1. Event-driven Security Analytics with MongoDB Atlas and SIEM Tools


    Event-driven security analytics involves collecting and analyzing data from various security devices and applications to detect and respond to potential security threats in real-time. In the context of cloud services, this can include integration with security information and event management (SIEM) tools.

    To set this up with MongoDB Atlas as our database service, we will orchestrate the following:

    1. Enable MongoDB Atlas auditing to keep track of activity.
    2. Integrate SIEM tools that are compatible with MongoDB for analytics.

    For this program, I will focus on setting up MongoDB Atlas auditing using Pulumi, which enables us to export audit logs that could be used with security analytics tools. You will need to set up the SIEM tool separately to consume the MongoDB audit logs, which is outside the scope of this coding session.

    Here's a Pulumi program written in Python that demonstrates how to enable auditing in MongoDB Atlas. Note that you need to replace "<your-project-id>" with your actual MongoDB project ID, and the auditFilter should be configured according to your organization's requirements.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Enable auditing in MongoDB Atlas auditing = mongodbatlas.Auditing("auditing", project_id="<your-project-id>", audit_filter='{ "atype": { "$in": [ "authenticate", "createIndex", "dropCollection", "dropDatabase", "dropIndex", "insert", "remove", "renameCollectionSameDB", "update" ] } }', audit_authorization_success=True, ) # Output the auditing ID to use in your SIEM integration pulumi.export("auditing_id", auditing.audit_filter)

    This code does the following:

    • Imports the necessary modules for Pulumi and the MongoDB Atlas provider.
    • Instantiates an auditing resource which dictates what actions to log.
    • Sets an audit_filter which specifies the types of operations to be audited.
    • Uses audit_authorization_success to log successful authentication attempts.
    • Exports the audit filter so you can refer back to it or use it as a parameter when integrating with a SIEM tool.

    To proceed, replace the placeholders in the project_id and audit_filter as per your MongoDB Atlas setup. After running this Pulumi program, you'll have the necessary auditing enabled in MongoDB Atlas, and the resulting logs can be consumed by a SIEM tool for event-driven security analytics.

    Please note, setting up the SIEM tool integration would typically require additional configuration such as specifying the destination for the audit logs (like a syslog server, a Kafka topic, or an AWS S3 bucket) and ensuring your SIEM tooling is set up to ingest data from that destination.

    For integrating and configuring your SIEM tool, you should refer to the specific instructions provided by both MongoDB for audit data forwarding and the SIEM tool you choose. Each SIEM tool (e.g., Splunk, Sumo Logic, LogRhythm) has different methods for data ingestion and analysis which will require specific configurations.

    Remember to consult the MongoDB Atlas documentation on auditing and your chosen SIEM tool documentation for specifics on their usage, configuration, and best practices.