↳ Source GitHubRègle analytiqueMedium

User Accounts - Sign in Failure due to CA Spikes

Description

' Identifies spike in failed sign-ins from user accounts due to conditional access policied. Spike is determined based on Time series anomaly which will look at historical baseline values. Ref : https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-user-accounts#monitoring-for-failed-unusual-sign-ins This query has also been updated to include UEBA logs IdentityInfo and BehaviorAnalytics for contextual information around the results.'
Type de règle
Scheduled
Version
2.0.5
Statut déclaré
Available
Fréquence
1d
Période analysée
14d
Déclenchement
gt 0

Couverture MITRE déclarée

Sources déclarées

Métadonnées du fichier source. Aucune dépendance déduite du KQL.

Connecteurs

Types de données

Requête KQL

Requête originale, sans modification.

let riskScoreCutoff = 20; //Adjust this based on volume of results
let starttime = 14d;
let timeframe = 1d;
let scorethreshold = 3;
let baselinethreshold = 50;
let aadFunc = (tableName:string){
  // Failed Signins attempts with reasoning related to conditional access policies.
  table(tableName)
  | where TimeGenerated between (startofday(ago(starttime))..startofday(now()))
  | where ResultDescription has_any ("conditional access", "CA") or ResultType in (50005, 50131, 53000, 53001, 53002, 52003, 70044)
  | extend UserPrincipalName = tolower(UserPrincipalName)
  | extend timestamp = TimeGenerated, AccountCustomEntity = UserPrincipalName
};
let aadSignin = aadFunc("SigninLogs");
let aadNonInt = aadFunc("AADNonInteractiveUserSignInLogs");
let allSignins = union isfuzzy=true aadSignin, aadNonInt;
let TimeSeriesAlerts = 
allSignins
| make-series DailyCount=count() on TimeGenerated from startofday(ago(starttime)) to startofday(now()) step 1d by UserPrincipalName
| extend (anomalies, score, baseline) = series_decompose_anomalies(DailyCount, scorethreshold, -1, 'linefit')
| mv-expand DailyCount to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double), score to typeof(double), baseline to typeof(long)
// Filtering low count events per baselinethreshold
| where anomalies > 0 and baseline > baselinethreshold
| extend AnomalyHour = TimeGenerated
| project UserPrincipalName, AnomalyHour, TimeGenerated, DailyCount, baseline, anomalies, score;
// Filter the alerts for specified timeframe
TimeSeriesAlerts
| where TimeGenerated > startofday(ago(timeframe))
| join kind=inner ( 
  allSignins
  | where TimeGenerated > startofday(ago(timeframe))
  // create a new column and round to hour
  | extend DateHour = bin(TimeGenerated, 1h)
  | summarize PartialFailedSignins = count(), LatestAnomalyTime = arg_max(TimeGenerated, *) by bin(TimeGenerated, 1h), OperationName, Category, ResultType, ResultDescription, UserPrincipalName, UserDisplayName, AppDisplayName, ClientAppUsed, IPAddress, ResourceDisplayName
) on UserPrincipalName, $left.AnomalyHour == $right.DateHour
| project LatestAnomalyTime, OperationName, Category, UserPrincipalName, UserDisplayName, ResultType, ResultDescription, AppDisplayName, ClientAppUsed, UserAgent, IPAddress, Location, AuthenticationRequirement, ConditionalAccessStatus, ResourceDisplayName, PartialFailedSignins, TotalFailedSignins = DailyCount, baseline, anomalies, score
| extend timestamp = LatestAnomalyTime, Name = tostring(split(UserPrincipalName,'@',0)[0]), UPNSuffix = tostring(split(UserPrincipalName,'@',1)[0])
| extend UserPrincipalName = tolower(UserPrincipalName)
| join kind=leftouter (
    IdentityInfo
    | summarize LatestReportTime = arg_max(TimeGenerated, *) by AccountUPN
    | project AccountUPN, Tags, JobTitle, GroupMembership, AssignedRoles, UserType, IsAccountEnabled
    | summarize
        Tags = make_set(Tags, 1000),
        GroupMembership = make_set(GroupMembership, 1000),
        AssignedRoles = make_set(AssignedRoles, 1000),
        UserType = make_set(UserType, 1000),
        UserAccountControl = make_set(UserType, 1000)
    by AccountUPN
    | extend UserPrincipalName=tolower(AccountUPN)
) on UserPrincipalName
| join kind=leftouter (
    BehaviorAnalytics
    | where ActivityType in ("FailedLogOn", "LogOn")
    | where isnotempty(SourceIPAddress)
    | project UsersInsights, DevicesInsights, ActivityInsights, InvestigationPriority, SourceIPAddress
    | project-rename IPAddress = SourceIPAddress
    | summarize
        UsersInsights = make_set(UsersInsights, 1000),
        DevicesInsights = make_set(DevicesInsights, 1000),
        IPInvestigationPriority = sum(InvestigationPriority)
    by IPAddress)
on IPAddress
| extend UEBARiskScore = IPInvestigationPriority
| where UEBARiskScore > riskScoreCutoff
| sort by UEBARiskScore desc 

Entités déclarées

AccountIP

Contenus associés

Liens établis à partir des identifiants déclarés et des manifests des solutions.

Traçabilité de la source

GitHub

Les valeurs affichées proviennent des fichiers du dépôt Azure/Azure-Sentinel. Elles décrivent le modèle publié, pas la configuration de votre workspace.

Identifiant source
3a9d5ede-2b9d-43a2-acc4-d272321ff77c
Autres fichiers source 3Solutions/Microsoft Entra ID/Analytic Rules/UserAccounts-CABlockedSigninSpikes.yamlsource ↗Detections/SigninLogs/UserAccounts-CABlockedSigninSpikes.yamlmigration-note ↗Solutions/Microsoft Entra ID/Data/Solution_AAD.jsonsolution-membership ↗
GSTEP / SUIVI DU CATALOGUE

Ajouté au catalogue : 16 sept. 2026 · 05:49 UTC
Dernier changement observé : 16 sept. 2026 · 05:49 UTC

Dates de synchronisation GSTEP, distinctes des dates de publication du contenu source.