Azure AD Identity Protection (part 2 of 3)

By Ashwin Venugopal -

 




To read part 1, please click here
To read part 3, please click here




Detect Vulnerabilities & Risk Events

Vulnerability is defined as a weakness which can be exploited by a threat actor i.e. an attacker to perform unauthorized actions within a computer system. The vulnerabilities reported by the Azure Identity Protection are as follows:
  • Multi-factor Authentication Registration Not Configured- As the name suggests, this vulnerability affects the deployment of Azure Multi-factor Authentication in your organization which can readily provide strong authentication throughout the range of easy verification options like phone call, text message, mobile app notification, verification code, and third-party OATH tokens. It is recommended for user sign-ins and plays a key-role in risk-based conditional access policies available through Azure Identity Protection.

  • Unmanaged Cloud Apps- It allows you to easily identify unmanaged cloud apps in your organization as nowadays IT departments cannot keep track of all the cloud applications that users are using to complete their work which may lead to unauthorized access to corporate data, possible data leakage, and other security risks. It is recommended to deploy Cloud App Discovery to discover unmanaged cloud applications and to manage them with the help of Azure AD.

  • Security Alerts from Privileged Identity Management- Here you can discover as well as resolve alerts about privileged identities in your organization as privilege users may lead to increase in the attack surface of the organization. It is recommended to use Azure AD Privileged Identity Management to manage, control, and monitor privileged identities as well as their access to resources in Azure AD and the other Microsoft online services like Office 365 or Microsoft Intune.  

Azure AD Risk Events

Azure AD can easily detect suspicious actions related to an organization's user accounts with the help of its adaptive machine learning algorithms and heuristics. It can detect following types of risk events:
  • Users with Leaked Credentials- The Microsoft's Leaked Credentials Service can acquire username/password pairs by monitoring public as well as dark web sites and working with- Researchers, Law enforcement, Security teams at Microsoft, and the other trusted sources; and check them against AAD users' current valid credential. If a match is found, that means a users' password has been compromised and a leaked credentials risk event is created.

  • Sign-ins from Anonymous IP Address- This risk event can identify the users who have signed in from an IP address known as an anonymous proxy IP address that are generally used by those who want to hide their device's IP address and may be used for malicious intent.

  • Impossible Travel to Atypical Locations- This one can identify two sign-ins originating from geographically distant locations in which at least one of these is an atypical for the user according to his/her past behavior. It can also compare the time difference between the two sign ins and the time would have take for the user to travel from first location to the second one indicating that a different user is using the same credentials. However, the system have an initial learning period of 14 days during which it can learn a new user's sign-in behavior. 

  • Sign-in from Unfamiliar Locations- As the name suggests it can keep track of the past sign-in locations to determine new or unfamiliar locations and triggers a risk event if the location is not on the list of familiar locations. This system have an initial learning period of 30 days during which it does not mark any new locations as unfamiliar location. 

  • Sign-ins from Infected Devices- This one can identify sign-ins from the devices infected with malware that can actively communicate with a bot server. You can easily identify this by correlating IP addresses of the user's device against the IP addresses that were in contact with a bot server. 

  • Sign-ins from the IP Addresses with Suspicious Activity- It can identify the IP addresses with a high number of failed sign-in attempts across multiple user accounts over a short period of time. This is a machine learning algorithm that can ignore the obvious "false positives" like the IP addresses used by the other users in the organization. It has an initial learning period of 14 days to learn the sign-in pattern of a new user as well as a new tenant.  









To read part 1, please click here
To read part 3, please click here






































Comments

Post a Comment

Popular posts from this blog

Query, Visualize, & Monitor Data in Azure Sentinel

By Ashwin Venugopal -
Image
  Azure Sentinel Workbooks Several ready for use templates are provided by the Azure Sentinel that can be used to create your own workbook and then modify them as needed for Contoso. Most of the data connectors it uses to ingest data come with their own workbooks, but better insight can be obtained by simply looking into the data that is being ingested by using tables and visualizations, including bar and pie charts. You can also make your own workbook easily by using this data from the beginning instead of using the predefined templates. Workbook Page You can easily access the workbook page from the Azure Sentinel from the navigation pane which consists of the: Workbook header- You can add a new workbook and review the saved workbooks as well as templates that are available on the workbook page. Templates section- You can access existing workbook templates on the Templates tab. You can save some of the workbooks for quick access and they will appear on the My Workbooks tab.  From the
Read more

Work with String Data Using KQL Statements

By Ashwin Venugopal -
Image
  Extract Data from Unstructured String Fields Unstructured string fields often contains the Security log data and requires parsing to extract data. There are multiple ways of pulling information from string fields in KQL and the two primary operators used are extract and parse.  Extract It gets a match for a regular expression from a text string. You can convert the extracted substring to the indicated type.  Arguments regex- A regular expression. captureGroup- A positive int constant indicating the capture group to extract. 0 stands for the entire match, 1 for the value matched by the first '('parenthesis')' in the regular expression, 2 or more for subsequent parenthesis.  text- A string to search. typeLiteral- An optional type literal. If provided, the extracted substring is converted to this type.  Returns If regex finds a match in text- the substring matched against the indicated capture group captureGroup, optionally converted to typeLiteral. If there's no mat
Read more

Threat Hunting in Microsoft Sentinel (part 1)

By Ashwin Venugopal -
Image
  To read part 2, please click  here To read part 3, please click  here Introducing the Microsoft Sentinel Hunting Page Threat hunting is a series activities performed while investigating and as there are no guidelines to perform it, the available tools on the Microsoft Sentinel can be introduced to perform better in your investigations. Firstly, you have to choose the Hunting link in the Microsoft Sentinel navigation menu to get to the Hunting page which is divided into the following sections: The header bar- It is located at the top of the page as usual and contains Refresh as well as timespan drop down menu. The New Query button can help you with creating a new query and the Run all queries button can run all the hunting queries in the background while also updating the hunting query list section with the number of results found.  The summary bar- Here, you can view the total number of queries available to run, the total number of bookmarks, the number of results from running live
Read more