A complete guide to data classification

Data classification - or the process of categorizing data into relevant subgroups so that it is easier to find, retrieve, and use - is a foundational step to achieving compliance. Yet, it is often overlooked. Not all data is created equal, and classification can help you better understand the varying levels of sensitivity that surround your organization’s data to ensure you remain in compliance with laws such as the GDPR and HIPAA. This article offers a complete guide to data classification, starting with how we classify data into categories and data classification approaches and best practices.

We typically classify these levels of data sensitivity into three categories: 

• Low Risk - Low-sensitivity information that is readily available to the public and does not require any special protections (think: public web pages, job postings, company blog posts, etc.). 
• Medium Risk - Information that is for internal-use only, but would not cause the organization harm if it were to be exposed through a data breach. An example of this includes non-identifiable personal information.
• High Risk - Highly-sensitive information protected by laws such as GDPR, CCPA, and HIPAA that requires extensive protection. If breached, this information could pose significant harm to the organization and its customers. High-risk data includes social security numbers and credit card information.

Choosing the right approach for Data Classification 

The need to classify this data and understand where it falls on the sensitivity scale is key to mitigating risk and making sure all high-risk data is properly secured. So how exactly does data classification work? Organizations can either leverage a user-based manual method, a tool that automates this process, or a hybrid combination of both. 

As the traditional method for classifying data, a manual approach requires human intervention. While this method allows files to be viewed and organized in a highly-personalized manner according to your company’s preferences, it is also time-consuming and opens up the possibility of human error. In addition, a manual approach does not give the organization the flexibility to scale and is increasingly difficult to do with the influx of information that businesses are handling today. 

As an alternative, organizations can take an automated approach to data classification by leveraging an efficient, technology-driven solution that reduces the degree of human intervention and errors. Depending on the amount of data an organization is dealing with, an automated approach may be the only efficient way for an organization to accurately classify data and make sure all of its information is being accounted for. 

More often than not, most classification projects will require some degree of automation, while also enlisting a user-based approach where appropriate. This hybrid method combines the benefits of personalization, with the efficiency and scalability of automation. 

Data Classification steps & best practices

Regardless of which approach makes the most sense for your organization, a successful data classification project typically follows a similar process to reaching compliance. The following steps are best practices to keep in mind when embarking on a classification journey: 

1. Define Objectives - Understand the compliance requirements that apply to your organization and what you are trying to ultimately achieve.
2. Categorize Data - Identify what kinds of data your organization is holding onto and where this falls on the sensitivity scale mentioned above. 
3. Create Workflows - Identify processes going forward to scan and discover the new data coming into the organization. Data discovery tools are a great way to handle this.
4. Define Outcomes - Understand the intended use of classified data and identify how to organize this information in a way that maximizes its value for business decisions.  
5. Monitor & Maintain - Continue classifying and discovering new data to ensure sensitive information is being protected and your organization remains in compliance.

While this process can be done manually, it evidently consumes a significant amount of time and effort and requires continuous monitoring to be effective. Using an automated data classification tool can help to process large amounts of information more efficiently and synthesize some of these best practices. Embracing the productivity and timeliness of these tools enables organizations to free up more time for team members to focus on what really matters: using this classified information to make informed business decisions, mitigate risk - and ultimately ensure data compliance. 

Use Ground Labs’ Data Classification Tool 

Ground Labs offers Enterprise Recon, a globally recognized solution for data discovery. It is able to detect over 300 data types, it is also agile, which is key to the ever-changing compliance and cybersecurity landscape. 

If you are ready to find, classify and harness the power of your company’s data, book a demo with one of Ground Labs’ experts today.