Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
295 detections
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Detects when a user authenticates via device code flow or performs device registration/ownership addition, followed by a bulk download of SharePoint or OneDrive files (25 or more distinct files) within a 60-minute window. This behavior can indicate session hijacking or unauthorized access where an attacker uses device code phishing to gain access and quickly exfiltrate cloud documents.
Detects potential data exfiltration and credential hunting where an endpoint downloads a database backup using presigned URLs or cloud storage signatures within 6 hours of searching local files for API keys or private keys.
Detects a suspicious behavioral sequence in AWS CloudTrail logs from external IPs using the Boto3 user agent. The sequence starts with caller-identity validation (GetCallerIdentity), followed by a high volume of secret reads (Secrets Manager) or cloud storage object reads (S3), indicating automated reconnaissance, credential theft, or data exfiltration.
Box Shield Anomalous Download
Cortex XDR
Surfaces Box Shield anomalous download alerts with full context extraction, including download delta, historical baseline vs anomaly period comparison, and source IPs.
Box Shield Suspicious Location Access
Cortex XDR
Surfaces Box Shield suspicious location alerts with geographic context and accessed content details. Detects access from unusual or excluded geographic locations indicating potential compromised accounts or credential theft.
Detects one account downloading many files through GoAnywhere Managed File Transfer.
MFT platforms sit between organisations and hold exactly the data worth stealing —
payment files, payroll, partner exchanges — and have been the target of mass
exploitation campaigns precisely because they are internet-facing and rarely watched.
Onboarding: GOANYWHERE-MFT is ingested via Syslog (Bloo extractor 850).
Integration guide: https://bloo.io/docs/hypercloud/syslog-connector
BQL (Bloo Query Language):
stream=documents where sourcename='GOANYWHERE-MFT' and action='DOWNLOADED'
| duration 1h
| groupby user, srcip
| select user, srcip, distinct_count(file)
| having distinct_count_col1 > 20
MFT platforms sit between organisations and hold exactly the data worth stealing —
payment files, payroll, partner exchanges — and have been the target of mass
exploitation campaigns precisely because they are internet-facing and rarely watched.
Onboarding: GOANYWHERE-MFT is ingested via Syslog (Bloo extractor 850).
Integration guide: https://bloo.io/docs/hypercloud/syslog-connector
BQL (Bloo Query Language):
stream=documents where sourcename='GOANYWHERE-MFT' and action='DOWNLOADED'
| duration 1h
| groupby user, srcip
| select user, srcip, distinct_count(file)
| having distinct_count_col1 > 20
The following analytic detects a high volume of Amazon CloudWatch Logs read operations performed by the same AWS principal and account within a
five-minute period.
It identifies more than 500 DescribeLogGroups, DescribeLogStreams, DownloadLogEvents, FilterLogEvents, GetLogEvents, or GetQueryResults API calls.
This activity may indicate automated discovery or collection of operational, application, or security telemetry from CloudWatch Logs.
five-minute period.
It identifies more than 500 DescribeLogGroups, DescribeLogStreams, DownloadLogEvents, FilterLogEvents, GetLogEvents, or GetQueryResults API calls.
This activity may indicate automated discovery or collection of operational, application, or security telemetry from CloudWatch Logs.
Detects a single Box account downloading an unusually high number of distinct files
in a short window. This is the clearest signal of data theft from cloud storage,
whether by a departing employee or an attacker using a compromised account.
Baseline per role before deployment: sales and legal teams legitimately pull large
document sets, while most users download a handful of files a day.
Onboarding: BOX is ingested via Box native connector (Bloo extractor 711).
Integration guide: https://bloo.io/docs/hypercloud/box-connector
BQL (Bloo Query Language):
stream=documents where sourcename='BOX' and action='DOWNLOADED'
| duration 1h
| groupby user
| select user, distinct_count(file)
| having distinct_count_col1 > 50
in a short window. This is the clearest signal of data theft from cloud storage,
whether by a departing employee or an attacker using a compromised account.
Baseline per role before deployment: sales and legal teams legitimately pull large
document sets, while most users download a handful of files a day.
Onboarding: BOX is ingested via Box native connector (Bloo extractor 711).
Integration guide: https://bloo.io/docs/hypercloud/box-connector
BQL (Bloo Query Language):
stream=documents where sourcename='BOX' and action='DOWNLOADED'
| duration 1h
| groupby user
| select user, distinct_count(file)
| having distinct_count_col1 > 50
Detects a Box account accessed from a source address with no prior history, followed
by substantial download activity. The combination is far stronger than either signal
alone: a new location by itself is routine for mobile users, and downloads by
themselves are routine for everyone.
Onboarding: BOX is ingested via Box native connector (Bloo extractor 711).
Integration guide: https://bloo.io/docs/hypercloud/box-connector
BQL (Bloo Query Language):
stream=documents where sourcename='BOX' and action='DOWNLOADED'
| duration 1h
| groupby user, srcip
| select user, srcip, count(*) as cnt
| having cnt > 25
by substantial download activity. The combination is far stronger than either signal
alone: a new location by itself is routine for mobile users, and downloads by
themselves are routine for everyone.
Onboarding: BOX is ingested via Box native connector (Bloo extractor 711).
Integration guide: https://bloo.io/docs/hypercloud/box-connector
BQL (Bloo Query Language):
stream=documents where sourcename='BOX' and action='DOWNLOADED'
| duration 1h
| groupby user, srcip
| select user, srcip, count(*) as cnt
| having cnt > 25
The following analytic detects the creation of open/public S3 buckets via the AWS CLI.
It leverages AWS CloudTrail logs to identify events where a user has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." This activity is significant because open S3 buckets can expose sensitive data to unauthorized users, leading to data breaches.
If confirmed malicious, an attacker could gain unauthorized access to potentially sensitive information stored in the S3 bucket, posing a significant security risk.
It leverages AWS CloudTrail logs to identify events where a user has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." This activity is significant because open S3 buckets can expose sensitive data to unauthorized users, leading to data breaches.
If confirmed malicious, an attacker could gain unauthorized access to potentially sensitive information stored in the S3 bucket, posing a significant security risk.
Detects high-volume retrieval of secrets by a single principal within a one-hour window across AWS Secrets Manager, Azure Key Vault, and Google Cloud Secret Manager. This behavior is indicative of bulk credential harvesting or automated sensitive data exfiltration following unauthorized access or privilege escalation in cloud environments.
Detects an anomalous volume of file downloads from SharePoint or OneDrive by a user on an unmanaged, non-compliant device, shortly following a suspicious or risky sign-in event. The rule establishes a baseline for individual user behavior over 30 days and alerts when the current hourly download activity significantly exceeds that baseline.
This rule detects potentially compromised accounts by correlating suspicious authentication activity (such as risky sign-ins, impossible travel, or malicious OAuth/Mail-rule configurations) with a subsequent spike in SharePoint or OneDrive file access, downloads, or external link sharing within a one-hour window. This behavior is indicative of an adversary performing cloud-based data exfiltration after gaining unauthorized access to an account.
Detects anomalous high-volume download or 'GetObject' operations from a single identity across multiple cloud storage objects and buckets within a short time window, which is indicative of bulk data exfiltration.
Detects unauthorized sharing, copying, or exporting of EBS, disk, or database snapshots to external cloud accounts, subscriptions, or projects that are not present in the defined trusted allow-list for AWS, Azure, or GCP environments.
Detects modifications to cloud storage access control configurations in Azure (containers/blobs) and GCP (storage buckets) that result in public or unauthenticated access. The rule uses lookups to filter out approved public assets before alerting.
Detects the modification of an Azure Storage account configuration to enable public access (allowBlobPublicAccess: true). This activity is often associated with attackers attempting to facilitate anonymous data exfiltration by enabling public access to blobs and subsequently reading them.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
This rule detects administrative changes to cloud storage configurations (AWS S3, Azure Blob, and GCP Storage) that result in public or anonymous read/write access to buckets or containers, which could lead to unauthorized data exposure.
Detects modifications to cloud storage configurations in Azure and GCP that grant public access to containers or blobs. Attackers may leverage this to expose sensitive data stored in cloud environments by changing permissions to be publicly readable.
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