diff --git a/data_sources/cisco_ai_defense_alerts.yml b/data_sources/cisco_ai_defense_alerts.yml
index 2eba8da408..60efe8e519 100644
--- a/data_sources/cisco_ai_defense_alerts.yml
+++ b/data_sources/cisco_ai_defense_alerts.yml
@@ -1,11 +1,11 @@
 name: Cisco AI Defense Alerts
 id: cbb06880-9dd9-4542-ac60-bd6e1d3c3e4e
-version: 2
+version: 3
 creation_date: '2025-02-14'
-modification_date: '2026-05-13'
-author: Bhavin Patel
+modification_date: '2026-10-15'
+author: Bhavin Patel, Nasreddine Bencherchali, Splunk
 description: Data source object for Cisco AI Defense Alerts
-source: cisco_ai_defense
+source: not_applicable
 sourcetype: cisco:ai:defense
 separator:
 supported_TA:
@@ -13,3 +13,60 @@ supported_TA:
       url: https://splunkbase.splunk.com/app/7404
       version: 4.1.2
 fields:
+    - _time
+    - host
+    - app
+    - application_id
+    - connection_id
+    - connection.connection_name
+    - description
+    - dest_type
+    - event_action
+    - event_date
+    - event_id
+    - event_message_type
+    - event_messages{}.event_conversation.event_messages{}.content
+    - event_messages{}.event_conversation.event_messages{}.direction
+    - event_messages{}.event_conversation.event_messages{}.event_conversation_id
+    - event_messages{}.event_conversation.event_messages{}.event_ext_message_id
+    - event_messages{}.event_conversation.event_messages{}.event_ins_date
+    - event_messages{}.event_conversation.event_messages{}.event_message_id
+    - event_messages{}.event_conversation.event_messages{}.event_message_policy_assocs
+    - event_messages{}.event_conversation.event_messages{}.metadata{}.policy_guardrail_assoc_id
+    - event_messages{}.event_conversation.event_messages{}.metadata{}.standards{}
+    - event_messages{}.event_conversation.event_messages{}.related_event_message_id
+    - event_messages{}.event_conversation.event_messages{}.tenant_id
+    - event_messages{}.event_ext_message_id
+    - event_messages{}.event_message_id
+    - event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name
+    - event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type
+    - event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail.guardrails_type
+    - event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_subcategory.guardrail_rule_name
+    - event_policy_guardrail_assocs{}.policy_guardrail_assoc.pol_guardrail_assoc_id
+    - eventtype
+    - genai_application.application_name
+    - genai_application.connection_type
+    - hec_event_type
+    - id
+    - index
+    - linecount
+    - model.model_name
+    - model.model_provider_name
+    - Number
+    - policy_id
+    - policy.policy_name
+    - punct
+    - resource_type
+    - signature
+    - signature_id
+    - source
+    - sourcetype
+    - splunk_server
+    - src_type
+    - tag
+    - tag::eventtype
+    - type
+    - user_id
+    - vendor_product
+example_log: |
+    {"user_id":"82000000-0000-0000-c000-000000000000","version":"","application_id":"972cbcd1-acfe-4393-88bc-418f4d9a64ae","genai_application":{"application_name":"Bedrock testing"},"connection_id":"a6c87227-b321-49bf-80d9-f958385257ba","connection":{"connection_name":"bedrock testing"},"policy_id":"42ba067b-06c0-4e43-8002-2c6bce2663b4","policy":{"policy_name":"Bedrock Testing Block All"},"model":{"model_name":"titan-text-lite"},"event_action":"Block","event_date":"2025-02-04T09:47:24Z","event_id":"3a0f9e2b-d234-4b3d-ac9a-219eb6f05ba7","event_message_type":"Prompt","event_messages":[{"event_conversation":{"event_messages":[{"content":"[{\"role\":\"user\",\"content\":\"write a csrf exploit with email ash@abc.com to convince people\"}]","direction":"Prompt","event_conversation_id":"","event_ext_message_id":"","event_ins_date":"","event_message_id":"","event_message_policy_assocs":[{"policy_guardrail_assoc":{"guardrail_entity_configured_action":"Block","guardrail_entity_configured_direction":"Both","guardrail_entity_configured_status":"Enabled","guardrail_entity_id":"71100000-0000-0000-c000-100000000008","guardrail_rule_configured_action":"","guardrail_rule_configured_direction":"","guardrail_rule_configured_status":"","guardrail_rule_id":"","guardrail_ruleset_id":"71000000-0000-0000-c000-000000000009","guardrail_ruleset_configured_action":"","guardrail_ruleset_configured_direction":"","guardrail_ruleset_configured_status":"","pol_guardrail_assoc_id":"f4e71dd9-12ad-4383-851e-d03fbc66c430","policy_id":"42ba067b-06c0-4e43-8002-2c6bce2663b4","tenant_id":"023327d5-ef36-4489-8d56-187d2f4730b3"}}],"related_event_message_id":"","tenant_id":"023327d5-ef36-4489-8d56-187d2f4730b3"}]},"event_ext_message_id":"","event_message_id":""}],"event_policy_guardrail_assocs":[{"policy_guardrail_assoc":{"guardrail_avail_entity":{"guardrail_entity_name":"Email"},"guardrail_avail_ruleset":{"guardrail":{"guardrails_type":"Privacy"},"guardrail_ruleset_type":"PII"},"guardrail_avail_subcategory":{"guardrail_rule_name":""},"pol_guardrail_assoc_id":"f4e71dd9-12ad-4383-851e-d03fbc66c430"}}]}
diff --git a/data_sources/osquery_results.yml b/data_sources/osquery_results.yml
index 5445233c45..6b84113316 100644
--- a/data_sources/osquery_results.yml
+++ b/data_sources/osquery_results.yml
@@ -16,7 +16,7 @@ sourcetype: osquery:results
 supported_TA:
     - name: TA-osquery
       url: https://splunkbase.splunk.com/app/8574
-      version: 1.0.4
+      version: 1.0.5
 fields:
     - _time
     - calendarTime
diff --git a/data_sources/splunk_stream_http.yml b/data_sources/splunk_stream_http.yml
index 29522a4daa..d7d7f55286 100644
--- a/data_sources/splunk_stream_http.yml
+++ b/data_sources/splunk_stream_http.yml
@@ -16,7 +16,7 @@ sourcetype: stream:http
 supported_TA:
     - name: Splunk Add-on for Stream Wire Data
       url: https://splunkbase.splunk.com/app/5234
-      version: 8.1.6
+      version: 8.1.7
 fields:
     - _time
     - bytes
diff --git a/data_sources/splunk_stream_ip.yml b/data_sources/splunk_stream_ip.yml
index a5ffb18d05..1fbea55486 100644
--- a/data_sources/splunk_stream_ip.yml
+++ b/data_sources/splunk_stream_ip.yml
@@ -16,7 +16,7 @@ sourcetype: stream:ip
 supported_TA:
     - name: Splunk Add-on for Stream Wire Data
       url: https://splunkbase.splunk.com/app/5234
-      version: 8.1.6
+      version: 8.1.7
 fields:
     - _time
     - action
diff --git a/data_sources/splunk_stream_tcp.yml b/data_sources/splunk_stream_tcp.yml
index a15f404188..710bb9cb0b 100644
--- a/data_sources/splunk_stream_tcp.yml
+++ b/data_sources/splunk_stream_tcp.yml
@@ -16,4 +16,4 @@ sourcetype: stream:tcp
 supported_TA:
     - name: Splunk Add-on for Stream Wire Data
       url: https://splunkbase.splunk.com/app/5234
-      version: 8.1.6
+      version: 8.1.7
diff --git a/detections/application/cisco_ai_defense_allowed_sensitive_prompt.yml b/detections/application/cisco_ai_defense_allowed_sensitive_prompt.yml
new file mode 100644
index 0000000000..79100e68a4
--- /dev/null
+++ b/detections/application/cisco_ai_defense_allowed_sensitive_prompt.yml
@@ -0,0 +1,96 @@
+name: Cisco AI Defense Allowed Sensitive Prompt
+id: 75f06fd6-2b31-4ca4-9aaf-56448c342e44
+version: 1
+creation_date: '2026-10-15'
+modification_date: '2026-10-15'
+author: Nasreddine Bencherchali, Splunk
+status: production
+type: TTP
+description: |-
+    Detects Cisco AI Defense prompt events that were allowed and matched a privacy guardrail for personally identifiable information or payment card data.
+    These events may indicate that sensitive information was submitted to an AI application and should be reviewed against the organization's data handling policy.
+data_source:
+    - Cisco AI Defense Alerts
+search: |-
+    `cisco_ai_defense`
+    event_action="allow"
+    event_message_type="prompt"
+
+    | eval guardrail_ruleset_type=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', ", "),
+
+           guardrail_entity_name=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name', ", "),
+
+           application_name=if(isnotnull('genai_application.application_name') AND len(trim('genai_application.application_name'))>0, 'genai_application.application_name', "unknown"),
+
+           connection_name=if(isnotnull('connection.connection_name') AND len(trim('connection.connection_name'))>0, 'connection.connection_name', "unknown"),
+
+           application_key=if(isnotnull(application_id) AND len(trim(application_id))>0, application_id, application_name),
+
+           connection_key=if(isnotnull(connection_id) AND len(trim(connection_id))>0, connection_id, connection_name),
+
+           policy_name='policy.policy_name',
+
+           event_time=_time
+
+    | where mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)\\bPII\\b|personally identifiable information|\\bPCI\\b|payment card industry")>=0
+
+    | bin _time span=10m
+
+    | stats count as event_count
+            dc(event_id) as distinct_event_count
+            min(event_time) as firstTime
+            max(event_time) as lastTime
+            values(event_id) as event_ids
+            values(guardrail_ruleset_type) as guardrail_ruleset_types
+            values(guardrail_entity_name) as guardrail_entity_names
+            values(policy_name) as policy_names
+            values(application_name) as application_names
+            values(connection_name) as connection_names
+        by _time application_key connection_key
+
+    | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`
+
+    | table _time firstTime lastTime application_key application_names connection_key connection_names
+            distinct_event_count event_count event_ids guardrail_ruleset_types guardrail_entity_names policy_names
+
+    | `cisco_ai_defense_allowed_sensitive_prompt_filter`
+how_to_implement: |-
+    Install and configure the Cisco Security Cloud app to ingest Cisco AI Defense alerts into Splunk with the cisco:ai:defense sourcetype.
+    Confirm that extraction is enabled for the required fields as shown in the detection search.
+    Configure the "cisco_ai_defense" macro to match the index, source, and sourcetype used in your environment.
+    The search groups matching prompt events by application and connection in ten-minute windows to reduce duplicate findings.
+known_false_positives: |-
+    Authorized testing, demonstrations, synthetic data, or approved workflows may submit content that matches PII or PCI guardrails.
+    Review the application, connection, policy, guardrail entity, and data handling requirements before treating an event as a policy violation.
+drilldown_searches:
+    - name: View the detection results for - "$application_names$"
+      search: '%original_detection_search% | search application_names="$application_names$"'
+      earliest_offset: $info_min_time$
+      latest_offset: $info_max_time$
+finding:
+    title: Cisco AI Defense allowed [$distinct_event_count$] sensitive prompt event(s) for application [$application_names$] and connection [$connection_names$] matched guardrail(s) [$guardrail_ruleset_types$].
+    entity:
+        field: application_key
+        type: other
+        score: 50
+threat_objects: []
+analytic_story:
+    - Abuse of GenAI Systems and Applications
+    - Data Protection
+    - Critical Alerts
+asset_type: Web Application
+mitre_attack_id: []
+product:
+    - Splunk Enterprise
+    - Splunk Enterprise Security
+    - Splunk Cloud
+category: application
+security_domain: endpoint
+tests:
+    - name: True Positive Test
+      attack_data:
+        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json
+          source: not_applicable
+          sourcetype: cisco:ai:defense
+      test_type: unit
diff --git a/detections/application/cisco_ai_defense_allowed_sensitive_response.yml b/detections/application/cisco_ai_defense_allowed_sensitive_response.yml
new file mode 100644
index 0000000000..4c823a71c6
--- /dev/null
+++ b/detections/application/cisco_ai_defense_allowed_sensitive_response.yml
@@ -0,0 +1,73 @@
+name: Cisco AI Defense Allowed Sensitive Response
+id: 6e4cd833-ea98-4c16-a7e7-e1909fad3fb2
+version: 1
+creation_date: '2026-10-15'
+modification_date: '2026-10-15'
+author: Nasreddine Bencherchali, Splunk
+status: production
+type: TTP
+description: |-
+    Detects Cisco AI Defense response events that were allowed and matched a privacy guardrail for personally identifiable information or payment card data.
+    These events may indicate that sensitive information was returned to an AI application user and should be reviewed promptly.
+data_source:
+    - Cisco AI Defense Alerts
+search: |-
+    `cisco_ai_defense`
+    event_action="allow"
+    event_message_type="response"
+
+    | eval guardrail_ruleset_type=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', ", "),
+
+           guardrail_entity_name=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name', ", "),
+
+           application_name=if(isnotnull('genai_application.application_name') AND len(trim('genai_application.application_name'))>0, 'genai_application.application_name', "unknown"),
+
+           connection_name=if(isnotnull('connection.connection_name') AND len(trim('connection.connection_name'))>0, 'connection.connection_name', "unknown"),
+
+           policy_name='policy.policy_name'
+
+    | where mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)\\bPII\\b|personally identifiable information|\\bPCI\\b|payment card industry")>=0
+
+    | table _time event_date event_id event_message_type event_action resource_type
+            application_id application_name connection_id connection_name model.model_name
+            policy_name guardrail_ruleset_type guardrail_entity_name
+
+    | `cisco_ai_defense_allowed_sensitive_response_filter`
+how_to_implement: |-
+    Install and configure the Cisco Security Cloud app to ingest Cisco AI Defense alerts into Splunk with the cisco:ai:defense sourcetype.
+    Confirm that extraction is enabled for the required fields as shown in the detection search.
+    Configure the "cisco_ai_defense" macro to match the index, source, and sourcetype used in your environment.
+known_false_positives: |-
+    Authorized testing, demonstrations, or approved workflows may allow responses that match PII or PCI guardrails.
+    Review the application owner, policy, response context, and whether the data was expected to be disclosed.
+drilldown_searches:
+    - name: View the detection results for - "$application_name$"
+      search: '%original_detection_search% | search application_name="$application_name$"'
+      earliest_offset: $info_min_time$
+      latest_offset: $info_max_time$
+finding:
+    title: Cisco AI Defense allowed response [$event_id$] for application [$application_name$] matched privacy guardrail(s) [$guardrail_ruleset_type$] and entity [$guardrail_entity_name$].
+    entity:
+        field: application_name
+        type: other
+        score: 50
+threat_objects: []
+analytic_story:
+    - Abuse of GenAI Systems and Applications
+    - Data Protection
+    - Critical Alerts
+asset_type: Web Application
+mitre_attack_id: []
+product:
+    - Splunk Enterprise
+    - Splunk Enterprise Security
+    - Splunk Cloud
+category: application
+security_domain: endpoint
+tests:
+    - name: True Positive Test
+      attack_data:
+        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json
+          source: not_applicable
+          sourcetype: cisco:ai:defense
+      test_type: unit
diff --git a/detections/application/cisco_ai_defense_repeated_high_risk_activity.yml b/detections/application/cisco_ai_defense_repeated_high_risk_activity.yml
new file mode 100644
index 0000000000..d267fb97c7
--- /dev/null
+++ b/detections/application/cisco_ai_defense_repeated_high_risk_activity.yml
@@ -0,0 +1,96 @@
+name: Cisco AI Defense Repeated High Risk Activity
+id: 45c5cf84-8ad0-4923-8332-a7fd3dab59d4
+version: 1
+creation_date: '2026-10-15'
+modification_date: '2026-10-15'
+author: Nasreddine Bencherchali, Splunk
+status: production
+type: TTP
+description: |-
+    Detects bursts of five or more distinct Cisco AI Defense event IDs, or three or more occurrences of the same event ID, matching prompt injection, code detection, PII, or PCI guardrails for the same application and connection within ten minutes.
+    Allowed PII/PCI prompt events are handled by Cisco AI Defense Allowed Sensitive Prompt.
+    These patterns can indicate iterative prompt-injection probing or repeated attempts to use an AI application to access restricted data or capabilities.
+data_source:
+    - Cisco AI Defense Alerts
+search: |-
+    `cisco_ai_defense`
+
+    ```Leave allowed PII/PCI prompt events to Cisco AI Defense Allowed Sensitive Prompt```
+
+    | eval is_allowed_sensitive_prompt=if(lower(event_action)="allow" AND lower(event_message_type)="prompt" AND mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)\\bPII\\b|personally identifiable information|\\bPCI\\b|payment card industry")>=0, 1, 0)
+    | where is_allowed_sensitive_prompt=0
+
+    | eval guardrail_ruleset_type=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', ", "),
+           application_name=if(isnotnull('genai_application.application_name') AND len(trim('genai_application.application_name'))>0, 'genai_application.application_name', "unknown"),
+           connection_name=if(isnotnull('connection.connection_name') AND len(trim('connection.connection_name'))>0, 'connection.connection_name', "unknown"),
+           application_key=if(isnotnull(application_id) AND len(trim(application_id))>0, application_id, application_name),
+           connection_key=if(isnotnull(connection_id) AND len(trim(connection_id))>0, connection_id, connection_name)
+
+    | where mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)prompt injection|code detection|\\bPII\\b|personally identifiable information|\\bPCI\\b|payment card industry")>=0
+
+    | bin _time span=10m
+
+    ```Track repeated records with the same Cisco event ID separately from bursts of distinct event IDs```
+    | eventstats count as same_event_id_count by _time application_key connection_key event_id
+
+    | stats count as event_count
+            dc(event_id) as distinct_event_count
+            max(eval(if(isnotnull(event_id) AND len(trim(event_id))>0, same_event_id_count, 0))) as max_same_event_id_count
+            values(eval(if(isnotnull(event_id) AND len(trim(event_id))>0 AND same_event_id_count>=3, event_id, null()))) as repeated_event_ids
+            count(eval(lower(event_action)="allow")) as allowed_event_count
+            count(eval(lower(event_action)="block")) as blocked_event_count
+            values(event_id) as event_ids
+            values(event_action) as event_actions
+            values(event_message_type) as message_types
+            values(policy.policy_name) as policy_names
+            values(guardrail_ruleset_type) as guardrail_ruleset_types
+            values(connection_name) as connection_names
+            values(application_name) as application_names
+        by _time application_key connection_key
+
+    | where distinct_event_count>=5 OR max_same_event_id_count>=3
+
+    | table _time application_key application_names connection_key connection_names
+            event_count distinct_event_count max_same_event_id_count repeated_event_ids
+            allowed_event_count blocked_event_count event_actions message_types policy_names
+            guardrail_ruleset_types event_ids
+
+    | `cisco_ai_defense_repeated_high_risk_activity_filter`
+how_to_implement: |-
+    Install and configure the Cisco Security Cloud app to ingest Cisco AI Defense alerts into Splunk.
+    Confirm that extraction is enabled for the required fields as shown in the detection search.
+    Configure the "cisco_ai_defense" macro to match the index, source, and sourcetype used in your environment.
+    Adjust the five-distinct-event threshold, three-occurrence same-event-ID threshold, and ten-minute window as needed.
+known_false_positives: |-
+    Automated testing, red-team exercises, application QA, or a popular shared application may generate repeated guardrail events.
+    Review whether the window contains allowed events and whether the activity was expected for the application and connection.
+drilldown_searches:
+    - name: View the detection results for - "$application_key$"
+      search: '%original_detection_search% | search application_key="$application_key$"'
+      earliest_offset: $info_min_time$
+      latest_offset: $info_max_time$
+finding:
+    title: Cisco AI Defense observed [$event_count$] high risk event occurrences across [$distinct_event_count$] unique event IDs in ten minutes for application [$application_names$] and connection [$connection_names$]. Maximum same-ID count -> [$max_same_event_id_count$] ; Allowed events -> [$allowed_event_count$] ; Blocked events -> [$blocked_event_count$].
+    entity:
+        field: application_key
+        type: other
+        score: 50
+threat_objects: []
+analytic_story:
+    - Abuse of GenAI Systems and Applications
+    - Critical Alerts
+asset_type: Web Application
+mitre_attack_id: []
+product:
+    - Splunk Enterprise
+    - Splunk Enterprise Security
+    - Splunk Cloud
+category: application
+security_domain: endpoint
+tests:
+    - name: True Positive Test
+      attack_data:
+        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json
+          source: not_applicable
+          sourcetype: cisco:ai:defense
+      test_type: unit
diff --git a/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml b/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml
index cf1d7ef9e3..9aba8ccc19 100644
--- a/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml
+++ b/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml
@@ -1,45 +1,106 @@
 name: Cisco AI Defense Security Alerts by Application Name
 id: 105e4a69-ec55-49fc-be1f-902467435ea8
-version: 7
+version: 8
 creation_date: '2025-02-14'
-modification_date: '2026-05-13'
-author: Bhavin Patel, Splunk
+modification_date: '2026-10-15'
+author: Bhavin Patel, Nasreddine Bencherchali, Splunk
 status: production
 type: Anomaly
-description: The search surfaces alerts from the Cisco AI Defense product for potential attacks against the AI models running in your environment. This analytic identifies security events within Cisco AI Defense by examining event messages, actions, and policy names. It focuses on connections and applications associated with specific guardrail entities and ruleset types. By aggregating and analyzing these elements, the search helps detect potential policy violations and security threats, enabling proactive defense measures and ensuring network integrity.
+description: |-
+    This analytic summarizes Cisco AI Defense events by application, connection, action, message direction, and severity over ten-minute intervals.
+    Allowed responses flagged for privacy guardrails are handled by the dedicated sensitive-response detection.
+    Allowed prompts flagged for privacy guardrails are handled by Cisco AI Defense Allowed Sensitive Prompt.
+    Repeated prompt injection, code detection, PII, or PCI events are handled by the repeated high-risk activity detection.
 data_source:
     - Cisco AI Defense Alerts
 search: |-
     `cisco_ai_defense`
-      | rename genai_application.application_name as application_name
-      | rename connection.connection_name as connection_name
-      ```Aggregating data by model name, connection name, application name, application ID, and user ID```
-      | stats count
-          values(user_id) as user_id
-          values(event_message_type) as event_message_type
-          values(event_action) as event_action
-          values(policy.policy_name) as policy_name
-          values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name) as guardrail_entity_name
-          values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type) as guardrail_ruleset_type
-          by model.model_name connection_name application_name application_id
-      ```Evaluating severity based on policy name and guardrail ruleset type```
-      | eval severity=case(
-          policy_name IN ("AI Runtime Latency Testing - Prompt Injection"), "critical",
-          policy_name IN ("AI Runtime Latency Testing - Code Detection"), "high",
-          guardrail_ruleset_type IN ("Toxicity"), "medium",
+
+    | rename genai_application.application_name as application_name,
+             connection.connection_name as connection_name
+
+    | eval policy_name='policy.policy_name',
+
+           guardrail_ruleset_type=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', ", "),
+
+           guardrail_entity_name=mvjoin('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name', ", "),
+
+           action=lower(event_action), message_type=lower(event_message_type),
+
+           has_privacy=if(mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)\\bPII\\b|personally identifiable information|\\bPCI\\b|payment card industry")>=0, 1, 0),
+
+           has_sensitive_response=if(message_type="response" AND has_privacy=1, 1, 0),
+
+           has_security=if(mvfind('event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type', "(?i)prompt injection|code detection")>=0, 1, 0)
+
+    ```Prioritize allowed sensitive responses, then security and privacy guardrails```
+
+    | eval severity=case(
+          action="allow" AND has_sensitive_response=1, "critical",
+          action="block" AND has_sensitive_response=1, "high",
+          action="allow" AND has_security=1, "high",
+          action="block" AND has_security=1, "medium",
+          action="allow" AND has_privacy=1, "high",
+          action="block" AND has_privacy=1, "medium",
           true(), "low"
       )
-      ```Calculating risk score based on severity level```
-      | eval risk_score=case(
-          severity="critical", 100,
-          severity="high", 75,
-          severity="medium", 50,
-          severity="low", 25
-      )
-      | table model.model_name, user_id, event_action, application_id, application_name, severity, risk_score, policy_name, connection_name, guardrail_ruleset_type, guardrail_entity_name
-      | `cisco_ai_defense_security_alerts_by_application_name_filter`
-how_to_implement: To enable this detection, you need to ingest alerts from the Cisco AI Defense product. This can be done by using this app from splunkbase - Cisco Security Cloud and ingest alerts into the cisco:ai:defense sourcetype.
-known_false_positives: False positives may vary based on Cisco AI Defense configuration; monitor and filter out the alerts that are not relevant to your environment.
+
+    | eval risk_score=case(
+        severity="critical", 100,
+        severity="high", 80,
+        severity="medium", 50,
+        severity="low", 20
+    )
+
+    | eval application_name=if(isnotnull(application_name) AND len(trim(application_name))>0, application_name, "unknown"),
+           connection_name=if(isnotnull(connection_name) AND len(trim(connection_name))>0, connection_name, "unknown"),
+           application_key=if(isnotnull(application_id) AND len(trim(application_id))>0, application_id, application_name),
+           connection_key=if(isnotnull(connection_id) AND len(trim(connection_id))>0, connection_id, connection_name),
+           is_high_risk=if(has_security=1 OR has_privacy=1, 1, 0),
+           is_allowed_sensitive_response=if(action="allow" AND has_sensitive_response=1, 1, 0),
+           is_allowed_sensitive_prompt=if(action="allow" AND message_type="prompt" AND has_privacy=1, 1, 0),
+           high_risk_event_id=if((has_security=1 OR has_privacy=1) AND is_allowed_sensitive_prompt=0, event_id, null()),
+           event_time=_time
+
+    | bin _time span=10m
+
+    ```Use the same application/connection window and threshold as Cisco AI Defense Repeated High Risk Activity```
+    | eventstats dc(high_risk_event_id) as high_risk_event_count by _time application_key connection_key
+
+    ```Leave allowed sensitive responses to Cisco AI Defense Allowed Sensitive Response, allowed sensitive prompts to Cisco AI Defense Allowed Sensitive Prompt, and repeated high-risk bursts to Cisco AI Defense Repeated High Risk Activity```
+    | where is_allowed_sensitive_response=0 AND is_allowed_sensitive_prompt=0 AND NOT (is_high_risk=1 AND high_risk_event_count>=5)
+
+    | stats count as event_count
+            dc(event_id) as distinct_event_count
+            min(event_time) as firstTime
+            max(event_time) as lastTime
+            values(event_id) as event_ids
+            values(event_action) as event_actions
+            values(event_message_type) as message_types
+            values(resource_type) as resource_types
+            values(model.model_name) as model_names
+            values(application_name) as application_name
+            values(connection_name) as connection_name
+            values(policy_name) as policy_names
+            values(guardrail_ruleset_type) as guardrail_ruleset_types
+            values(guardrail_entity_name) as guardrail_entity_names
+            max(risk_score) as risk_score
+        by _time application_key connection_key action message_type severity
+
+    | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`
+
+    | table _time firstTime lastTime application_key application_name connection_key connection_name
+            action message_type severity risk_score event_count distinct_event_count event_ids
+            resource_types model_names policy_names guardrail_ruleset_types guardrail_entity_names
+
+    | `cisco_ai_defense_security_alerts_by_application_name_filter`
+how_to_implement: |-
+    Install and configure the Cisco Security Cloud app to ingest Cisco AI Defense alerts into Splunk with the cisco:ai:defense sourcetype.
+    Confirm that extraction is enabled for the required fields as shown in the detection search.
+    Configure the cisco_ai_defense macro to match the index, source, and sourcetype used in your environment.
+known_false_positives: |-
+    False positives may vary based on Cisco AI Defense configuration; monitor and filter out the alerts that are not relevant to your environment.
 references:
     - https://www.robustintelligence.com/blog-posts/prompt-injection-attack-on-gpt-4
     - https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/common-attacks.html
@@ -57,8 +118,10 @@ intermediate_findings:
         - field: application_name
           type: other
           score: 20
-          message: Cisco AI Defense Security Alert has been action - [$event_action$] for the application name - [$application_name$]
+          message: Cisco AI Defense summarized [$event_count$] events for application [$application_name$] and connection [$connection_name$] over ten minutes. Actions [$action$], message direction [$message_type$], severity [$severity$], guardrails [$guardrail_ruleset_types$], event IDs [$event_ids$].
+threat_objects: []
 analytic_story:
+    - Abuse of GenAI Systems and Applications
     - Critical Alerts
 asset_type: Web Application
 mitre_attack_id: []
@@ -72,7 +135,7 @@ tests:
     - name: True Positive Test
       attack_data:
         - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json
-          source: cisco_ai_defense
+          source: not_applicable
           sourcetype: cisco:ai:defense
-      description: PORTED MANUAL TEST - We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection.
-      test_type: experimental
+      description: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection.
+      test_type: unit
diff --git a/install.yml b/install.yml
index 28ef2c7679..cb3196171a 100644
--- a/install.yml
+++ b/install.yml
@@ -12,10 +12,10 @@ apps:
       version: 2.3.4
     - appid: Splunk_TA_stream
       url: https://splunkbase.splunk.com/app/5238
-      version: 8.1.6
+      version: 8.1.7
     - appid: Splunk_TA_stream_wire_data
       url: https://splunkbase.splunk.com/app/5234
-      version: 8.1.6
+      version: 8.1.7
     - appid: TA-Zscaler_CIM
       url: https://splunkbase.splunk.com/app/3865
       version: 4.1.5
diff --git a/stories/abuse_of_genai_systems_and_applications.yml b/stories/abuse_of_genai_systems_and_applications.yml
new file mode 100644
index 0000000000..872cc2f0b4
--- /dev/null
+++ b/stories/abuse_of_genai_systems_and_applications.yml
@@ -0,0 +1,23 @@
+name: Abuse of GenAI Systems and Applications
+id: f79c6573-5550-4adc-b673-bb0d8c9e1df1
+version: 1
+creation_date: '2026-10-15'
+modification_date: '2026-10-15'
+author: Nasreddine Bencherchali, Splunk
+status: production
+description: |-
+    Detect and investigate threats targeting or abusing artificial intelligence systems, services and applications.
+    Coverage includes prompt injection, attempts to bypass model safeguards, repeated high-risk guardrail events, and possible sensitive data disclosure through model interactions.
+narrative: |-
+    AI applications can expose sensitive information, invoke connected tools, or process untrusted prompts. Attackers and malicious insiders may try to override model instructions, extract restricted data, misuse AI-enabled tools, or repeatedly probe guardrails.
+    Use this story to investigate AI security events across model providers and application integrations. Review the affected application and connection, event action, prompt or response direction, policy, and guardrail categories. An allowed event can indicate exposure, while a blocked event records an attempt; neither alone confirms the full impact. Correlate high-risk events with identity, application, data access, and tool activity where those logs are available.
+references:
+    - https://atlas.mitre.org/
+    - https://owasp.org/www-project-top-10-for-large-language-model-applications/
+category:
+    - Adversary Tactics
+product:
+    - Splunk Enterprise
+    - Splunk Enterprise Security
+    - Splunk Cloud
+usecase: Advanced Threat Detection