diff --git a/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml
new file mode 100644
index 0000000000..e2314764bc
--- /dev/null
+++ b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml
@@ -0,0 +1,69 @@
+name: Detect Password Spray Attack Behavior From Source
+id: b6391b15-e913-4c2c-8949-9eecc06efacc
+version: 1
+date: '2023-10-30'
+author: Steven Dick
+status: production
+type: TTP
+description: The following analytic identifies one source failing to authenticate with 10 or more unique users. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed.
+data_source:
+- Authentication Events (various)
+search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user
+| `drop_dm_object_name("Authentication")`
+| eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null)
+| `detect_password_spray_attack_behavior_from_source_filter`
+| stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success by src
+| fields - _time
+| where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc
+| `security_content_ctime(firstTime)` 
+| `security_content_ctime(lastTime)`'
+how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
+known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners.
+references:
+- https://attack.mitre.org/techniques/T1110/003/
+- https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/
+- https://github.com/MarkoH17/Spray365
+tags:
+  analytic_story:
+  - Compromised User Account
+  asset_type: Account
+  confidence: 75
+  impact: 80
+  message: The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
+  mitre_attack_id:
+  - T1110.003
+  - T1110
+  observable:
+  - name: src
+    type: Hostname
+    role:
+    - Victim
+  - name: user
+    type: User
+    role:
+    - Victim
+  - name: failed_user
+    type: User
+    role:
+    - Attacker
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
+  required_fields:
+  - _time
+  - Authentication.user_category
+  - Authentication.src_category
+  - Authentication.app
+  - Authentication.action
+  - Authentication.src
+  - Authentication.user
+  risk_score: 60
+  security_domain: access
+tests:
+- name: True Positive Test
+  attack_data:
+  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
+    source: XmlWinEventLog:Security
+    sourcetype: XmlWinEventLog
+    update_timestamp: true
\ No newline at end of file
diff --git a/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml
new file mode 100644
index 0000000000..339965acb8
--- /dev/null
+++ b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml
@@ -0,0 +1,65 @@
+name: Detect Password Spray Attack Behavior On User
+id: a7539705-7183-4a12-9b6a-b6eef645a6d7
+version: 1
+date: '2023-10-30'
+author: Steven Dick
+status: production
+type: TTP
+description: The following analytic identifies any user failing to authenticate from 10 or more unique sources. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises. Environments can be very different depending on the organization. Test and customize this detections thresholds as needed
+data_source:
+- Authentication Events (various)
+search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user
+| `drop_dm_object_name("Authentication")`
+| eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null)
+| `detect_password_spray_attack_behavior_on_user_filter`
+| stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success by user
+| fields - _time
+| where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc
+| `security_content_ctime(firstTime)` 
+| `security_content_ctime(lastTime)`'
+how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
+known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners.
+references:
+- https://attack.mitre.org/techniques/T1110/003/
+- https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/
+- https://github.com/MarkoH17/Spray365
+tags:
+  analytic_story:
+  - Compromised User Account
+  asset_type: Account
+  confidence: 75
+  impact: 80
+  message: A total of $src_dc$ distinct sources attempted to access the account [$user$], $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
+  mitre_attack_id:
+  - T1110.003
+  - T1110
+  observable:
+  - name: src
+    type: Hostname
+    role:
+    - Victim
+  - name: user
+    type: User
+    role:
+    - Victim
+  product:
+  - Splunk Enterprise
+  - Splunk Enterprise Security
+  - Splunk Cloud
+  required_fields:
+  - _time
+  - Authentication.user_category
+  - Authentication.src_category
+  - Authentication.app
+  - Authentication.action
+  - Authentication.src
+  - Authentication.user
+  risk_score: 60
+  security_domain: access
+tests:
+- name: True Positive Test
+  attack_data:
+  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
+    source: XmlWinEventLog:Security
+    sourcetype: XmlWinEventLog
+    update_timestamp: true