Dataverse - Anomalous application user activity

Status: available
Severity: medium
Time window: 14d
Group by: AnomalyTimeGenerated, InstanceUrl, OriginalObjectId, UserId
Source: github.com/Azure/Azure-Sentinel

Identifies anomalies in activity patterns of Dataverse application (non-interactive) users, based on activity falling outside the normal pattern of use.

MITRE ATT&CK coverage

Tactic	Techniques
Execution	`T1569` System Services
Credential Access	`T1528` Steal Application Access Token
Execution	`T0834` Native API, `T0871` Execution through API
Persistence	`T0859` Valid Accounts
Lateral Movement	`T0859` Valid Accounts

Rule body kusto

id: 0820da12-e895-417f-9175-7c256fcfb33e
kind: Scheduled
name: Dataverse - Anomalous application user activity
description: Identifies anomalies in activity patterns of Dataverse application (non-interactive)
  users, based on activity falling outside the normal pattern of use.
severity: Medium
status: Available
requiredDataConnectors:
  - connectorId: Dataverse
    dataTypes:
      - DataverseActivity
queryFrequency: 5h
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - CredentialAccess
  - Execution
  - Persistence
relevantTechniques:
  - T1528
  - T1569
  - T0871
  - T0834
  - T0859
query: |
  let query_lookback = 14d;
  let query_frequency = 5h;
  let anomaly_threshold = 2.5;
  let seasonality = -1;
  let trend = 'linefit';
  let step_duration = 5h;
  let app_user_regex = "^[0-9A-Fa-f]{8}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{12}\\.com$";
  let guid_regex = "([0-9A-Fa-f]{8}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{12})";
  let application_users = DataverseActivity
      | where TimeGenerated >= ago(query_frequency)
      | where UserId !endswith "@onmicrosoft.com" and UserId != "Unknown"
      | summarize by UserId
      | where split(UserId, "@")[1] matches regex app_user_regex;
  DataverseActivity
  | where TimeGenerated >= startofday(ago(query_lookback))
  | where UserId in (application_users)
  | where isnotempty(OriginalObjectId)
  | make-series TotalEvents = count() default=0 on TimeGenerated from startofday(ago(query_lookback)) to now() step step_duration by UserId, InstanceUrl, OriginalObjectId
  | extend (Anomalies, Score, Baseline) = series_decompose_anomalies(TotalEvents, anomaly_threshold, seasonality, trend)
  | mv-expand
      TotalEvents to typeof(double),
      AnomalyTimeGenerated = TimeGenerated to typeof(datetime),
      Anomalies to typeof(double),
      Score to typeof(double),
      Baseline to typeof(long)
  | where Anomalies > 0
  | extend Details = bag_pack(
                         "TotalEvents",
                         TotalEvents,
                         "Anomalies",
                         Anomalies,
                         "Baseline",
                         Baseline,
                         "Score",
                         Score,
                         "OriginalObjectId",
                         OriginalObjectId
                     )
  | summarize Details = make_set(Details, 100) by UserId, InstanceUrl, AnomalyTimeGenerated
  | extend
      CloudAppId = int(32780),
      AadUserId = extract(guid_regex, 1, tostring(split(UserId, "@")[0]))
  | project
      AnomalyTimeGenerated,
      UserId,
      AadUserId,
      InstanceUrl,
      Details,
      CloudAppId
eventGroupingSettings:
  aggregationKind: AlertPerResult
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: AadUserId
        columnName: AadUserId
  - entityType: CloudApplication
    fieldMappings:
      - identifier: AppId
        columnName: CloudAppId
      - identifier: InstanceName
        columnName: InstanceUrl
alertDetailsOverride:
  alertDisplayNameFormat: 'Dataverse - Non-interactive account anomaly detected in
    {{InstanceUrl}} '
  alertDescriptionFormat: 'Anomaly detected on {{UserId}} in {{InstanceUrl}}.  Details:
    {{Details}}'
customDetails:
  InstranceUrl: InstanceUrl
version: 3.2.0

Stages and Predicates

Parameters

let query_lookback = 14d;
let query_frequency = 5h;
let anomaly_threshold = 2.5;
let seasonality = -1;
let trend = 'linefit';
let step_duration = 5h;
let app_user_regex = "^[0-9A-Fa-f]{8}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{12}\\.com$";
let guid_regex = "([0-9A-Fa-f]{8}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{12})";

Let binding: `application_users`

let application_users = DataverseActivity
    | where TimeGenerated >= ago(query_frequency)
    | where UserId !endswith "@onmicrosoft.com" and UserId != "Unknown"
    | summarize by UserId
    | where split(UserId, "@")[1] matches regex app_user_regex;

Derived from query_frequency, app_user_regex.

Stage 1: `source`

DataverseActivity

Stage 2: `where`

| where TimeGenerated >= startofday(ago(query_lookback))

Stage 3: `where`

| where UserId in (application_users)

References application_users (defined above).

Stage 4: `where`

| where isnotempty(OriginalObjectId)

The stages below score time-series anomalies (make-series, series_decompose_anomalies).

Stage 5: `summarize`

| make-series TotalEvents = count() default=0 on TimeGenerated from startofday(ago(query_lookback)) to now() step step_duration by UserId, InstanceUrl, OriginalObjectId

Stage 6: `extend`

| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(TotalEvents, anomaly_threshold, seasonality, trend)

Stage 7: `mv-expand`

| mv-expand
    TotalEvents to typeof(double),
    AnomalyTimeGenerated = TimeGenerated to typeof(datetime),
    Anomalies to typeof(double),
    Score to typeof(double),
    Baseline to typeof(long)

Stage 8: `where`

| where Anomalies > 0

Stage 9: `extend`

| extend Details = bag_pack(
                       "TotalEvents",
                       TotalEvents,
                       "Anomalies",
                       Anomalies,
                       "Baseline",
                       Baseline,
                       "Score",
                       Score,
                       "OriginalObjectId",
                       OriginalObjectId
                   )

Stage 10: `summarize`

| summarize Details = make_set(Details, 100) by UserId, InstanceUrl, AnomalyTimeGenerated

Stage 11: `extend`

| extend
    CloudAppId = int(32780),
    AadUserId = extract(guid_regex, 1, tostring(split(UserId, "@")[0]))

Stage 12: `project`

| project
    AnomalyTimeGenerated,
    UserId,
    AadUserId,
    InstanceUrl,
    Details,
    CloudAppId

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

Field	Kind	Values
`Anomalies`	gt	`0` transforms: `cased` corpus 6 (kusto 6)
`OriginalObjectId`	is_not_null	(no value, null check)
`UserId`	in	`application_users` transforms: `cased`

Output fields

Fields the rule emits when it matches. Chronicle authors list these in the outcome block; they appear on the detection and $risk_score drives alerting. Sentinel / Defender XDR rules build them up through project / summarize / extend stages. Sentinel maps these into alert fields via entityMappings and customDetails; Defender XDR custom detections surface them as alert fields directly.

Field	Source
`AadUserId`	`project`
`AnomalyTimeGenerated`	`project`
`CloudAppId`	`project`
`Details`	`project`
`InstanceUrl`	`project`
`UserId`	`project`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)

Dataverse - Anomalous application user activity

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `application_users`

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `where`

Stage 5: `summarize`

Stage 6: `extend`

Stage 7: `mv-expand`

Stage 8: `where`

Stage 9: `extend`

Stage 10: `summarize`

Stage 11: `extend`

Stage 12: `project`

Indicators

Output fields

Keyboard shortcuts

Search operators

Dataverse - Anomalous application user activity

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: application_users

Stage 1: source

Stage 2: where

Stage 3: where

Stage 4: where

Stage 5: summarize

Stage 6: extend

Stage 7: mv-expand

Stage 8: where

Stage 9: extend

Stage 10: summarize

Stage 11: extend

Stage 12: project

Indicators

Output fields

Let binding: `application_users`

Stage 1: `source`

Stage 2: `where`

Stage 3: `where`

Stage 4: `where`

Stage 5: `summarize`

Stage 6: `extend`

Stage 7: `mv-expand`

Stage 8: `where`

Stage 9: `extend`

Stage 10: `summarize`

Stage 11: `extend`

Stage 12: `project`