Truly understanding and protecting your data is a laborious and convoluted endeavor—especially when dealing with unstructured data. Although as much as 90% of an organization’s data is considered unstructured, many businesses lack a strong framework to classify, store, and protect this often sensitive information. However, through sensitive data discovery, companies can develop a comprehensive understanding of their unstructured data sensitivity and risk in order to make informed decisions moving forward.
To offer some clarification and guidance, DryvIQ developed this guide to unstructured data classification and protection. Here you’ll learn about:
- The difference between structured and unstructured data
- Different data classification levels and categories
- Creating data classification policies and standards
- Data classification and unstructured data management tools
Structured Data vs. Unstructured Data
The fundamental differences between structured and unstructured data are actually quite simple. Structured data deals with concrete values that are easily classified, organized, and entered into Excel files and SQL databases. Some examples of structured data include:
- Social Security numbers
- Credit card numbers
- Addresses
On the other hand, unstructured data is most often non-numerical and text-formatted, making it difficult to enter into a database. Here are a few examples.
- Text-based documents like resumes
- Email correspondence
- Images and videos
- E-booklets
The very nature of unstructured data lends itself to a host of security and storage issues. For example, if an employee’s desktop has the resumes of interviewees stored in an unsecured folder, all sensitive information included on those resumes is potentially put at risk if that employee were to experience a security breach. However, proper classification can help prevent much of this frustration and provide greater protection for your organization and its customers.
Data Classification Categories
The first component of sensitive data classification involves using data classification levels to determine how sensitive certain information is. These levels are:
- Low Sensitivity | Low sensitivity data includes unstructured data that is intended for the public, like your company’s social media posts or press releases.
- Medium Sensitivity | This accounts for non-confidential internal information that should not be made public. An example of unstructured medium-sensitivity data would be internal email correspondence.
- High Sensitivity | High sensitivity indicates that an item of data is absolutely confidential. Failing to protect highly sensitive data like surveillance footage and proprietary intellectual property has the potential to ruin a business.
In addition to classification levels, data can also be organized based on certain categories:
- Public Data | This data is allowed to be accessed by any member of the public. This includes unstructured data like PDFs hosted on your website and blog content.
- Internal Data | This category covers all data that can be distributed company-wide. Most often, unstructured internal data remains at a medium sensitivity level. Some examples include employee handbooks and office memos.
- Confidential Data | This data should only be handled by specific internal departments of your organization. For example, W2s and other tax documents should only be made available to certain departments like Human Resources.
- Restricted Data | Restricted access data is only made available to a few members of an organization. Budgeting plans are one example of highly sensitive information that should only be shared with a few individuals within an organization.
In short, data classification is based on grouping data by sensitivity level and category to identify how classified the information is and who should have access to it. However, to effectively classify unstructured data, policies must also be set in place.
Developing a Data Classification Policy With Sensitive Data Discovery
Engaging in the process of sensitive data discovery helps companies develop a consistent policy and data classification standards to assess vulnerabilities that may be hiding within their unstructured data. To achieve this, the following data discovery steps must be completed:
- Begin collecting all of your unstructured data to better understand what types of sensitive information is being shared with and stored by your organization.
- Determine the reliability of your unstructured data and identify inconsistencies or errors.
- Organize your data into categories and develop a method for distributing information to authorized personnel only. This involves identifying which restrictions and security protocols should be enacted.
- Enlist the help of data scientists and unstructured data management tools to analyze the data you captured and derive insights. This will offer your business recommendations for improved data governance.
- Lastly, analyze the data and insights you collected and present your findings to the rest of your organization. From there, you can develop a more concrete classification and monitoring process for your unstructured data.
At the end of this process, you will be able to create internal policies that allow your unstructured data to be easily protected, accessed, and interpreted throughout your entire organization.
The Role of Data Classification Tools
Naturally, developing effective data classification policies, processes, and standards for your organization is an overwhelming task. The sheer magnitude of information makes it impossible to manually classify and govern an ever-growing repository of unstructured data. Because of this, many businesses are beginning to utilize automated data classification tools.
Tools like DryvIQ use artificial intelligence (AI) and a sophisticated classification methodology to discover and classify sensitive items within unstructured data. Ultimately, having a data classification system in place protects your business, employees, and customers. With mountains of unstructured data piling up, now is the time to act.
Learn more about unstructured data discovery and classification on our resources page.
