Data Classification is a process of categorizing data and a simple solution organizations can implement across all systems while leveraging the security tools they already in place. Information is more accessible, sharable, and replicable than ever, which can be as exciting as it is overwhelming. The balance between being making information visible and secure can often be a point of contention between Information Security stakeholders and the rest of the business. Much of this conversation relates to these three core aspects of business information:

  1. Many organizations do not have a firm grasp on the scope of their information existing in digital forms, or where it is stored.
  2. Not all data is same, and the consequences for losing some data can be much more costly than other types.
  3. Adopting a maximum-security stance for all forms of data can constrict the flow of normal business processes and create inconvenience where it is not needed, while lax security standards surrounding information can lead to data leaks and breaches.

In this article, we will walk through the approaches to classifying your data, how to create your data classification framework, and how your organization can effectively implement the solution to meet your needs.

Understanding and Protecting Your Network Information by Using Data Classification

To start the process of classifying data, we have to look at the methodologies used to organize it prior to classification, each of which have their own advantages and drawbacks.

Content-Based

Content-Based Classification is a centralized and manual read through the information contained in each file; classifying each file based on the sensitivity or type of data it contains. This is the most accurate and consistent of the approaches but requires a central authority to open and read every single file, making it costly and time consuming especially for large companies.

Context-Based

The context-based approach is also centralized but involves the automatic classification based on specific metadata, which could be the type of file, which user or business unit created it, which application or system was used to create it, or where the user was located when creating the file. This method saves a considerable amount of time when compared to content-based classification once it is set up, but it may require regular tuning to ensure files are not being mis-identified.

User-Based

This final approach engages the employees creating the files to label their own work before it is allowed to be saved in the system. The decentralized approach is a quick and easy way to set up a classification schema, but without proper training employees may be inconsistent.

Finding the best approach for your organization requires understanding the size and scope of data owned by the company and what the most important data in your environment is. Multiple approaches can be leveraged at the same time or in succession as a way to initially classify all of your data, then review and refine the framework as necessary.

Four Categories of Data Classification

Once you have developed a plan of action for classifying your data, the next step is to define the categories of data each file will be sorted into. Each business is unique in the data they store, so this step is subjective to an organization’s needs. That being said, many companies use some version of this four-category framework.

Public Data

This is the least sensitive category because this data is created with the intention of being widely shared. Completed marketing materials, user agreements, or product information guides all with the purpose of informing the general public fall into this category.

Internal Data

Data that is not intended for public viewing but could only cause minimal reputational damage to the organization goes here. This could include unfinished drafts of public materials, employee notes and non-sensitive messages, or intellectual property the company would rather not have fall into the hands of competitors.

Confidential Data

Information in this category carries significant legal, regulatory, or ethical ramifications should it be exposed, but may need to be made available to internal or external parties in a controlled manner for business purposes. Information protected by non-disclosure agreements, personally identifiable information (PII) of employees or customers, internal network and data flow diagrams, or protected data that can be requested by certain parties, like student records, may be placed in this category.

Restricted Data

Information must be put in this category if exposing it could include consequences such as criminal or civil penalties, reputational damage, invasion of privacy, identity theft, financial loss including loss of federal funding, or could be used to gain access to more of this category of information. This category includes information all organizations have, such as passwords, encryption keys, financial information, and highly sensitive PII like social security numbers or driver’s license numbers. This is also the tier for specific data regulated by the government like protected health information (PHI), payment card information (PCI), and any classified government data.

It is important to remember that this is just an example and classifying data how it makes sense to your organization is always best way to implement. It is important to keep in mind that the more complex you make your schema, the harder it will be to implement. A three to four category schema is probably the best option unless your organization has a specific need for more than that.

Implementing Data Classification Framework in Your Organization

With the core framework of your classification scheme laid out, it is now time for implementation. As you move forward in your selected classification approach, here are some final points to consider.

Is all of your data accounted for?

Implementing a data classification is as much about identifying data as it is about protecting it. This may require an audit of your network for shadow IT and hidden repositories, as well as determining who has responsibility for different types of data within the network.

Are you set up for long term success?

The hardest part of implementation will be the initial push of classifying all the pre-existing data within the environment and making sure your workforce understands the framework. Organizations have to take a measured and deliberate approach to this starting phase, as it will determine how effective the solution will be.

How are you measuring effectiveness?

One of the core tenants of a good information security program is continuous improvement, and it is no different for data classification. Monitoring of classification activities can be completed through internal and external audits or through security information and event management (SIEM) or data loss prevention (DLP) tools. By creating benchmarking goals and tracking metrics, stakeholders are able to identify areas of improvement and use them to shape company objectives and future initiatives.

Implementing a data classification scheme across the entirety of your business can be a large undertaking but taking time to understand the data profile of your organization and keeping things simple can help to minimize the hassle.

How to Get Started with Data Classification Framework

Data classification can be an effective way of managing your organization’s information while maintaining appropriate security and convenience for all data in the network, but data classification must exist in the context of other security capabilities in order to truly be as effective as it can be.

For organizations with data protected by GDPR, CCPA, or other data subject legislation, data classification can streamline identification, and make providing or destroying relevant data a more efficient process. Once classification is included in a file’s metadata, it can be leveraged by DLP systems at in multiple systems and levels to ensure highly sensitive data cannot be transmitted from the network or stored on systems that may be vulnerable to attack. SIEM and ticketing tools can also leverage this metadata, automatically assigning higher priority and more alerting for events based on data class.

By layering and integrating security solutions in a defense-in-depth approach to information security, organizations are better protected from cybercriminals.

If your organization is wondering if implementing a data classification scheme is right for you, LBMC offers risk assessments and security consulting services that will help identify your areas of improvement based on your current capabilities and today’s threat landscape. Contact our team today!