One of the best practices is to classify your data, which provides a way to categorize and handle organizational data based on levels of sensitivity. According to data sensitivity, you can plan data protection, data encryption, and data access requirements.
By managing data classification as per your system's workload requirements, you can create the data controls and level of access needed for the data. For example, content such as a user rating and review is often public, and it's fine to provide public access, but user credit card information is highly sensitive data that needs to be encrypted and put under very restricted access.
At a high level, you can classify data into the following categories:
- Restricted data: This contains information that could harm the customer directly if it got compromised. Mishandling of restricted data can damage a company's reputation and impact a business adversely. Restricted data may include customer Personally Identifiable Information (PII) data such as social security numbers, passport details, credit card numbers, and payment information.
- Private data: Data can be categorized as confidential if it contains customer-sensitive information that an attacker can use to plan to obtain their restricted data. Confidential data may include customer email IDs, phone numbers, full names, and addresses.
- Public data: This is available and accessible to everyone, and requires minimal protection—for example, customer ratings and reviews, customer location, and customer username if the user made it public.
You can have a more granular category depending on the type of industry and the nature of the user data. Data classification needs to balance between data usability versus data access. Putting different levels of access, as mentioned previously, helps to restrict only the necessary data and make sure sensitive data is not exposed. Always avoid giving direct human access to data and add some tools that can generate a read-only report for users to consume in a restrictive manner.