Securing Structured and Unstructured Data

With every passing day, more data is created and stored worldwide. The volume of data that organizations create and store continues to increase yearly. According to Statista, the total global volume of data will reach 161 zettabytes (161 trillion gigabytes) by 2025. Business decisions are made based on data. Hence, an organization’s ability to collect the right data, analyze, interpret, and act on its insights determines its success. Data also informs how innovative a business can be. This underscores why data is important, and every other organization is trying to get more. Data is the new currency. Hence, organizations should leave no stone unturned to protect it. Organizations should know that data is broadly categorized into unstructured and structured data whenever they prepare to gather, analyze or even secure it. The two types of data are different and have varying challenges, particularly with data security. Hence, it is essential that enterprises understand the two.

What is Structured Data?

It is any information that is consistently organized, making it trivial to search, query, analyze, or even manipulate. Such data is typically stored in a relational database. It is displayed in defined rows and columns. Therefore, data mining algorithms and tools can access and analyze structured data through search. This data can be used in customer relationship management, sales analysis and control, airline reservation systems, ATM activity, and inventory management systems. Organizations base their business decisions on structured data with the help of various tools that make the collection and analysis easy.

What Is Unstructured Data?

This is information that does not have any easily recognizable/identifiable structure. It doesn’t fit in any predefined data model. Because it lacks a data model or structure, it isn’t easy to analyze, query, or search through unstructured data using standard tools which work effectively for structured data. One characteristic unique to unstructured data is that it is stored in shared and easily accessible formats. It can be found in social media posts, PDF files, text messages, spreadsheets, emails, video, audio, and image files, and in word processing documents. Via these formats, unstructured data can communicate information with ease. However, it’s the ease that makes this type of data prone to unauthorized access.

The Key Drivers Of Unstructured Data

As mentioned above, unstructured data doesn’t have any recognizable format. It is a collection of documents, emails within a folder, and spreadsheets. File sharing is a routine part of any organization. It involves the continuous creation of daily information extracted from structured databases and stored in various formats. Business plans, intellectual property, CAD drawings, and business meeting minutes all fall under unstructured information. Since it is the information that most humans understand, it is the most shared. From unstructured data, enterprises can get insights into:

• Customer sentiment and experience
• Marketing intelligence
• Innovation, research, and development opportunities.
• Regulatory compliance posture for the organizations in the highly regulated industries.

What Are The Differences Between Unstructured And Structured Data

From the preceding, the difference between unstructured and structured data bight seem to be their organization or structure. The two have other key differences, such as searching, schema creation, and analysis. However, this post will cover two main differences, data access and data entry.

Data Entry

Relational databases depend on a structured data entry that is highly restrictive. This ensures that the entered data matches the database schema’s predefined structure. Since only specific data types can be entered in the defined fields, the machines can easily analyze structured data. Although unstructured data doesn’t follow any defined schema, it can be stored within files having an internal structure. Unstructured data is highly useful in predictive data analytics.

Data Access

The structured data is organized to make it easy for the machines to understand or recognize. However, the data isn’t easily legible or understood by human beings. On the other hand, unstructured data is easily accessible human users. However, algorithms and machines find it hard to analyze and access data that isn’t structured. Technological advancements, especially in Machine Learning and Artificial Intelligence, have developed some tools or algorithms that can analyze unstructured data. The analysis of unstructured data depends on the aggregation of all the data available, determining the data which is integral to the current problem (useful data), and then analyzing it to uncover any relationships and patterns. This is one of the areas where machine learning in connection with tools like Hadoop, Microsoft Power-BI, Azure Synapse, and Azure Cosmos DB have played a critical role.

What Are The Vulnerabilities Of Unstructured And Structured Data?

Since the structured data follows a set pattern or organization in the database, securing it is pretty straightforward. However, unstructured data is highly vulnerable because it is easily accessible to humans and is spread out within an organization. You can find unstructured data wherever the users are creating or accessing content. This makes unstructured data in various enterprises grow at an exponential rate. Data breaches involving stolen personally identifiable information (PII) such as credit card information and social security number are mostly captured on the news. However, most intellectual property and other sensitive information are stored in files/documents. However, the attackers do not automatically identify it, meaning they don’t immediately make the headlines. Since unstructured data is not automatically protected or even identified, it becomes harder to:

• Communicate ways of managing and protecting it.
• Know that the vulnerable data exists & where it’s stored.
• Track its flow during auditing.
• Identify the users having access to and are using unstructured data.

Unfortunately, this means unstructured data is shared, stored, or even copied in an unprotected state. Managing unstructured data and controlling it is a data security nightmare for various organizations. Fortunately, they can use content matching technologies to scan their workstations and servers and classify the unstructured data. However, this solution normally has many false positives that can negatively impact the workflow. All the PII and other sensitive information should be protected using persistent security policies and always encrypted.

What Are The Best Practices To Secure Structured Data?

Although securing structured data seems far simpler than securing unstructured data, this does not mean it is easy or insignificant. Securing structured data is an essential component of IT governance which starts with:

• Creation of central and secure storage for the data.
• Tracking the data entry and use.
• Managing encrypted communication and authentication with SSL (Secure Socket Layer) protocol.
• Using secure passwords to protect devices and systems.
• Locating and wiping data from any missing devices remotely, and
• Training your employees on best practices and policies.

This will ensure that your structured enterprise data is secure and can only be accessed by authorized users using passwords, OTPs, MFA, or biometrics.

What Are the Best Practices to Secure Unstructured Data?

Securing any organization’s unstructured data is more challenging than securing the structured data. The initial attempts to deal with the unstructured data involved the EDRM (Enterprise Digital Rights Management) systems. However, these systems did not work properly with the existing workflows, needed training, were not realistically scoped most of the time, and had unpredicted negative effects on the other IT functions. Eventually, most of the EDRM projects would be abandoned at security doorsteps. Accepting the unstructured data’s free-wheeling chaos seems like a better approach. Organizations should adapt their technologies to prioritize, classify, and protect unstructured data through encryption and implementing policies on who can see and access it. Below are some best practices to secure unstructured data.

Identifying Unstructured Data at Its Point of Creation

The first step to securing unstructured data is knowing where it’s being created and stored. This may involve scanning and analyzing the file information throughout the enterprise files looking for unprotected, sensitive unstructured information. In most cases, it comes from structured sources. For instance, data may be exported to a shared document on a pen drive or the cloud from a database. This removes it from monitoring and access control protections. However, organizations can mitigate this security risk by using secure environments for storing unstructured data. The nature of the problem is constantly changing. Hence, organizations must constantly adapt their understanding of the underlying problem through iterative data discovery. They should analyze data in transit between networks and computers, at rest, in use, and being copied, saved in alternative types, or even printed. The discovery can also include analyzing all the unprotected and encrypted data files. Encrypted files are usually watermarked with a DRM (Digital Rights Management) token.

Classify Unstructured Data

There are different categories of unstructured data; not all are sensitive. Hence, enterprises should review unstructured data’s meaning, impact, and sensitivity level. Broadly, sensitive unstructured data comprises:

• Proprietary data such as customer lists, banking details, and intellectual property.
• Data that should be preserved for regulatory or legal reasons.
•  Employees and customers Personally Identifiable Information (PII).

You might find some unstructured data within an enterprise with high analytical value. If data is too hard to use or used for various processes, some employees might carry it using personal storage devices, or even via their cloud accounts. This makes the data vulnerable to malicious actors unless the employee follows the best cybersecurity practices.

Assigning An Owner

It is necessary to secure the unstructured data using encryption. However, that alone isn’t enough. The owner creating, collecting, or modifying the data should be determined. Who are/is collecting and modifying the unstructured data? After you find out who they are, ensure they are responsible for the unstructured data’s security. A more robust approach to securing the unstructured data involves adding an embedded ID or a unique “tag” into the encryption process. However, several viewers or users of that data can also be made the source if you cannot identify the owner. Assigning an owner of the unstructured data is critical to securing and maintaining it in a manner that informs its users.

Identifying Who Can Access the Unstructured Data

People accessing unsecured data are critical to ensuring control over its access. Organizations are recommended to set up Centralized Permissions Management to restrict who has access to sensitive data sources and also manage data access from remote devices. With users being more empowered to access sensitive unstructured data and working remotely, enterprises should ensure proper data access is courteously enforced and allow the users to collaborate. The unique “tag” or embedded ID assigned to a file plays a critical role. It provides a basis on which an organization can track copies of files and changes for every user that accessed it. It can also be used to restrict access to a file or data to a specific or a class of users. The tag can also be used for tracing how data is created and moved within the enterprise network infrastructure.

Monitoring User Activity

Unstructured and structured data are equally important to an enterprise. However, most protections focus on structured data security. The protections don’t take sufficient measures to secure or protect sensitive unstructured data. However, organizations require more robust data protection solutions which can secure any form of data they create, use, or even maintain. Enterprises should log all access requests to any sensitive data. By doing this, an organization will have a clear view of any malicious or unauthorized activity when the logs are correlated with various events from other sources.

Conclusion

Data is the new currency, and more of it is created with each passing day. However, the enterprise must ensure that it instates proper protection measures for both structured and unstructured data. This begins by understanding the vulnerabilities and risks related to both and taking the best practices to secure them. At Cleared Systems, we can help you set up policies to secure your data. From scanning the data, analyzing and categorizing it, setting up permission and access management policies, to implementing data security controls, we can help ensure that your organization’s data is secure. Contact us today to learn more about protecting structured and unstructured data.