![]() ![]() Vault makes a connection to the database and generates a set of restricted access credentials. When you want to log into the database, you ask Vault for credentials. For example, you give Vault the root credentials for your PostgreSQL database, granting it access to create credentials on your behalf. With dynamic secrets, you delegate the responsibility to Vault for creating and managing the lifecycle of a secret. For example, you could store an email account password in Vault but you need to ensure that it is periodically changed. With static secrets, you must create and manage the lifecycle of the secret. Vault generates dynamic secrets on-demand, while you receive static secrets already pre-defined. You can have static secrets like an API key or a credit card number or dynamic secrets like auto-generated cloud or database credentials. In this section, we review how these concepts work in Vault. Vault is built around three main concepts: If you do not have access to a Kubernetes cluster with Vault and would like to try the features in this blog, you can use the online Instruqt environment which has Kubernetes and Vault installed:Īlternately you can use Shipyard to create a local Docker-based Kubernetes cluster with Vault pre-installed:Īll the example configuration mentioned in this post is available can be cloned from GitHub: Vault can be installed on Kubernetes using the latest version of our Helm Chart: To try out the features listed in this blog you need a Kubernetes environment with Vault. Let's introduce some HashiCorp Vault’s core concepts. username: v-kubernet-db-app-QDTv8wn6oGze6aGxuYbQ-1576778182īefore we will walk through the process of deploying and configuring Vault in a Kubernetes cluster and learn how to inject PostgreSQL credentials into a Kubernetes deployment. Spec : replicas : 1 selector : matchLabels : app : webĪnnotations : /agent-inject : "true" /agent-inject-secret-db-creds : "database/creds/db-app"īy convention Vault will inject these at the path /vault/secrets/, the following snippet shows an example of this file. Adding the annotations shown in the following example automatically inject secrets controlled by the db-creds role into the pod. The retrieved secrets are written to a pod volume mount that your application can read.įor example, we can use Vault to dynamically generate database credentials for a PostgreSQL database. This sidecar manages the authentication to Vault and the retrieval of secrets. ![]() ![]() When a new deployment is submitted to Kubernetes, a mutating webhook modifies the deployment, injects a Vault sidecar. Injecting Secrets into Kubernetes Deployments.Creating Policy to Allow Access to Secrets.Configuring Kubernetes Authentication in Vault.Configuring Dynamic Secrets for PostgreSQL.The integration automatically handles all the authentication with Vault and the management of the secrets, the application just reads the secrets from the filesystem. In this blog post, we will look at how the Vault integration for Kubernetes allows an operator or developer to use metadata annotations to inject dynamically generated database secrets into a Kubernetes pod. Vault manages the lifecycle of credentials, rotating and revoking as required. HashiCorp Vault solves this problem by enabling operators to provide dynamically generated credentials for applications. The reality is that without automation, it is impossible to satisfy them. While these requirements are essential for reducing the blast radius in the event of an attack, they are operationally challenging. Access should be restricted by application function, a system which only needs to read a specific table, should have database access which grants this particular purpose.Credentials should be short-lived and rotated frequently.Credentials should be disabled or deleted when a pod terminates.Each Kubernetes pod should have a unique set of credentials.For optimum security, we ideally need to implement the following requirements for database credentials: Providing database credentials for your Kubernetes applications has always proved operationally challenging.
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