Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous data access or collection activity across multiple M365 workloads (SharePoint, OneDrive, Exchange). The rule identifies users exhibiting high volumes of file or mail access across at least two distinct workloads within a one-hour window, which may indicate account compromise, bulk data exfiltration, or unauthorized persistence via mailbox rules.
Detects anomalous data access or collection activity across multiple M365 workloads (SharePoint, OneDrive, Exchange). The rule identifies users exhibiting high volumes of file or mail access across at least two distinct workloads within a one-hour window, which may indicate account compromise, bulk data exfiltration, or unauthorized persistence via mailbox rules.
Detects anomalous data access or collection activity across multiple M365 workloads (SharePoint, OneDrive, Exchange). The rule identifies users exhibiting high volumes of file or mail access across at least two distinct workloads within a one-hour window, which may indicate account compromise, bulk data exfiltration, or unauthorized persistence via mailbox rules.
Detects potentially unauthorized or suspicious file activities such as bulk downloads, external file sharing, or CASB policy violations monitored by Zscaler ZIA, which may indicate data exfiltration or policy non-compliance.
This hunting query detects potential data exfiltration by identifying users whose outbound data volume to cloud storage and messaging platforms: (OneDrive, Dropbox, Google Drive, SharePoint, Telegram) spikes dramatically compared to their own historical behavior.
It uses a per-user adaptive baseline approach rather than static thresholds, making it more resilient to normal variance in user upload habits while still flagging statistically abnormal spikes.
MITRE ATT&CK Mapping
Tactic: Exfiltration (TA0010)
Technique: T1567 – Exfiltration Over Web Service
Sub-technique T1567.002 – Exfiltration to Cloud Storage
Data Source
DeviceNetworkEvents (Microsoft Defender for Endpoint / Microsoft 365 Defender advanced hunting schema)
Logic Breakdown:
Baseline window (31 days, excluding the most recent day):
The query calculates each user's average daily upload volume (BytesSent) to a defined set of cloud storage/messaging destinations over the trailing 31 days, ending 1 day ago. This establishes a personal historical norm per user.
Global peer baseline fallback:
For users with little or no prior upload history (average below minBaselineFloor = 1 MB), the query substitutes a global average across all users (globalPeerBaseline) — or the 1 MB floor, whichever is higher — to avoid division-by-near-zero producing artificially inflated ratios for low-activity users.
Today's upload volume:
Sums BytesSent to the same destination patterns over the last 24 hours per user.
Ratio comparison:
Joins today's upload volume against each user's effective baseline and computes UploadRatio = TodayUpload / EffectiveBaseline.
Alert conditions (both must be true):
UploadRatio > 5 — today's upload is more than 5x the user's normal daily average.
TodayUpload > minUploadFloor (100 MB) — an absolute floor ensuring small, low-value increases (e.g., 1 KB → 6 KB) don't trigger noise alerts even if the ratio is high.
Output: Results sorted by UploadRatio descending, surfacing the most anomalous users firs
It uses a per-user adaptive baseline approach rather than static thresholds, making it more resilient to normal variance in user upload habits while still flagging statistically abnormal spikes.
MITRE ATT&CK Mapping
Tactic: Exfiltration (TA0010)
Technique: T1567 – Exfiltration Over Web Service
Sub-technique T1567.002 – Exfiltration to Cloud Storage
Data Source
DeviceNetworkEvents (Microsoft Defender for Endpoint / Microsoft 365 Defender advanced hunting schema)
Logic Breakdown:
Baseline window (31 days, excluding the most recent day):
The query calculates each user's average daily upload volume (BytesSent) to a defined set of cloud storage/messaging destinations over the trailing 31 days, ending 1 day ago. This establishes a personal historical norm per user.
Global peer baseline fallback:
For users with little or no prior upload history (average below minBaselineFloor = 1 MB), the query substitutes a global average across all users (globalPeerBaseline) — or the 1 MB floor, whichever is higher — to avoid division-by-near-zero producing artificially inflated ratios for low-activity users.
Today's upload volume:
Sums BytesSent to the same destination patterns over the last 24 hours per user.
Ratio comparison:
Joins today's upload volume against each user's effective baseline and computes UploadRatio = TodayUpload / EffectiveBaseline.
Alert conditions (both must be true):
UploadRatio > 5 — today's upload is more than 5x the user's normal daily average.
TodayUpload > minUploadFloor (100 MB) — an absolute floor ensuring small, low-value increases (e.g., 1 KB → 6 KB) don't trigger noise alerts even if the ratio is high.
Output: Results sorted by UploadRatio descending, surfacing the most anomalous users firs
Detects potentially unauthorized or suspicious file activities such as bulk downloads, external file sharing, or CASB policy violations monitored by Zscaler ZIA, which may indicate data exfiltration or policy non-compliance.
This rule detects the creation of Google Cloud Platform (GCP) persistent disk snapshots. Monitoring for disk snapshot creation is critical as adversaries may use this functionality to create backups of sensitive virtual machine disks for exfiltration, staging, or offline analysis, effectively bypassing traditional network-based egress controls.
This takes the earliest available snapshot per agent instead of comparing two states. If an agent is born already carrying MCP access and org-wide sharing there is no baseline to drift from, so this needs its own check rather than a diff.
Detects the creation of Google Cloud Dataflow jobs that exhibit indicators of malicious data exfiltration pipelines, such as usage of suspicious job names (common with exfiltration tools), service account delegation, or execution via scripted user agents.
Detects unusually large outbound network data transfers (over 1TB) from a single process account within an hour. This could indicate data exfiltration, unauthorized bulk data synchronization, or other malicious activity where large volumes of data are being moved out of the environment.
This rule detects when a single process account initiates a high volume (10 or more) of file move operations to folders associated with cloud storage services (e.g., 'cloud', 'dropbox', 'drive') within a one-hour window. This behavior could indicate data exfiltration or staging for exfiltration.
Detects anomalous activities targeting SaaS CRM applications (Salesforce, Microsoft Dynamics 365, HubSpot), including high-volume bulk exports, off-hours access, mass API queries for sensitive objects, and suspicious OAuth token grants.
This rule detects an unusually high volume of file sharing events from a single user in OneDrive within a one-hour window. Specifically, it looks for five or more 'SharingSet' or 'AnonymousLinkCreated' operations by the same UserId within an hour, which could indicate data exfiltration or unauthorized sharing.
Detects anomalous bulk configuration data collection from Cisco vManage via REST API endpoints typically used by UAT-8616. The rule identifies repeated HTTP GET requests to critical configuration, device, and system information endpoints on port 8443, which may indicate an attacker attempting to export SD-WAN configuration data following an exploitation event.
Detects cloud disk snapshot creation potentially for data exfiltration
This rule detects successful configuration modifications to diagnostic settings or storage services where the destination resource is identified as being outside of the organization's trusted subscriptions or expected storage naming conventions. This activity may indicate an adversary attempting to exfiltrate logs or data to an attacker-controlled storage account.
Detects the configuration and usage of personal Microsoft OneDrive accounts on corporate endpoints. This is achieved by monitoring for OneDrive process execution with personal account command-line arguments, OneDrive setup utilities configured with personal tenant identifiers, and file system activity within directory paths associated with personal OneDrive synchronization.
Detects anomalous activities targeting SaaS CRM applications (Salesforce, Microsoft Dynamics 365, HubSpot), including high-volume bulk exports, off-hours access, mass API queries for sensitive objects, and suspicious OAuth token grants.
Detects S3 bucket lifecycle policies configured for automatic object deletion
Detects potential financial data exfiltration by monitoring for bulk downloads of files with financial keywords from SaaS/Office365 platforms, and identifies unauthorized processes attempting to access sensitive financial application data files (.qbw, .qbb, etc.) on local systems.
Detects the configuration and usage of personal Microsoft OneDrive accounts on corporate endpoints. This is achieved by monitoring for OneDrive process execution with personal account command-line arguments, OneDrive setup utilities configured with personal tenant identifiers, and file system activity within directory paths associated with personal OneDrive synchronization.
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