Definition of Data Classification
Data classification is broadly defined as the process of categorizing data so that it can be used and protected more effectively. At its most basic level, the classification process facilitates the location and retrieval of data. Data classification is especially important in risk management, compliance, and data security.
Data classification is the labeling of data in order to make it more searchable and trackable. It also removes multiple data duplications, which can save storage and backup costs while speeding up the search process. Even though the categorization process may seem very technical, the people in charge of your organization should know about it.
Which of the following statements about protecting classified data is correct?
- Ensure proper labeling by labeling all classified material properly.
- It must be made available to the public as soon as possible.
- Store classified data in a GSA-approved vault/container.
- Make a note of any identifying information and the website’s URL.
The correct answer to protecting classified data –
Ensure proper labeling by labeling all classified material properly.
Let us understand the process of protecting classified data.
Analysis of Data Risk
In addition to classification kinds, an organization should assess the relative risk associated with the types of data, how that data is managed, and where it is stored (endpoints). It is common practice to categorize data and systems into three levels of risk.
Low risk: If the data is public and not easily lost (e.g., recovery is simple), this data collection and the systems surrounding it are likely to be less risky than others.
This is data that is not publicly available or is used internally (by your organization and/or partners). However, it is unlikely to be too vital to operations or sensitive to be considered “high risk.” Moderate responsibilities may include proprietary operating processes, cost of goods, and some company paperwork.
High risk: Anything remotely sensitive or critical to operational security is classified as high risk. Furthermore, data is incredibly difficult to recover (if lost). All confidential, sensitive, and necessary data is classified as high risk.
Some people use a more granular scale, adding “extreme” risk or other categories to further differentiate the data.
Application of a Data Classification Matrix
Some businesses may find it simple to create and label data. If your organization has fewer transactions or has fewer data types, identifying the risk of data and your systems is likely to be easier. However, many businesses working with big volumes or multiple types of data are likely to require a full risk assessment. Many people utilize a “data classification matrix” for this.
By making a matrix that ranks data and/or systems based on how likely they are to be broken into and how sensitive the data is, you can quickly figure out how to better classify and protect everything sensitive. Protecting classified data by making a matrix is very helpful.
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A Data Classification Example
Data can be classified as restricted, private, or public by an entity. In this case, public data is the least sensitive data with the least stringent security requirements, whereas restricted data has the highest security classification and is the most sensitive data. Many businesses begin with this form of data classification, followed by subsequent identification and tagging operations that identify data based on its usefulness to the enterprise, quality, and other classifications. To keep sensitive data where it belongs, the most successful data classification systems include follow-up processes and frameworks.
