As organisations increasingly operate in digital environments, they are creating and handling ever-increasing volumes of sensitive data, including customer information, employee records and confidential business data. This will also include personal data, a special category that carries additional legal protections and obligations.

A robust data classification and labelling process is therefore essential for managing information security and meeting these legal and regulatory obligations.

What is Data Classification?

Data classification involves categorising data based on its sensitivity and potential impact if compromised. Examples include:

  • Government classifications: Labels such as “Top Secret,” “Secret,” or “Confidential,” indicate the severity of potential national security risk.
  • Corporate classifications: Labels such as “Highly Confidential,” “Internal Use,” or “Public” set expectations on business and regulatory requirements, eg. GDPR.

From Physical Tags to Metadata

The practice of labeling data has evolved alongside classification. In the days or paper, documents would be annotated, stamped or tagged to indicate their protection level. Nowadays electronic labeling involves embedding metadata within or alongside the data itself. This metadata acts as an electronic tag, specifying how the data should be handled, accessed, and secured. For example, in applications like Microsoft Office, users might be prompted to select a data classification level when creating or saving a document. This label then travels with the document, informing subsequent users and systems about the appropriate security protocols to maintain. 

Implementing Effective Data Classification and Labelling

Several factors should be considered when implementing a data classification and labeling system. These include defining clear classification levels, establishing consistent labeling procedures, and training users on how to apply them correctly. Furthermore, organisations must consider the technical infrastructure needed to support automated labeling and then enforce data handling policies based on these labels.

Decision Fatigue -> AI Assistance?

Where users are continually prompted to select data classification levels, there is a risk of decision fatigue where users are simply blindly clicking through to the next prompt. This can lead to inaccurate labelling and undermine the whole data protection objective.

This is an area that AI is helping with, by using content analysis to suggest labels, applying labels automatically, and monitoring compliance of labelling.

Conclusion

In conclusion, effective data classification and labeling are essential ensuring data protection, as well as meeting the corresponding legal and regulatory obligations. By understanding the sensitivity of their data and applying appropriate labels, organisations can implement targeted security measures, comply with regulations, and minimise the risk of consequential data breaches.