Skip to main content

Automation Rules inside Azure Sentinel

 Nowadays, automation is part of our day-to-day life. To be able to react to security incidents, it is not enough to detect them. We need a mechanism that can trigger an action when an incident is detected. 

Azure Sentinel is a cloud-native SIEM (security information and event manager) that analyze what is happening inside your organization and can detect a possible security breach. Azure Sentinel can automatically raise an alert when an incident occurs.

This is not enough, and to increase the SOC efficiency, reduce the response time and the no. of resources that you have available, you need to be able to implement SOAR (Security Orchestration, Automation and Response) on top of it. 

Automation is the keyword here; we can now do it inside Azure Sentinel using Automation Rules. 

Except for an alert and running a playbook the automation rules allow us to react to multiple analytics rules at once and automatically assign or close incidents. It is a mechanism that enables us to do the orchestration, incident orchestration, on top of Azure Sentinel. Complex workflows for different types of incidents can now be defined and part of Azure Sentinel directly. 

The running and trigger SLAs for automation rules that we define and the playbooks trigger are under a few seconds and take into account how you define the rule and how long each playbook takes. 

As we expected when we define automation rules there are the following components:

  • Trigger
  • Conditions
  • Actions
  • Order
  • Expiration date
The expiration date is a nice feature that makes our life much easier especially when we run a security test (e.g. penetration testing) or we have a time window for specific activities. For example, during a penetration test, we might want to define automation that changes the severity to low and automatically close the incident. This can be achieved easily using the expiration date and the order component. Without automation rules, incident suppression is not easily achieved during penetration testing, especially in the production environment. 

With automation rules, we have the ability to reuse the playbooks. For example, we could have a playbook that automatically generates a ticket inside ServiceNow and do automatically assignment of incidents taking into account the SOC specialization and who is on-call at that moment in time. 

The last thing that I want to mention is the tagging capability. It enables us to add tags to each incident. Useful for larger organizations, where you want to filter the incidents on your own custom tags and rules. 

Don't forget that at this moment in time (Oct 2021), automation rules are still in public preview. Play with them in non-production env. and be ready to push them to PROD once they are GA (General Availability)


Popular posts from this blog

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

How to audit an Azure Cosmos DB

In this post, we will talk about how we can audit an Azure Cosmos DB database. Before jumping into the problem let us define the business requirement: As an Administrator I want to be able to audit all changes that were done to specific collection inside my Azure Cosmos DB. The requirement is simple, but can be a little tricky to implement fully. First of all when you are using Azure Cosmos DB or any other storage solution there are 99% odds that you’ll have more than one system that writes data to it. This means that you have or not have control on the systems that are doing any create/update/delete operations. Solution 1: Diagnostic Logs Cosmos DB allows us activate diagnostics logs and stream the output a storage account for achieving to other systems like Event Hub or Log Analytics. This would allow us to have information related to who, when, what, response code and how the access operation to our Cosmos DB was done. Beside this there is a field that specifies what was th

ADO.NET provider with invariant name 'System.Data.SqlClient' could not be loaded

Today blog post will be started with the following error when running DB tests on the CI machine: threw exception: System.InvalidOperationException: The Entity Framework provider type 'System.Data.Entity.SqlServer.SqlProviderServices, EntityFramework.SqlServer' registered in the application config file for the ADO.NET provider with invariant name 'System.Data.SqlClient' could not be loaded. Make sure that the assembly-qualified name is used and that the assembly is available to the running application. See for more information. at System.Data.Entity.Infrastructure.DependencyResolution.ProviderServicesFactory.GetInstance(String providerTypeName, String providerInvariantName) This error happened only on the Continuous Integration machine. On the devs machines, everything has fine. The classic problem – on my machine it’s working. The CI has the following configuration: TeamCity .NET 4.51 EF 6.0.2 VS2013 It see