Amazon Aurora is a fully managed relational database engine that is compatible with MySQL and PostgreSQL. Panther can collect, normalize, and monitor Aurora MySQL Relational Database Service (RDS) logs to help you identify suspicious activity in real time. Your normalized data is then retained to power future security investigations in a serverless data lake powered by Snowflake.
The AuroraMySQLAudit log is an RDS Aurora audit log containing context on database events such as connections, disconnections, and table queries. Common security use cases for Aurora audit logs include monitoring for:
Panther’s integration for Aurora is easy and fast to configure through the use of AWS S3 as a data transport. Simply select AWS Aurora MySQL from the list of pre-defined log sources in Panther, select AWS S3 Bucket as your data transport method, and configure your S3 bucket to stream AWS Aurora logs.
For more detailed steps on onboarding Aurora audit logs or for supported log schema, you can view our AWS Aurora documentation here.
As Panther ingests Aurora audit logs, they are parsed, normalized, and stored in a Snowflake security data lake. This allows security teams to craft detections, identify anomalies, and conduct investigations on logs in the context of days, weeks, or months of data.
Panther applies normalization fields to all log records, which standardizes names for attributes and empowers users to correlate data across all log sources. Panther’s search tools - Data Explorer, Indicator Search, and Query Builder - allow you to investigate your normalized logs for suspicious activity or vulnerabilities. For more on searching log data in Panther, check out our documentation on Investigations & Search.
With Panther, your team won’t be confined to rigid detection rules or proprietary languages as seen in most legacy SIEM platforms. Panther is built with detection-as-code principles, giving you the ability to write Python to define detection logic and to integrate external systems like version control and CI/CD pipelines into your detection engineering workflows. This results in powerful, flexible, and reusable scripting of detections for your security team.
Panther fires alerts when your detection rules or policies are triggered, and integrates with a variety of alert destinations to allow for easy access and management of any Aurora alerts. Alerts can also be sent to alert context or SOAR platforms for more remediation options.
Alerts are categorized in five different severity levels: Info, Low, Medium, High, and Critical. Security teams have the options to dynamically assign severity based on specific log event attributes.
If you have any questions about configuring or monitoring Aurora access logs in Panther, we’re here to help. All customers have access to our technical support team via a dedicated Slack channel, email, or in-app messenger.
You can view our documentation on configuring and monitoring AWS Aurora logs here, or customers can sign up for the Panther Community to share best practices or custom detections for monitoring your Aurora.
With Panther, security teams don’t have to struggle with restrictive detection logic, waste time and resources on operational overhead, or pay skyrocketing costs to keep up with the growth of cloud app data. Panther was founded by a team of veteran security practitioners who struggled with legacy SIEM challenges first-hand, and built an intuitive, cloud-native platform to solve them.
Panther is a cloud-native SIEM built for security operations at scale, offering flexible detection-as-code, intuitive security workflows, and actionable real-time alerts to keep up with the needs of today’s security teams. For a powerful, flexible, and scalable SIEM solution for AWS Aurora, request a demo today.
Monitor the configuration of your AWS resources.
Investigate traffic that is analyzed by your web Access Control Lists (ACLs).
Monitor your AWS environment for malicious activity and unauthorized behavior.
Identify any suspicious activity within your AWS infrastructure.
Correlate AWS Security Hub findings.
Gain complete visibility into activity across your cloud service.
Monitor requests sent to your load balancer for suspicious activity.
Monitor any resource changes within your AWS environment.
