Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the creation of new mail flow rules in Office 365 that are configured to redirect or copy emails to unauthorized or external addresses. It specifically looks for the "New-TransportRule" operation in Office 365 Management Activity logs, combined with parameters such as "BlindCopyTo", "CopyTo", or "RedirectMessageTo". Such activity can indicate potential data exfiltration, unauthorized access to sensitive information, or an attacker attempting to intercept email communications, which could lead to data breaches or information leakage.
The following analytic detects when the risk-based step-up consent security setting in Azure AD is disabled. It monitors Azure Active Directory logs for the "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling this feature can expose the organization to OAuth phishing threats by allowing users to grant consent to potentially malicious applications. If confirmed malicious, attackers could gain unauthorized access to user data and sensitive information, leading to data breaches and further compromise within the organization.
The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
This analytic identifies when specific Office 365 advanced security settings related to anti-phishing, safe links, safe attachments, or malware protection are altered within the Office 365 tenant. An attacker disabling these settings could reduce their risk of detection, potentially leading to unauthorized data access, data exfiltration, account compromise, or other malicious activities without leaving a detailed audit trail.
The following analytic detects instances where the inbox folder of an Office 365 mailbox is shared with all users within the tenant. It leverages Office 365 management activity events to identify when the 'Inbox' folder permissions are modified to include 'Everyone' with read rights. This activity is significant as it represents a potential security risk, allowing unauthorized access to sensitive emails. If confirmed malicious, this could lead to data breaches, exfiltration of confidential information, and further compromise through spear-phishing or other malicious activities based on the accessed email content.
The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute timeframe in Azure AD. It detects this activity by monitoring the 'Add service principal' operation and aggregating data in 10-minute intervals. This behavior is significant as it may indicate an adversary rapidly creating multiple service principals to stage an attack or expand their foothold within the network. If confirmed malicious, this activity could allow attackers to establish persistence, escalate privileges, or access sensitive information within the Azure environment.
The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute window in the Office 365 environment. It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service principal' operation in Azure Active Directory. This activity is significant as it may indicate a compromised user account or unauthorized actions, potentially leading to broader network infiltration or privilege escalation. If confirmed malicious, this behavior could allow attackers to gain persistent access, escalate privileges, or exfiltrate sensitive information.
The following analytic identifies instances where four or more distinct detection analytics are associated with malicious command line behavior on a specific host. This detection leverages the Command Line Interface (CLI) data from various sources to identify suspicious activities. This behavior is significant as it often indicates attempts to execute malicious commands, access sensitive data, install backdoors, or perform other nefarious actions. If confirmed malicious, attackers could gain unauthorized control, exfiltrate information, escalate privileges, or launch further attacks within the network, leading to severe compromise.
The following analytic identifies instances where a user has denied consent to an OAuth application seeking permissions within the Azure AD environment. This detection leverages Azure AD's audit logs, specifically focusing on user consent actions with error code 65004. Monitoring denied consent actions is significant as it can indicate users recognizing potentially suspicious or untrusted applications. If confirmed malicious, this activity could suggest attempts by unauthorized applications to gain access, potentially leading to data breaches or unauthorized actions within the environment. Understanding these denials helps refine security policies and enhance user awareness.
The following analytic detects an attempt to disable multi-factor authentication (MFA) for a Google Cloud Platform (GCP) user. It leverages Google Workspace Admin log events, specifically the UNENROLL_USER_FROM_STRONG_AUTH command. This activity is significant because disabling MFA can allow an adversary to maintain persistence within the environment using a compromised account without raising suspicion. If confirmed malicious, this action could enable attackers to bypass additional security layers, potentially leading to unauthorized access, data exfiltration, or further exploitation of the compromised account.
The following analytic detects when AWS S3 bucket versioning is suspended by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant because disabling versioning can prevent recovery of deleted or modified data, which is a common tactic in ransomware attacks. If confirmed malicious, this action could lead to data loss and hinder recovery efforts, severely impacting data integrity and availability.
The analytic detects the assignment of high-risk Graph API permissions in Azure AD, specifically Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. It scans Azure AD AuditLogs for 'Update application' operations, identifying when these permissions are assigned. This activity grants broad control over Azure AD, including application and directory settings. If malicious, it could lead to unauthorized modifications and potential security breaches, compromising the integrity and security of the Azure AD environment. Immediate investigation is required.
The analytic detects when a user removes an organization from GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for organization deletion events, which could indicate unauthorized removal of critical business resources. For a SOC, identifying organization removals is crucial as it may signal account compromise, insider threats, or malicious attempts to disrupt business operations by deleting entire organizational structures. The impact could be severe, potentially resulting in loss of source code, repositories, team structures, access controls, and other critical organizational assets. This disruption could halt development workflows, cause data loss, and require significant effort to restore from backups if available. Additionally, unauthorized organization removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets.
This analytic detects the use of the Get-ADComputer cmdlet with parameters indicating a search for Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific activity. This behavior is significant as it may indicate an attempt by adversaries or Red Teams to gain situational awareness and perform Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network.
The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This detection leverages CloudTrail logs to identify suspicious lifecycle configurations. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively.
The following analytic detects the invitation of an external guest user within Azure AD. It leverages Azure AD AuditLogs to identify events where an external user is invited, using fields such as operationName and initiatedBy. Monitoring these invitations is crucial as they can lead to unauthorized access if abused. If confirmed malicious, this activity could allow attackers to gain access to internal resources, potentially leading to data breaches or further exploitation of the environment.
The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services.
The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network.
The following analytic detects the addition of new service principal accounts in O365 tenants. It leverages data from the `o365_management_activity` dataset, specifically monitoring for operations related to adding or creating service principals. This activity is significant because attackers can exploit service principals to gain unauthorized access and perform malicious actions within an organization's environment. If confirmed malicious, this could allow attackers to interact with APIs, access resources, and execute operations on behalf of the organization, potentially leading to data breaches or further compromise.
This analytic detects when Microsoft Office 365 Data Loss Prevention (DLP) rules have been triggered. DLP rules are configured for security, regulatory, or business compliance reasons. Detections from this analytic should be thoroughly evaluated to determine the security relevance of the underlying DLP events. The accuracy of this analytic depends on the upstream DLP configuration.
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