The Windows Event Log captures notification details for application, system, and security activities from Windows operating systems. Panther can collect, normalize, and monitor Windows Event Logs to help you audit security events, correlate data with other log types, or diagnose problems or trends on Windows machines. Your normalized data is then retained to power future security investigations in a serverless data lake powered by Snowflake.
Use Cases for Windows Event Logs
There are five types of events captured by Windows Event Logs: errors, warnings, successful security access attempts, failed security access attempts, and information notifications. Common security use cases for monitoring Windows Event Logs include:
- Monitoring user actions and system events using Sysmon
- Identifying potential security breaches or suspicious behavior on your Windows machines
- Corroborating Windows OS activity details with other data sources
Onboarding Windows Event Logs in Panther
Windows Event Log data can be streamed directly to Panther via HTTPS. To set up a Windows Event Log data stream in Panther, you’ll first create a new HTTP Source within the Panther console, and then create a Fluent Bit configuration file to forward your Windows Event Log data.
For more detailed steps on onboarding Windows Event Logs or for supported log schema, you can view our Windows Event documentation here.
Normalizing & Analyzing Windows Event Logs
As Panther ingests Windows Event Logs, they are parsed, normalized, and stored in a Snowflake security data lake. This empowers 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 and investigate data across all log types. For more on searching log data in Panther, check out our documentation on Investigations & Search.
Detection as Code
With Panther, your team won’t be confined to rigid detection rules or proprietary languages as seen in many 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 Windows Event Log alerts. Alerts can also be forwarded 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 Windows Event Log data 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 Windows Event Logs here, or customers can sign up for the Panther Community to share best practices or custom detections for monitoring Windows Event.
The Ideal SIEM for Windows Event Logs
With Panther, you won’t have to struggle with restrictive detection logic, waste time and resources on operational overhead, or pay skyrocketing costs as you scale up data ingestion. Panther was founded by a team of veteran security practitioners who struggled with legacy SIEM challenges first-hand, and built an intuitive 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 Windows Event Logs, request a demo today.