![]() ![]() ![]() A case when you might not need these is if you don’t have any highly sensitive data like credit card numbers or Social Security numbers in your database. For example, you might not need some of the specialized security controls like tokenization of data or security microsegmentation, both described later in this post. To decide which of the security controls described later in this post apply to you, understand the classification of your data. For a deeper description than provided here and for the concepts behind data classification and security-zone modeling, see the first post of the series. Data classification and security-zone modelingįor a refresher on data classification and security-zone modeling, see following. Let’s walk through the implementation of the security concepts in the order in which they were described in the first post. In these cases, I include implementation examples from Amazon Aurora with MySQL compatibility but also point you to where to get the information for other database engines. In this second post, I demonstrate how these concepts can be implemented to Amazon RDS databases.Īlthough many of the implementation examples are common to all RDS database engines, a few might differ based on the individual engine type. Using these, you can create a stronger security posture around your data. In the first post of the series, I described some generic security concepts and corresponding AWS security controls that can be applied to data stores on AWS.
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