What is Data Classification?
Data is one of the most important assets an organization holds, yet it can also be one of the most delicate. While sensitive data use is now all but ubiquitous, data leaks and breaches are becoming more common and costly.
Regulators and governments are taking note, and implementing standards in an effort to tamp down threats. Data localization laws and zero trust architectures have become more mainstream in recent years, but underscoring these measures is the need for effective data classification. What exactly is data classification and what steps should you take to incorporate it? In this article, you’ll find everything you need to know about the fundamentals of data classification.
What Is Data Classification and Why Is It Important?
There is a strong correlation between an organization’s ability to avoid a data breach and the level of control over its data environment. Data classification is the foundation of a controlled data environment – without it, any downstream security mechanisms will be fragile and unreliable.
Data classification is defined as the identification of the types, levels of sensitivity, and criticality of an organization’s data. This helps quickly and systematically understand the data ecosystem, which in turn informs risk management, data security needs, and relevant compliance standards. If you know how much sensitive data is in your possession and the frequency with which it may be used based on its criticality to the business, you can more easily determine who should be authorized to access it and how to build the appropriate policies to ensure only those people can do so.
Long before those policies can be implemented, organizations typically follow a three-part journey to implementing data protection measures:
- Mitigating risks to the data, such as information leakage
- Mitigating risks from the data, such as bias or errors
- Mitigating risks with the data, such as misuse or exploitation
But before this journey can even begin, data must first be classified. Without knowing about the data, it’s virtually impossible to accurately anticipate, let alone mitigate, these risks. Data classification is therefore critical to establishing a strategic and resilient data security framework.
Types of Data Classification
Data can be tagged and analyzed in a myriad of different ways based on the contents of the data set, the industry to which it pertains, the applicable regulation, and its format, among others. But to create consistency across all data, there are a few primary types of data classification:
- Public data, like job postings and press releases, which can be shared with the public at large.
- Internal data, which in some contexts may include individuals’ contact information and/or emails, is more sensitive than public data but if misused, is considered less harmful than confidential data. This type of information is often shared among a wide group of authorized employees and authorized third parties, such as contractors.
- Confidential data, like networking and infrastructure data in some contexts, is more critical to an organization’s operations than internal data, and/or may be more sensitive than internal data. Misuse could cause moderate damage to the organization’s competitive position and reputation, and/or pose moderate risks to individuals. The group of employees and third parties authorized to access it is thus usually smaller than for internal data.
- Restricted data usually comprises highly sensitive data elements, such as individuals’ health or financial data, or strategic business information covered by confidentiality agreements. Unauthorized access and use of such information could violate regulatory and/or contractual requirements with serious consequences for the organizations and individuals, irreversibly affect an organization’s competitive position and/or reputation, and pose high risks to individuals. Restricted information tends to be limited to an even smaller group of authorized employees, contractors, and business partners who have a strategic business need to access the information.
To determine whether data can be classified as internal, confidential, or restricted, organizations can take three approaches:
Content-Based Classification identifies sensitive information, like personally identifiable information (PII), based on the contents of files or documents. Content-based classification answers the question “what is in the file/document?”.
Context-Based Classification finds and classifies sensitive information indirectly using a document’s metadata, like the application, owner, or location of the data.
User-Based Classification relies on manual judgment to identify sensitive data. In other words, a user can stipulate how sensitive a document is when they create, edit, review, or release it.
Finally, whether data is at rest, in process, or in transit, as well as its format – structured, semi-structured, or unstructured – can also be considered ways of classifying data. Knowing the types of data classification helps all stakeholders, including security and governance teams, get a basic understanding of their assets. In turn, this makes assessing the data’s levels of sensitivity more straightforward.
What Are the Levels of Data Sensitivity?
Classifying sensitive data is essential to ensuring its protection. But what is it about this data that makes its privacy such a high priority?
Consider this common scenario. When a streaming service requests your email, you probably don’t hesitate to provide it. But if it asked for your social security number, you’d probably opt not to provide it. Why? There’s a clear need for one, but not the other – and the two pieces of information are not created equal. Email addresses tend to be widely available, and on their own, usually don’t cause substantial harm. Social security numbers, on the other hand, are tied to personal health and financial information, and are often involved in identity theft cases. The latter clearly has more potential to cause harm, and therefore is considered highly sensitive.
Data’s sensitivity has a direct correlation with the impact and harm individuals and organizations may experience if it lands in the wrong hands. Sensitivity can have two different dimensions: its confidentiality, or the potential impact if access to the data was not restricted; and its availability, or the consequences of data deemed critical to everyday business operations not being available. In the example above, the confidentiality of your social security far outweighs its criticality for the streaming service – the same level of service can be provided without it.
Despite the complexity of handling sensitive data, the levels of data sensitivity are relatively straightforward – high, medium, and low.
High Sensitivity Data generally refers to information that is generally protected by a rule or regulation, and could have severe consequences if compromised. Sensitive personal information and protected health information (PHI), like bank account numbers and medical records, both fall into this category.
Medium Sensitivity Data is typically meant to be kept internal and could cause moderate harm if accessed without authorization, but the effects would not necessarily be dire. Information like companies’ contract agreements or certain attributes associated with personal information, such as customer name and contact details, are often considered medium sensitivity.
Low Sensitivity Data is widely available and meant for general consumption. Therefore, public websites, press releases, and maps could all be categorized as low sensitivity.
Once you understand how to classify data, you can start putting it to work for your data use cases.
Data Classification in Practice
Regardless of your industry, establishing data classification follows a relatively standard process. This starts with sensitive data discovery, which is the process of identifying data across all connected sources and answering questions like “what data do I have, and is it creating risks?”. Sensitive data discovery tools simplify this process by scanning and tagging data, including metadata, that contains information that may fall in the medium or high sensitivity levels. This takes the place of manual human inspection, which is tedious and error-prone. Tags and classifiers should map to access control policies in order to make the enforcement process easy and streamlined.
Risk assessments typically involve three steps – identification, analysis, and evaluation – and are essential to a governance, risk, and compliance framework. The process boils down to identifying potential threats relative to data’s level of confidentiality, availability, and integrity; analyzing the impact, likelihood, and detectability of each; and determining the best course of action in the event that a breach occurs. These steps are done within the context of any rules and regulations that apply to your data.
With the risk assessment completed, you can standardize your approach to data classification. This includes defining the policies that should be applied to the various data types and sensitivity levels, as well as the roles and responsibilities associated with creating, enforcing, and validating them. Then, when you locate and categorize your data, there is no ambiguity about where data lives, how it should be handled, and by whom.
Finally, ongoing monitoring and maintenance of data access and policy implementation will keep you up-to-speed on any broken processes or anomalies, and by default, whether your data classification system needs to be revisited.
Data classification is key for cloud data management and is indispensable for satisfying compliance laws and regulations. It both helps ensure data consumers only have access to the data they need, while allowing you to monitor data usage and detect risky behavior.
What to Look for in Data Classification Software
As mentioned in the previous section, standardizing your approach to data classification is key to operationalizing it. But it can also help you choose the right data classification software for your tech stack.
The best data classification tools will make it easy to bypass some of the most common data classification challenges, like keeping up with the speed of data creation and use, and maintaining compliance with various regulations. The following capabilities can help avoid these pitfalls and simplify data classification:
- Automation – Tools that automatically scan data sources and tag sensitive data streamline manual investigations of every data set that enters your ecosystem. This releases bottlenecks and helps accelerate workflows.
- Customization – Many solutions offer pre-built data classifiers, like PII and PHI. But it’s critical to note that every organization is different, and so are the ways in which they classify data and define fine-grained access control policies. To tailor your solution to your needs, look for the ability to customize data classifiers.
- Native Integrations – Data classification is key to an organization’s data security posture, and therefore should be integrated with other security functions, like data access control, monitoring, and detection. Native integrations are also the only way to avoid having to unnecessarily move or copy data during the classification process. In particular, the ability to connect to data catalogs and identity access management (IAM) systems ensures that metadata can be easily consumed into policy implementation.
- Dynamic Execution – Since data classification is key to detecting suspicious behavior and insider threats, it needs to be comprehensive and apply to data in any state. Exposure to sensitive data is different from access to sensitive data, but both must be covered. Data classification should therefore be dynamic and done at different points in time, for instance when the data is at rest as well as when it is queried.
Implementing data classification can be easy when it’s done in tandem with other data security efforts. To see why, find out more about data security solutions.