in accordance with business and regulatory standards. Because the data is easily accessible, businesses may implement safeguards that decrease data exposure risks, reduce data footprints, eliminate data protection redundancy, and direct security resources to the most important tasks. Organizations' data privacy and security protection strategies are streamlined and strengthened as a result of categorization.
The advantages of data classification are virtually endless. A majority of business leaders out there don’t know exactly where their most sensitive data is stored, nor do they know how to properly protect that data. This is a major issue, as data breaches and cybersecurity crimes are at an all-time high. With a well-designed data classification program or process in place, business leaders can keep that valuable information safe. In addition to this, there are many more benefits to data classification that go beyond just knowing where one’s data resides.
Data classification makes it possible for organizations to protect internal and customer data by identifying a few key things. What data is available? How much of that data is extremely sensitive, such as social security numbers or payment accounts? Who can access that data, and how can a data leak affect the organization as a whole?
By identifying the answers to these questions through data classification, business leaders can do a number of things. They can reduce vulnerable data footprints, reduce overall access to sensitive data, grasp different types of data for the purpose of protecting it, and optimize the overall costs of managing unneeded or obsolete data.
Data categorization may assist companies in successfully securing, storing, and managing their data from the moment it is generated until it is destroyed. Data categorization may help companies get a better understanding of and control over the data they collect and distribute. Such procedures can help organizations get more efficient access to and use of protected data. Data categorization also aids risk management by assisting companies in determining the worth of their data as well as the consequences of it being lost, stolen, abused, or hacked.
Data categorization aids in locating regulated data within the organization, as well as ensuring that adequate security measures are in place and that the data is traceable and searchable, as needed by compliance standards. Data categorization guarantees that sensitive data, such as medical, credit card, and personally identifiable information, is handled effectively for various requirements. It also makes it easier to stay in compliance with all essential rules, regulations, and privacy laws on a daily basis. Data classification can also help satisfy modern compliance regulations by allowing for the speedy retrieval of specified information within a given deadline.
While data classification is quite important for the modern business, it does have its downsides.
Traditional data categorization methods are typically manual, expensive and generally inaccurate. This poses a number of difficulties. Sensitive information has the potential to get lost in data silos, where it will be unknown, unreachable and vulnerable. Mishandling regulated data can result in fines and penalties for businesses. Client data breaches can result in litigation, degrade an organization's brand, and reduce goodwill. The key to a successful data classification program is automation and the ability to scale with near perfect accuracy. This is where organizations like Data Sentinel come in.
Many firms have theoretical rather than operational data categorization policies. In other words, the corporate policy is either ignored or left to the discretion of business users and data owners. This problem originates from a variety of oversights, but a common discussion point it that the problem is too complex and large to be undertaken. Ultimately leaving the company's data exposed to undue risk.
A range of data security and privacy issues might occur as a result of poor data categorization execution. Typically, companies tend to start with the easier to manage and identify structured data sources, leaving the truly risky unstructured data to last or not at all. Data and privacy issues are then ultimately pushed to the back burner in favor of more important goals like sales growth and efficiency. Businesses overcomplicate data classification more because of legacy approaches to the problem, resulting in a lack of practical outcomes.
The sensitivity levels of data are used to classify it. The regulations established by various countries and states might categorize sensitivity differently, but in general we can simplify by saying that there are four categories of sensitive data: low, moderate, high, and restricted data sensitivity.
Low data sensitivity refers to information that provides little to no risk to the company. Because there are little or no constraints on who may access the data in this class, it can be viewed by anybody. In retrospect, this knowledge is public and may be discussed by anyone, anywhere. For an organization, data in this class include any publicly available information on the organization. Information on founders, business niches, and leadership might all be included.
Data in this category is subject to contractual agreements between the parties interested in the data. Notably, the loss of such data in this category usually results in significant consequences for the company. IT service information, internal staff information, and business processes details are examples of data that fall under this category.
This categorization contains sensitive information that should be kept private. A breach of this data collection might result in serious consequences for the company, including criminal responsibility and /or consumer litigation. Furthermore, a data breach might jeopardize the company's ability to operate. IT security information, controlled unclassified information, PHI, and PII are examples of this data category.
Data in this category is regarded as highly confidential and is frequently subject to a nondisclosure agreement (NDA). Industry-specific data, trade secrets, and clients' financial information are examples of limited, sensitive data. A compromise of this sort of sensitive data might result in the organization being shut down completely, as well as legal repercussions and unfathomable financial damages.
Data categorization is a hygiene practice for most firms. It increases data security and enables them to comply with regulatory requirements. It also implies that information can be more readily reviewed and examined, both in terms of correctness and how it is kept.
Customer information that is sensitive must be maintained securely and removed after a set length of time. These regulatory requirements can be accomplished by categorizing data and applying security rules to it. The advantages of data classification mostly stem from this premise, although there are also functional benefits. Instead of having to check each endpoint, businesses may grant central rights to manage who can read, alter, and remove essential intelligence by classifying data fields.
Permissions can be provided to different programs to guarantee that only the most correct records are updated in data fields. As a consequence, a system that can restrict access to sensitive data, track the usage of intellectual property assets, and maintain security has been created.
If done manually, you must establish sensitivity levels, teach your users to recognize each level, and offer a way for them to tag and categorize every new file they produce when you want them to classify their own data.
The majority of classification systems integrate with policy-enforcing solutions, such as data loss prevention (DLP)software, which tracks and secures sensitive data marked by users. The benefit of using user categorization is that people are quite competent at determining whether or not material is sensitive. Classification accuracy may be fairly excellent with the right equipment and simple rules, but it is extremely dependent on your users' vigilance and won't scale to keep up with data generation.
Manually marking data is time-consuming, and many users will forget or ignore it. Furthermore, getting people to go back and retrospectively annotate past data is a massive issue if you have significant volumes of pre-existing data (or machine-generated data).
To discover and understand data in systems, modern and automated data categorization engines use machine learning and other techniques to read and analyze the data. Automated classification is far more efficient than manual categorization, although accuracy is dependent on the engine's architecture, technology and configuration. The closeness of text, negative keywords, match ranges, and validation methods are all common characteristics in data categorization services or engines that assist validating findings and reducing false positives.
When choosing an automated categorization product, accuracy, efficiency, and scalability are all critical factors to consider. For situations with hundreds of huge data stores, you'll need a distributed, multi-threaded engine that can scan numerous systems at the same time without taking too much of the resources on the stores being scanned.
It can take a long time to conduct an initial categorization scan of a big multi-petabyte environment. Following scans can be sped considerably by using true incremental scanning. Some classification engines necessitate the creation of an index for each object they categorize. Look for an engine that doesn't require an index or only indexes items that meet a certain policy or pattern if storage space is an issue.
The process of data classification is both complex and somewhat simple in nature. Basically, most data classification processes follow these steps:
Implementation of a good data classification process can be difficult, which is why we recommend employing the help of a trusted partner like Data Sentinel to take on the task for your company. However, there are a few best practices for implementing good data classification:
If you need a bit of help starting the process of data classification or are interesting in automating the process now and on an ongoing basis, Data Sentinel is here to help.
Data Sentinel’s proprietary deep learning discovery technology illuminates the true nature of an organization’s data across all sources and systems, monitoring, measuring, and remediating the data to ensure compliance with company policies and evolving data management privacy regulations.
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