As many businesses have rapidly moved to a more paperless system over recent years, you probably don’t even realize how much data you unconsciously handle every day. When we think about data we might immediately think of things like financial records or confidential customer information, but even the emails you send and receive every day or documents you share internally with colleagues can contain data, and all of this needs to be stored and handled in the right way.
Handling this sea of data might seem a pretty overwhelming task, but don't worry – there's a simple way to keep it all organized and secure: data classification. It’s a great way to make sure your sensitive information stays safe, you’re ticking all the compliance boxes, and your resources are being used wisely.
What is Data Classification?
The term data classification might sound like a technical or complex process, but it essentially means organizing your data into different categories so you can more easily manage, protect, and access it. Think of it like sorting through a pile of documents and labeling each one with a digital sticky note.
Then, you can easily distinguish between different types of data and know how to handle each one based on its importance and sensitivity. For example, marketing materials might be available to the public and shared widely, so these don’t need too much careful management.
On the other hand, data like financial documents or personal information from customer forms and surveys will need tighter control to prevent it from falling into the wrong hands or being open to abuse.
Why is Data Classification Important?
Before we get into the how of setting up data classification, let's take a quick look at why it’s important:
Data security: Data classification allows you to concentrate your data security in areas that need it most. For instance, sensitive data such as customer payment information requires more protection compared to public data like a company press release.
Regulatory compliance: In certain fields, like healthcare and finance, there are specific regulations on how data should be handled that need to be followed. Regardless of what industry you’re in, if you’re handling customer data, you need to comply with necessary data legislation.
For example, if you’re operating a cloud-based call center, you’ll need to make sure that sensitive customer data, such as call recordings or payment information, is properly tagged and protected to meet these regulatory standards.
Effective resource allocation: After categorizing your data you can identify exactly what to allocate resources for, such as encryption, backups, and monitoring. This can prevent you from wasting time or money putting high-security measures in place on data that doesn’t need high levels of protection.
Data governance: By categorizing data, you can ensure that the right policies and controls are applied to different types of information. This helps improve the accuracy, accessibility, and consistency of data across the organization, as it ensures that sensitive information is protected, while less critical data is easily accessible to the appropriate users.
Risk management: Knowing where your sensitive data resides and how it’s being accessed can reduce the risk of data breaches, helping you protect both your business and your customers. If you want to learn more about risk management and how it ties into data protection, attending digital transformation conferences can be a great idea.
Step 1: Understand Your Data
Before you jump straight into categorizing your data, you need to start by getting a good idea of the different types of data you’re going to be dealing with. You don’t want to be adding in data types as you’re going along, as this could quickly get messy or confusing.
So, begin by doing a data inventory and mapping out which data types you will need to classify within your organization.
Depending on the sort of data you routinely handle, this could typically include things like:
Customer records.
Employee information.
Financial data.
Intellectual property.
Communication logs.
Transaction histories.
Starting the process with a clear picture of the different data types you’re working with will help to make sure you have all bases covered and nothing slips between the cracks.
Step 2: Define Classification Levels
Now you’ve got a good grasp of your data, it’s time to decide how you’re going to classify it. This basically means the different levels of security that you’ll apply to your data, depending on how it needs to be stored and managed.
There’s no hard and fast rule for how you apply these classification levels, as it really depends on the needs of your organization, but a typical model might look something like this:
Public: This is data that can be shared far and wide without any real concerns. It would generally include things that are already in the public domain – like marketing materials, press releases, or reports, and industry white papers that you’ve published publicly.
Internal use only: This covers information that isn’t really intended for public consumption, but where there are no disastrous consequences if it does get accidentally leaked. It might be things like non-sensitive emails or internal memos, for example.
Confidential: Confidential data is anything that needs to be kept secure and, if breached, could pose a risk. Things like customer data and confidential business information are obvious examples, but don’t forget about less obvious stuff, like internal strategy plans or product development notes, which could also be harmful if leaked and make your organization vulnerable to cyber threats.
Highly confidential/restricted: This is your most sensitive data that needs to be handled with the highest level of care. If this data gets out, the consequences are likely to be more than a mild headache – it could result in legal action, severe financial loss, or major damage to reputation. Think trade secrets, financial records, or personal health information.
Keep in mind that these levels are just examples. You’ll want to tailor your classification levels to suit your organization's specific needs.
Step 3: Establish Clear Classification Criteria
Once you’ve got your classification levels mapped out, it’s time to create the criteria that will be used to decide which data falls into which category.
This is an important step in ensuring consistency across the organization and in not leaving the classification up to individual interpretation.
Your criteria might include:
Content: What is the nature of the information? Is it inherently sensitive, such as financial data or a Social Security number?
Context: What is the purpose of the data? Who created the data, and how is this data used? Is the data affiliated with a certain department or project?
Regulations: Does this data fall within any data privacy regulations (e.g., GDPR, HIPAA)? Then it might need a higher level of protection.
Access level: Who needs to access this information? All employees, one department, or just a few individuals?
Source: Consider the origin of the data. For example, internal web design projects may require different handling than external marketing content.
Step 4: Implement Classification Tools
Now that you’ve got your classification levels and criteria in place, the next step is to get stuck in and start applying them to your data.
Depending on the size of your organization and how much data you’ve got to work through, you can do this process manually, use automation tools, or even use a mix of both.
Automated Data Classification Tools
You can use automated tools to do the majority of the leg work for you in classifying your data. These tools can automatically scan, classify, and tag data for you by analyzing content, metadata, and user interactions to determine the appropriate classification level.
Using automated classification can be a lifesaver when you’re dealing with large volumes of daily data or backdating large datasets that would be overwhelming to do manually. These tools can be even more effective when used alongside enterprise architecture benefits.
Popular tools for this include:
Varonis
Symantec Data Loss Prevention
Google Cloud Data Loss Prevention
Manual Data Classification
Automated classification can save you a huge amount of time and effort, but it’s not always appropriate; you might find you still need to manually classify at least some of your data.
This is usually data that requires a degree of human judgment to determine its sensitivity, for example classifying personal information based on its level of confidentiality.
If you’re using manual classification, you need to make sure that employees are properly trained on how to correctly classify new data and update existing classifications as needed.
Step 5: Train Employees and Create Awareness
All your data classification efforts can quickly go to waste if the procedures you have put in place aren’t followed consistently. Every single employee in your organization must understand how important it is for data to be classified properly.
Initial training: When you first implement data classification, hold training sessions to show employees how they should categorize data correctly.
Ongoing training: Don’t assume that once data classification is well established in your organization you can sit back and forget about it. Familiarity can breed complacency, and new employees might not know the correct procedures. Plus, regulations could change and catch you off guard. It’s a good idea to provide regular refresher courses on best practices and to remind employees how important it is to classify data.
Real-world scenarios: To help employees get a better understanding of data classification, you can use examples and real-life scenarios of it in practice. For example, you could explain how email analytics reports that include personal information should be classified as “Confidential”, whereas a general internal announcement might be low-risk and labeled as “Public”.
Taking Control of Your Data
Data classification doesn’t have to be as overwhelming as it might first seem. By breaking it down into simple steps, you can soon get to grips with your data and create a system that keeps everything tidy, safe, and compliant. So, roll up your sleeves and get organizing! Want to learn more about protecting your data? Check out Ironworks Cybersecurity Bootcamps.
Author:
Diana Nechita, Director of Product Marketing
Diana is the Director of Product Marketing at Ardoq. Her passion lies in fostering a deep understanding of Ardoq’s value in delivering tangible results for organizations navigating the complexities of digital transformation. Follow her on LinkedIn.
