What is data classification and how hard is it? At first sight, data classification doesn’t look hard. There are four key steps in the process:
- Entering assets, such as email and electronic documents, in the asset register
- Classifying each asset according to its sensitivity
- Labeling the asset based on how it is classified
- Handling the asset based on its classification (for example, encrypting emails that are classified as “confidential”)
Performing these steps manually requires training data owners or other users to classify documents according to your data classification policy. While humans are much better than machines at understanding the context of documents, as the number of assets, classification levels and rules grows, data classification quickly gets complicated and manual processes break down. Here are five reasons for automating data classification.
Reason #1. Manual data classification is subjective and inconsistent.
As the saying goes, beauty is in the eye of the beholder. Similarly, deciding what information is sensitive can be subjective. Even if two people are both trained to classify data, they will sometimes tag similar data differently. In fact, even a single individual might label similar content inconsistently, especially if there are a lot of tags to choose from. Finally, the quality of data classification also depends on the owner’s commitment, which can lead to inconsistency across the organization.
Reason #2. Users neglect data classification because they don’t perceive any value in it.
Data classification is rarely baked into business processes; instead, it is an awkward additional task that users see little value in bothering with, so they skip it if they can. If management requires them to do it, they often do a poor job, for example, just selecting the first tag on the list to expedite the task. This leads to incomplete or incorrect classifications — and having wrong information about your data can be even worse than having no information at all, since you might focus on the wrong data and leave your truly valuable assets with insufficient protection.
Reason #3. Data isn’t static.
Files are usually classified at the time of creation, but data — especially unstructured data — changes all the time. There’s no guarantee that a file’s original classification will remain accurate through its entire lifecycle. And as we just saw, if the classification isn’t correct, the information handling rules applied to it might be insufficient to protect the data.
Reason #4. Manual classification is complex and expensive.
Most organizations have both a wide variety of data and a large volume of data. As a result, classifying it by hand is a complex, time-consuming and expensive task.
Reason #5. It’s easy to miss sensitive information.
Whenever you begin classifying data, you can certainly have users classify new files as they are created. But what about all the data already stored on your file servers? If you omit it as being out of scope, you risk leaving sensitive data vulnerable. But trying to manually sort through masses of unstructured data would be prohibitively time consuming and extremely prone to errors. It would be like trying to manually do the work of a search engine that crawls the internet to index data.
There are some strategies that can help mitigate these challenges with manual data classification. You can try:
- Giving employees extra training to improve the consistency of their classification work
- Simplifying your data classification scheme
- Classifying folders instead of individual documents to save time if you have lots of data
- Excluding entire departments from classification if their data doesn’t seem likely to contain the most vital assets you need to focus on first
However, a data discovery and classification software will deliver far better results with far less effort, ensuring that classification rules are applied consistently and that data sensitivity is re-evaluated when data changes. As a result, you won’t have to worry that you’ll leave your most critical assets vulnerable based on incorrect information.