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Multiple Microsoft 365 User Account Lockouts in Short Time Window

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Multiple Microsoft 365 User Account Lockouts in Short Time Window

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Detects a burst of Microsoft 365 user account lockouts within a short 5-minute window. A high number of IdsLocked login errors across multiple user accounts may indicate brute-force attempts for the same users resulting in lockouts.

Rule type: esql

Rule indices: None

Severity: medium

Risk score: 47

Runs every: 5m

Searches indices from: now-9m (Date Math format, see also Additional look-back time)

Maximum alerts per execution: 100

References:

Tags:

  • Domain: Cloud
  • Domain: SaaS
  • Data Source: Microsoft 365
  • Data Source: Microsoft 365 Audit Logs
  • Use Case: Threat Detection
  • Use Case: Identity and Access Audit
  • Tactic: Credential Access
  • Resources: Investigation Guide

Version: 3

Rule authors:

  • Elastic

Rule license: Elastic License v2

Investigation guide

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Triage and Analysis

Investigating Multiple Microsoft 365 User Account Lockouts in Short Time Window

Detects a burst of Microsoft 365 user account lockouts within a short 5-minute window. A high number of IdsLocked login errors across multiple user accounts may indicate brute-force attempts for the same users resulting in lockouts.

This rule uses ESQL aggregations and thus has dynamically generated fields. Correlation of the values in the alert document may need to be performed to the original sign-in and Graph events for further context.

Investigation Steps

  • Review the user_id_list: Are specific naming patterns targeted (e.g., admin, helpdesk)?
  • Examine ip_list and source_orgs: Look for suspicious ISPs or hosting providers.
  • Check duration_seconds: A very short window with a high lockout rate often indicates automation.
  • Confirm lockout policy thresholds with IAM or Entra ID admins. Did the policy trigger correctly?
  • Use the first_seen and last_seen values to pivot into related authentication or audit logs.
  • Correlate with any recent detection of password spraying or credential stuffing activity.
  • Review the request_type field to identify which authentication methods were used (e.g., OAuth, SAML, etc.).
  • Check for any successful logins from the same IP or ASN after the lockouts.

False Positive Analysis

  • Automated systems with stale credentials may cause repeated failed logins.
  • Legitimate bulk provisioning or scripted tests could unintentionally cause account lockouts.
  • Red team exercises or penetration tests may resemble the same lockout pattern.
  • Some organizations may have a high volume of lockouts due to user behavior or legacy systems.

Response Recommendations

  • Notify affected users and confirm whether activity was expected or suspicious.
  • Lock or reset credentials for impacted accounts.
  • Block the source IP(s) or ASN temporarily using conditional access or firewall rules.
  • Strengthen lockout and retry delay policies if necessary.
  • Review the originating application(s) involved via request_types.

Rule query

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from logs-o365.audit-*
| mv_expand event.category
| eval
    Esql.time_window_date_trunc = date_trunc(5 minutes, @timestamp)
| where
    event.dataset == "o365.audit" and
    event.category == "authentication" and
    event.provider in ("AzureActiveDirectory", "Exchange") and
    event.action in ("UserLoginFailed", "PasswordLogonInitialAuthUsingPassword") and
    to_lower(o365.audit.ExtendedProperties.RequestType) rlike "(oauth.*||.*login.*)" and
    o365.audit.LogonError == "IdsLocked" and
    to_lower(o365.audit.UserId) != "not available" and
    o365.audit.Target.Type in ("0", "2", "6", "10") and
    source.`as`.organization.name != "MICROSOFT-CORP-MSN-as-BLOCK"
| stats
    Esql_priv.o365_audit_UserId_count_distinct = count_distinct(to_lower(o365.audit.UserId)),
    Esql_priv.o365_audit_UserId_values = values(to_lower(o365.audit.UserId)),
    Esql.source_ip_values = values(source.ip),
    Esql.source_ip_count_distinct = count_distinct(source.ip),
    Esql.source_as_organization_name_values = values(source.`as`.organization.name),
    Esql.source_as_organization_name_count_distinct = count_distinct(source.`as`.organization.name),
    Esql.source_geo_country_name_values = values(source.geo.country_name),
    Esql.source_geo_country_name_count_distinct = count_distinct(source.geo.country_name),
    Esql.o365_audit_ExtendedProperties_RequestType_values = values(to_lower(o365.audit.ExtendedProperties.RequestType)),
    Esql.timestamp_first_seen = min(@timestamp),
    Esql.timestamp_last_seen = max(@timestamp),
    Esql.event_count = count(*)
  by Esql.time_window_date_trunc
| eval
    Esql.event_duration_seconds = date_diff("seconds", Esql.timestamp_first_seen, Esql.timestamp_last_seen)
| keep
    Esql.time_window_date_trunc,
    Esql_priv.o365_audit_UserId_count_distinct,
    Esql_priv.o365_audit_UserId_values,
    Esql.source_ip_values,
    Esql.source_ip_count_distinct,
    Esql.source_as_organization_name_values,
    Esql.source_as_organization_name_count_distinct,
    Esql.source_geo_country_name_values,
    Esql.source_geo_country_name_count_distinct,
    Esql.o365_audit_ExtendedProperties_RequestType_values,
    Esql.timestamp_first_seen,
    Esql.timestamp_last_seen,
    Esql.event_count,
    Esql.event_duration_seconds
| where
    Esql_priv.o365_audit_UserId_count_distinct >= 10 and
    Esql.event_count >= 10 and
    Esql.event_duration_seconds <= 300

Framework: MITRE ATT&CKTM

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