Dataverse - Mass export of records to Excel

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

Identifies users exporting a large amount of records from Dynamics 365 to Excel, significantly more records exported than any other recent activity by that user. Large exports from users with no recent activity are identified using a predefined threshold.

MITRE ATT&CK coverage

Tactic	Techniques
Exfiltration	`T1567` Exfiltration Over Web Service

Rule body kusto

id: 57000f0d-ff5d-4166-94b6-aa5fb62b16ec
kind: Scheduled
name: Dataverse - Mass export of records to Excel
description: Identifies users exporting a large amount of records from Dynamics 365
  to Excel, significantly more records exported than any other recent activity by
  that user. Large exports from users with no recent activity are identified using
  a predefined threshold.
severity: Low
status: Available
requiredDataConnectors:
  - connectorId: Dataverse
    dataTypes:
      - DataverseActivity
queryFrequency: 1h
queryPeriod: 14d
triggerOperator: gt
triggerThreshold: 0
tactics:
  - Exfiltration
relevantTechniques:
  - T1567
query: |
  // Set a mass export threshold for users who have no historical activity.
  let mass_export_threshold = 10000;
  let query_lookback = 14d;
  let query_frequency = 1h;
  let export_activity = DataverseActivity
      | where Message == "ExportToExcel"
      | extend QueryCount = iif(QueryResults has ",", todouble(countof(tostring(QueryResults), ',') + 1), double(1));
  let current_activity = export_activity
      | where TimeGenerated > ago(query_frequency)
      | extend RecordId = split(QueryResults, ",")
      | summarize
          FirstEvent = min(TimeGenerated),
          CurrentExportRate = sum(QueryCount),
          SampleRecordIds = make_set(RecordId, 1000)
          by UserId, InstanceUrl;
  let historical_activity = export_activity
      | where TimeGenerated between(ago(query_lookback) .. ago(query_frequency))
      | summarize HistoricalBaseline = sum(QueryCount) by HistoricalUserId = UserId, InstanceUrl;
  current_activity
  | join kind=leftouter(historical_activity) on $left.UserId == $right.HistoricalUserId, InstanceUrl
  | extend BaselineThreshold = iif(isnotnull(HistoricalBaseline), HistoricalBaseline, todouble(mass_export_threshold))
  | where CurrentExportRate > BaselineThreshold
  | join kind=inner(export_activity
      | where TimeGenerated > ago(query_frequency)
      | summarize EntityCount = sum(QueryCount) by UserId, ClientIp, InstanceUrl, EntityName
      | extend Details = bag_pack("EntityName", EntityName, "EntityCount", EntityCount)
      | summarize Details = make_set(Details, 100) by UserId, ClientIp, InstanceUrl)
      on UserId, InstanceUrl
  | extend
      CloudAppId = int(32780),
      AccountName = tostring(split(UserId, '@')[0]),
      UPNSuffix = tostring(split(UserId, '@')[1])
  | project
      FirstEvent,
      UserId,
      ClientIp,
      BaselineThreshold,
      CurrentExportRate,
      Details,
      SampleRecordIds,
      InstanceUrl,
      AccountName,
      UPNSuffix,
      CloudAppId
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 export to Excel activity in {{{InstanceUrl}} '
  alertDescriptionFormat: User {{UserId}} exported {{{CurrentExportRate}} records
    using the ExportToExcel function in Dataverse.
version: 3.2.0

Stages and Predicates

Parameters

let mass_export_threshold = 10000;
let query_lookback = 14d;
let query_frequency = 1h;

Let binding: `historical_activity`

let historical_activity = export_activity
    | where TimeGenerated between(ago(query_lookback) .. ago(query_frequency))
    | summarize HistoricalBaseline = sum(QueryCount) by HistoricalUserId = UserId, InstanceUrl;

Derived from query_lookback, query_frequency, export_activity.

The stages below define let current_activity (the rule's main pipeline source).

Stage 1: `source`

DataverseActivity

Stage 2: `where`

| where Message == "ExportToExcel"

Stage 3: `extend`

| extend QueryCount = iif(QueryResults has ",", todouble(countof(tostring(QueryResults), ',') + 1), double(1))

Stage 4: `where`

| where TimeGenerated > ago(query_frequency)

Stage 5: `extend`

| extend RecordId = split(QueryResults, ",")

Stage 6: `summarize`

| summarize
        FirstEvent = min(TimeGenerated),
        CurrentExportRate = sum(QueryCount),
        SampleRecordIds = make_set(RecordId, 1000)
        by UserId, InstanceUrl

Threshold: gt BaselineThreshold

The stages below run on current_activity (the outer pipeline).

Stage 7: `join`

current_activity
| join kind=leftouter(historical_activity) on $left.UserId == $right.HistoricalUserId, InstanceUrl

Stage 8: `extend`

| extend BaselineThreshold = iif(isnotnull(HistoricalBaseline), HistoricalBaseline, todouble(mass_export_threshold))

Stage 9: `where`

| where CurrentExportRate > BaselineThreshold

Stage 10: `join`

| join kind=inner(export_activity
    | where TimeGenerated > ago(query_frequency)
    | summarize EntityCount = sum(QueryCount) by UserId, ClientIp, InstanceUrl, EntityName
    | extend Details = bag_pack("EntityName", EntityName, "EntityCount", EntityCount)
    | summarize Details = make_set(Details, 100) by UserId, ClientIp, InstanceUrl)
    on UserId, InstanceUrl

Stage 11: `extend`

| extend
    CloudAppId = int(32780),
    AccountName = tostring(split(UserId, '@')[0]),
    UPNSuffix = tostring(split(UserId, '@')[1])

Stage 12: `project`

| project
    FirstEvent,
    UserId,
    ClientIp,
    BaselineThreshold,
    CurrentExportRate,
    Details,
    SampleRecordIds,
    InstanceUrl,
    AccountName,
    UPNSuffix,
    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
`CurrentExportRate`	gt	`BaselineThreshold` transforms: `cased`
`Message`	eq	`ExportToExcel` 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
`AccountName`	`project`
`BaselineThreshold`	`project`
`ClientIp`	`project`
`CloudAppId`	`project`
`CurrentExportRate`	`project`
`Details`	`project`
`FirstEvent`	`project`
`InstanceUrl`	`project`
`SampleRecordIds`	`project`
`UPNSuffix`	`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 - Mass export of records to Excel

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: `historical_activity`

Stage 1: `source`

Stage 2: `where`

Stage 3: `extend`

Stage 4: `where`

Stage 5: `extend`

Stage 6: `summarize`

Stage 7: `join`

Stage 8: `extend`

Stage 9: `where`

Stage 10: `join`

Stage 11: `extend`

Stage 12: `project`

Indicators

Output fields

Keyboard shortcuts

Search operators

Dataverse - Mass export of records to Excel

MITRE ATT&CK coverage

Rule body kusto

Stages and Predicates

Parameters

Let binding: historical_activity

Stage 1: source

Stage 2: where

Stage 3: extend

Stage 4: where

Stage 5: extend

Stage 6: summarize

Stage 7: join

Stage 8: extend

Stage 9: where

Stage 10: join

Stage 11: extend

Stage 12: project

Indicators

Output fields

Let binding: `historical_activity`

Stage 1: `source`

Stage 2: `where`

Stage 3: `extend`

Stage 4: `where`

Stage 5: `extend`

Stage 6: `summarize`

Stage 7: `join`

Stage 8: `extend`

Stage 9: `where`

Stage 10: `join`

Stage 11: `extend`

Stage 12: `project`