Microsoft Sentinel Deployment Best Practices

 





Microsoft Sentinel Cloud-Native SIEM Architecture

The first cloud-native security information and management system (SIEM) from a major public cloud provider is Microsoft Sentinel. It can be deployed in enterprise's Azure tenant and can be accessed through Microsoft Azure gateway. It guarantees compatibility with previous policies for organizational access control. 

Making use of integrations with Microsoft Defender tools and Azure services, like Log Analysis and Logic Apps for analysis and automation capabilities, Microsoft Sentinel enables organizations to obtain  security signals and analyze them completely.

Harnessing the power of flexible computing and capability of storage, built into Azure for the use of heavy data programs like SIEM, is a significant benefit as compared to on-site solutions for analyzing logs. 

Moreover, Microsoft Sentinel has the capability to utilize Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) features offered in Azure to provide workflow automation and analysis services as well as the extension in storage of log data, which is only available as additional services by other SIEM providers.

Microsoft Sentinel for Security Operations

Benefits offered to Security Operations and Content Engineering teams may become even more impactful. The new threat detection content developed in KQL can be easily deployed via various methods, including tools like Azure DevOps for internal content teams, or by leveraging Azure Lighthouse to enhance and extend content development through a partner or MSSP. 

Many times, the security teams does both concurrently, developing internal SecOps capabilities while partnering with an external organization that can offer fast and threat-centric detection content. 

Conclusion

This is a small introduction to the deployment best practices and what could be expected from it. 












































Comments

Popular posts from this blog

Query, Visualize, & Monitor Data in Azure Sentinel

Planning for Implementing SAP Solutions on Azure (Part 2 of 5)

Work with String Data Using KQL Statements