The business setting today is more data-driven than ever.  

Sensitive information is created, stored, and managed by organizations continuously.   

Although your team may believe that they know every location where sensitive and personal data is stored within your company, security teams frequently find dark data stored in unexpected areas.   

How can your team confidently manage and reduce risk for your organization if they are unaware of all the data you have collected?   

Although awareness is crucial, navigating today’s digital environment can be challenging.  

Sensitive data can now travel via more routes and reside in more locations—many of which are outside of business premises—due to the rapid adoption of cloud storage, the growth of remote work, and the growing risk of data breaches.   

Because of this, identifying sensitive data is essential to developing and implementing a successful data security plan.   

IT teams may better understand what needs to be protected, where this vital information is stored, and the best course for dealing with recurring insider and external threats using sensitive data discovery.  

Even if it sounds challenging, Sensitive Data Discovery helps your business manage data security with ease.   

This article reviews the fundamental rules for managing sensitive data and helps you understand everything about data discovery, from its benefits to industries where sensitive data discovery is most important. 

What is sensitive data?  

Sensitive data is any information that must be protected from unauthorized users’ access and kept private.  
 
There are a few more precise definitions of sensitive data in practice, depending on the rules or guidelines you must comply with.   

Businesses should be aware of two major categories when using sensitive data discovery tools.   

Personal Data: Any information used to accurately and somewhat reliably identify a living person is considered personal data.  

Sensitive Data: Sensitive data is a subset of personal data that falls under the GDPR’s special processing requirements.  

The General Data Protection Regulation (GDPR) defines sensitive data as any personal information about an individual that relates to their racial or ethnic origin, political views, religion, or philosophical beliefs; trade union membership; genetic and biometric data; health-related data; or details about their sexual orientation or sex life.   

Examples of sensitive data  

Sensitive information can take many different forms. Legal data protection rules may frequently include some entities due to their greater legal protections.   

Here are five examples of sensitive data your company must be aware of.  

Protected Health Information (PHI)  

Although PHI tends to be related to the healthcare sector, it is likely collected and stored by businesses not directly involved in the medical field from their staff or customers.   

PHI may include a person’s name, medical history, disabilities, emergency contact details, current conditions, and medical history.   

Payment Card Industry Data Security Standard (PCI DSS)  

PCI DSS ensures companies securely handle, store, and transmit credit card information.   

These regulations require any business to handle a person’s credit card information to protect that data appropriately.

This covers all cardholder data, including PINs and other authentication data, cardholder names, service codes, expiration dates, magnetic stripe data, and card verification codes.  

Biometric Data  

A more recent form of sensitive data that is covered by the CPRA and the New York SHIELD Act is biometric data.    

Biometrics refers to an individual’s physical and behavioral traits that can be used to digitally identify them and provide access to devices, programs, or systems.    

Biometric data such as fingerprints, facial recognition, retinal scans, and voice recognition are among the most frequently used types.    

Business offices are rapidly adopting biometrics more frequently.  

Information on consumer behavior  

Consumer behavior data, another new category of sensitive data covered by the CPRA, is personal data that can be used to identify, relate to, or be associated with an individual or their household.   

This includes transaction records, search and browsing histories, geolocation information, and any data about a customer’s interactions with websites, applications, or advertisements on the Internet.  

What is sensitive data discovery?  

The process of locating and identifying sensitive data to safely eliminate or protect any information that may compromise security is known as sensitive data discovery.   

This is an essential step for security teams to guarantee regulatory compliance, protect the privacy of their clients and staff, and stop data breaches and leaks.   

Data discovery is a continuous process that security professionals must seek to establish to develop a solid, safe foundation because new data is created daily.  

Sensitive data discovery process  

The three stages of the sensitive data discovery process include:  

Preparation  

Data is cleaned and combined during the preparation process to achieve high data quality inside the networks, applications, and endpoints under examination.  

During this step, data discovery tools can automatically remove outliers, normalize data quality, detect null values, and unify data formats.   

Visualization  

Next, the IT staff may see visual maps of the locations of sensitive data’s travel and storage, thanks to data discovery tools.   

The visualization step’s data mapping allows one to see which devices, apps, and programs must be secured to protect sensitive data.  

Analysis  

Ultimately, the analysis produces realistic solutions for sensitive data protection.

This calls for various practices and programs like cloud DLP, endpoint and network security, and identity and access management.  

Benefits of sensitive data discovery  

What is the outcome of this sensitive data discovery process for your team?   

The General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the California Privacy Rights Act (CPRA), and the Stop Hacks and Improve Electronic Data Security Act (SHIELD Act) of New York are just a few of the data protection laws that this process assists your company in adhering to legally.   

Noncompliance might cost your company hundreds of thousands of dollars or more, depending on the severity of a data breach or leak.   

Apart from adhering to privacy regulations, companies may enjoy additional benefits following the sensitive data discovery:  

• Management of Reputation: Your company may be impacted by data breaches or the public disclosure of private information in ways other than legal or financial ones. It might also harm your business’s reputation.  
  
• Reduced Data Storage Expenditure: Thanks to sensitive data discovery, your team can quickly consolidate sensitive data locations. Less overall data also leads to cheaper overall data archiving and storage costs and reduced risk.  
  
• Reduced Sensitive Data Footprint: More isn’t necessarily better regarding sensitive data. Your organization will feel more at peace, and your security staff will be able to spend more on data protection with a smaller footprint of sensitive data.  

Semantic data discovery  

A comprehensive understanding of the types and locations of information is becoming increasingly crucial as businesses collect and store vast volumes of data.  

On the other hand, much of this information is easily missed when depending on human or semi-automated data classification.   

Semantic data discovery automates classification and analysis using advanced algorithms and machine learning to give a deeper look at organizational data.   

This makes it possible to find and analyze data more quickly and accurately, enabling a company to fully utilize information that might have been underutilized or overlooked.  

Industries where sensitive data discovery is important  

Any business must adequately secure and protect sensitive data, but specific industries are subject to stricter regulations and more frequent audits than others. Some can also have industry-specific guidelines and standards they must follow.  

• Healthcare: Due to the daily handling of large volumes of patient data, healthcare companies are subject to several data security laws and regulations, including HITECH and HIPAA.  
  
• Financial: Sensitive data, including credit or debit card information and financial records, is handled and stored by banks, credit unions, investment firms, and other financial service institutions. These institutions are subject to strict regulations.  
  
• Higher Education: Sensitive information is stored on academic campuses in various ways, from financial data in administrative offices to PHI in on-campus clinics.  
  
• eCommerce: With the recent CPRA-mandated laws, eCommerce companies have an even greater obligation to secure sensitive customer data.  
  
• Manufacturing: Knowing where sensitive data resides and how to protect it is essential, especially on large-scale industrial projects, as more manufacturing processes become automated, and the Internet of Things (IoT) becomes more widely used.  
  
• Telecommunications: Telecommunication organizations adhere to several security standards and continuously collect sensitive data, including call recordings, location information, transactional data, and more, in real-time.  
  
• Government: Government agencies, both local and federal, collect a vast quantity of personal data from the public. Even though some may be on public record, various data types need to be protected, including tax returns.  

Best practices for sensitive data discovery  

Ultimately, your strategy will depend on the data discovery tools appropriate for your company.   

Consider the following features to enhance your organization’s data visibility.  

Include classification in the process of discovery.  

The idea of data discovery revolves around data monitoring. Arguably, the capacity to categorize your data is much more crucial.  

Data classification tools enable you to parse files and data strings in the context of information security, allowing you to categorize data obtained in structured or unstructured data sources properly.   

It should be possible for you to determine the context and content of the data your business uses and stores if this procedure is performed with high precision (i.e., without false positives).  

Consider platforms that facilitate the implementation or correction of workflows.  

The main advantage of data classification and discovery tools is that they provide teams with the knowledge to develop comprehensive data use and storage policies.   

Effective programs also enable administrators to implement workflows that enforce these standards throughout the networks or applications where the data is located.  

Automate the process as much as possible.  

Cloud, IoT, BYOD, shadow IT, and other technologies changing the location and volume of data have made the security picture more complex.   

Automation is required to protect sensitive data due to the level of risk, particularly in remote work settings.  

Install security solutions that effectively use AI or other automated processes to give comprehensive data policies a strong foundation.   

Security teams that use automated technologies will be more productive because they won’t have to handle every possible issue or incorrect security setting.  

Conclusion  

It is critical for organizations to fully understand and identify sensitive data as they navigate the complex path of collecting, handling, and storing sensitive data.  

Protecting sensitive data is more complex than ever in a world of constant data generation and rapid technological advances.   

A key component of organizational security is sensitive data discovery, which provides an organized approach to finding, protecting, and handling data that may risk compliance and privacy.   

Accepting the discovery of sensitive data is not only necessary but also strategically crucial for a future powered by data because, as you know, you cannot protect the data if you don’t know it exists. 

FAQ

1. What is sensitive data, and why is it important to discover and protect it?

Sensitive data encompasses information that requires protection from unauthorized access to safeguard privacy and security. It’s crucial to discover and protect sensitive data to comply with regulations, prevent data breaches, and maintain trust with customers and stakeholders.

2. What are the major categories of sensitive data businesses should be aware of?

Businesses should primarily focus on two categories: Personal Data and Sensitive Data. Personal data includes information that can identify individuals, while Sensitive Data, as defined by regulations like GDPR, includes details such as racial or ethnic origin, health data, and biometric information.

3. What are some examples of sensitive data that organizations need to be cautious about?

Examples of sensitive data include Protected Health Information (PHI), Payment Card Industry Data Security Standard (PCI DSS) data, biometric data, information on consumer behavior, and financial records. These types of data require careful handling to ensure compliance and protect individuals’ privacy.

4. What is the sensitive data discovery process, and why is it essential?

Sensitive data discovery involves locating and identifying sensitive data within an organization’s systems to protect it effectively. This process is crucial for ensuring regulatory compliance, preventing data breaches, and maintaining trust with customers. It allows organizations to take proactive steps in securing sensitive information.

5. What are the benefits of sensitive data discovery for businesses?

Sensitive data discovery helps businesses comply with data protection laws like GDPR, HIPAA, and CPRA, reducing the risk of costly penalties due to noncompliance. Additionally, it aids in managing reputation, cutting data storage costs, reducing the sensitive data footprint, and enhancing overall data security posture.

6. What is semantic data discovery, and how does it differ from traditional methods?

Semantic data discovery employs advanced algorithms and machine learning to automate data classification and analysis. Unlike traditional methods relying on human or semi-automated classification, semantic data discovery provides a deeper understanding of organizational data, enabling quicker and more accurate insights.

7. In which industries is sensitive data discovery particularly important?

Sensitive data discovery is crucial across various industries, especially those handling large volumes of personal and sensitive information. This includes healthcare, financial services, higher education, eCommerce, manufacturing, telecommunications, and government sectors.

8. What are the best practices for implementing sensitive data discovery?

Effective implementation of sensitive data discovery involves incorporating classification into the discovery process, utilizing platforms facilitating workflow implementation, and automating the process wherever possible. It’s essential to leverage tools that provide comprehensive data policies and enhance security through automation.

9. How can organizations ensure successful sensitive data discovery?

Organizations can ensure successful sensitive data discovery by dedicating resources to proper preparation, visualization, and analysis stages of the process. This includes cleaning and combining data, visualizing data maps, and implementing solutions for sensitive data protection based on analysis insights.

10. What are the consequences of neglecting sensitive data discovery?

Neglecting sensitive data discovery can lead to severe consequences such as regulatory noncompliance fines, reputational damage, increased vulnerability to data breaches, and financial losses. By prioritizing sensitive data discovery, organizations can mitigate these risks and maintain a secure data environment.