Dataverse - Mass deletion of records

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

Identifies large scale record delete operations based on a predefined threshold and also detects scheduled bulk deletion jobs.

MITRE ATT&CK coverage

Tactic	Techniques
Impact	`T1485` Data Destruction

Rule body kusto

id: 716cf6d4-97ad-407b-923e-6790083acb58
kind: Scheduled
name: Dataverse - Mass deletion of records
description: Identifies large scale record delete operations based on a predefined
  threshold and also detects scheduled bulk deletion jobs.
severity: Medium
status: Available
requiredDataConnectors:
  - connectorId: Dataverse
    dataTypes:
      - DataverseActivity
queryFrequency: 1h
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - Impact
relevantTechniques:
  - T1485
query: |
  let mass_delete_threshold = 10000;
  let query_frequency = 1d;
  let delete_activities = DataverseActivity
      | where TimeGenerated >= ago(query_frequency)
      | where Message == "Delete";
  union
      (
      delete_activities
      | summarize FirstEvent = min(TimeGenerated), TotalEvents = count() by UserId, InstanceUrl
      | where TotalEvents > mass_delete_threshold
      | join kind=inner (
          delete_activities
          | summarize DeleteCount = count() by UserId, InstanceUrl, ClientIp, EntityName)
          on UserId, InstanceUrl
      | extend Entities = bag_pack("Entity", EntityName, "Count", DeleteCount)
      | summarize Details = make_set(Entities, 100), FirstEvent = min(FirstEvent) by UserId, InstanceUrl, ClientIp, TotalEvents
      ),
      (
      DataverseActivity
      | where TimeGenerated >= ago(query_frequency)
      | where Message == "BulkDelete"
      | summarize FirstEvent = min(TimeGenerated), TotalEvents = count() by UserId, InstanceUrl, ClientIp
      | extend Details = todynamic("Bulk delete scheduled")
      )
  | extend
      CloudAppId = int(32780),
      AccountName = tostring(split(UserId, '@')[0]),
      UPNSuffix = tostring(split(UserId, '@')[1])
  | project
      FirstEvent,
      UserId,
      ClientIp,
      TotalEvents,
      Details,
      InstanceUrl,
      CloudAppId,
      AccountName,
      UPNSuffix
eventGroupingSettings:
  aggregationKind: AlertPerResult
entityMappings:
  - entityType: Account
    fieldMappings:
      - identifier: Name
        columnName: AccountName
      - identifier: UPNSuffix
        columnName: UPNSuffix
  - entityType: CloudApplication
    fieldMappings:
      - identifier: AppId
        columnName: CloudAppId
      - identifier: InstanceName
        columnName: InstanceUrl
  - entityType: IP
    fieldMappings:
      - identifier: Address
        columnName: ClientIp
alertDetailsOverride:
  alertDisplayNameFormat: 'Dataverse - mass deletion or bulk deletion job detected
    in {{InstanceUrl}} '
  alertDescriptionFormat: '{{UserId}} triggered the mass deletion detection with the
    following information: {{Details}}'
version: 3.2.0

Stages and Predicates

Parameters

let mass_delete_threshold = 10000;
let query_frequency = 1d;

Let binding: `delete_activities`

let delete_activities = DataverseActivity
    | where TimeGenerated >= ago(query_frequency)
    | where Message == "Delete";

Derived from query_frequency.

union (2 sources)

Each leg below queries one source; the rule matches if any leg does. Sources: delete_activities, DataverseActivity

Leg 1: `delete_activities`

delete_activities
    | summarize FirstEvent = min(TimeGenerated), TotalEvents = count() by UserId, InstanceUrl
    | where TotalEvents > mass_delete_threshold
    | join kind=inner (
        delete_activities
        | summarize DeleteCount = count() by UserId, InstanceUrl, ClientIp, EntityName)
        on UserId, InstanceUrl
    | extend Entities = bag_pack("Entity", EntityName, "Count", DeleteCount)
    | summarize Details = make_set(Entities, 100), FirstEvent = min(FirstEvent) by UserId, InstanceUrl, ClientIp, TotalEvents

Leg 2: `DataverseActivity`

DataverseActivity
    | where TimeGenerated >= ago(query_frequency)
    | where Message == "BulkDelete"
    | summarize FirstEvent = min(TimeGenerated), TotalEvents = count() by UserId, InstanceUrl, ClientIp
    | extend Details = todynamic("Bulk delete scheduled")

Applied to the combined result

| extend
    CloudAppId = int(32780),
    AccountName = tostring(split(UserId, '@')[0]),
    UPNSuffix = tostring(split(UserId, '@')[1])
| project
    FirstEvent,
    UserId,
    ClientIp,
    TotalEvents,
    Details,
    InstanceUrl,
    CloudAppId,
    AccountName,
    UPNSuffix

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
`Message`	eq	`BulkDelete` transforms: `cased` `Delete` transforms: `cased` corpus 2 (kusto 2)
`TotalEvents`	gt	`10000` 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
`ClientIp`	`summarize`
`EntityName`	`summarize`
`InstanceUrl`	`summarize`
`UserId`	`summarize`

`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 - Mass deletion of records

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `delete_activities`

union (2 sources)

Leg 1: `delete_activities`

Leg 2: `DataverseActivity`

Applied to the combined result

Indicators

Output fields

Keyboard shortcuts

Search operators

Dataverse - Mass deletion of records

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: delete_activities

union (2 sources)

Leg 1: delete_activities

Leg 2: DataverseActivity

Applied to the combined result

Indicators

Output fields

Let binding: `delete_activities`

Leg 1: `delete_activities`

Leg 2: `DataverseActivity`