New Azure Security Center Capabilities Revealed At RSA 2019

Azure Security Center now leverages machine learning to cut the attack surface of internet-facing virtual machines.

Microsoft at RSA Conference 2019 announced that Azure Security Center now leverages machine learning to reduce the attack surface of internet facing virtual machines. The company has extended Azure Security Center's adaptive application controls to Linux and on-premises servers. The network map support has also been extended to peered virtual network (VNet) configurations.
 
Azure Security Center 
Source: Microsoft 
 
The company said that Security Center is now able to determine the network traffic and connectivity patterns of your Azure workload. It will provide you with NSG rule recommendations for your internet facing virtual machines. This will help you better configure your network access policies.
 
"Azure Security Center uses machine learning to fully automate this process, including an automated enforcement mechanism, enabling its customers to better protect their internet facing virtual machines with only a few clicks. These recommendations also use Microsoft’s extensive threat intelligence reports to make sure that known bad actors are blocked." wrote the company.
 
The company has also extended adaptive application controls in Azure Security Center to incorporate Linux VMs and servers/VMs external to Azure (Windows and Linux) in audit mode. Azure Security Center will now be able to identify applications running on your servers which are not in agreement with the Azure Security Center generated whitelisting regulation and will audit those violations. This will empower you to discover threats that might otherwise be missed by antimalware solutions.
 
Azure Security Center’s network map has added support for virtual network peering. The support includes displaying allowed traffic flows between peered VNets and peering related information on Security Center’s network map.
 
To know more details, you can visit the official announcement here.