Azure Active Directory Identity Protection risk detections reference

The vast majority of security breaches take place when attackers gain access to an environment by stealing a user’s identity. Discovering compromised identities is no easy task. Azure Active Directory uses adaptive machine learning algorithms and heuristics to detect suspicious actions that are related to your user accounts. Each detected suspicious action is stored in a record called risk detection.

Anonymous IP address

Detection Type: Real-time
Old name: Sign-ins from anonymous IP address

This risk detection type indicates sign-ins from an anonymous IP address (for example, Tor browser, anonymizer VPNs). These IP addresses are typically used by actors who want to hide their login telemetry (IP address, location, device, etc.) for potentially malicious intent.

Atypical travel

Detection Type: Offline
Old name: Impossible travel to atypical locations

This risk detection type identifies two sign-ins originating from geographically distant locations, where at least one of the locations may also be atypical for the user, given past behavior. Among several other factors, this machine learning algorithm takes into account the time between the two sign-ins and the time it would have taken for the user to travel from the first location to the second, indicating that a different user is using the same credentials.

The algorithm ignores obvious "false positives" contributing to the impossible travel conditions, such as VPNs and locations regularly used by other users in the organization. The system has an initial learning period of the earliest of 14 days or 10 logins, during which it learns a new user’s sign-in behavior.

Leaked Credentials

Detection Type: Offline
Old name: Users with leaked credentials

This risk detection type indicates that the user’s valid credentials have been leaked. When cybercriminals compromise valid passwords of legitimate users, the criminals often share those credentials. This is typically done by posting them publicly on the dark web or paste sites or by trading or selling the credentials on the black market. The Microsoft leaked credentials service acquires username / password pairs by monitoring public and dark web sites and by working with:

  • Researchers
  • Law enforcement
  • Security teams at Microsoft
  • Other trusted sources

When the service acquires user credentials from the dark web, paste sites or the above sources, they are checked against Azure AD users' current valid credentials to find valid matches.

Malware linked IP address

Detection Type: Offline
Old name: Sign-ins from infected devices

This risk detection type indicates sign-ins from IP addresses infected with malware that is known to actively communicate with a bot server. This is determined by correlating IP addresses of the user’s device against IP addresses that were in contact with a bot server while the bot server was active.

Unfamiliar sign-in properties

Detection Type: Real-time
Old name: Sign-ins from unfamiliar locations

This risk detection type considers past sign-in history (IP, Latitude / Longitude and ASN) to look for anomalous sign-ins. The system stores information about previous locations used by a user, and considers these “familiar” locations. The risk detection is triggered when the sign-in occurs from a location that's not already in the list of familiar locations. Newly created users will be in “learning mode” for a period of time in which unfamiliar sign-in properties risk detections will be turned off while our algorithms learn the user’s behavior. The learning mode duration is dynamic and depends on how much time it takes the algorithm to gather enough information about the user’s sign-in patterns. The minimum duration is five days. A user can go back into learning mode after a long period of inactivity. The system also ignores sign-ins from familiar devices, and locations that are geographically close to a familiar location.

We also run this detection for basic authentication (or legacy protocols). Because these protocols do not have modern properties such as client ID, there is limited telemetry to reduce false positives. We recommend our customers to move to modern authentication.

Azure AD threat intelligence

Detection Type: Offline
Old name: This detection will be shown in the legacy Azure AD Identity Protection reports (Users flagged for risk, Risk detections) as ‘Users with leaked credentials’

This risk detection type indicates user activity that is unusual for the given user or is consistent with known attack patterns based on Microsoft’s internal and external threat intelligence sources.

Admin confirmed user compromised

Detection Type: Offline
This detection indicates an admin has selected ‘Confirm user compromised’ in the Risky users UI or using riskyUsers API. To see which admin has confirmed this user compromised, check the user’s risk history (via UI or API).

Malicious IP address

Detection Type: Offline
This detection indicates sign-in from a malicious IP address. An IP address is considered malicious based on the following:

  • High failure rates (because of invalid credentials received from the IP address)
  • Other IP reputation sources

Additional risk detected

Detection Type: Real-time or Offline
This detection indicates that one of the above premium detections was detected. Since the premium detections are visible only to Azure AD Premium P2 customers, they are titled "additional risk detected" for non-P2 customers.