Executive Summary dashboard

The Executive Summary dashboard is designed to provide a high level insight into security operations so that executives can evaluate security trends over time based on key metrics, notables, risk, and other additional metrics. Use the Executive Summary dashboard to prioritize security operations, monitor the overall health and evaluate the risk to your organization.

Dashboard panels

Key metrics

Panel	Description and default search
Mean Time to Triage	Displays the average time (in minutes) to triage or prioritize the investigation of a notable over the duration of a specified time period. Also, displays a trendline (in absolute value) that indicates how the mean time taken to triage the notable compares to the previous mean time taken to triage the notable over the same time period. For example, the trendline may display that the mean time to triage a notable over the last 7 days is 0.5% up or down over the mean time taken to triage the notable during the previous 7 day time period. For more information, see Triage notable events in Splunk Enterprise Security. | tstats summariesonly=true earliest(_time) as _time FROM datamodel=Incident_Management BY "Notable_Events_Meta.rule_id" | rename "Notable_Events_Meta.*" as "*" | lookup update=true incident_updates_lookup rule_id OUTPUTNEW time | search time=* | stats earliest(_time) as create_time, earliest(time) as triage_time by rule_id | eval diff=triage_time-create_time, stat_type=if(create_time < relative_time(now(), "-7d@d"), "past", "current"), past=if(stat_type="past", 1, 0), current=if(stat_type="current", 1, 0), past_diff=if(stat_type="past", diff, 0), current_diff=if(stat_type="current", diff, 0) | stats sum(past) AS past, sum(current) AS current, sum(past_diff) AS past_diff, sum(current_diff) as current_diff | eval past = round(past_diff/past/60), current = round(current_diff/current/60) | table past, current | transpose | rename "column" as stat_type,"row 1" as mean_triage_time | fillnull value=0 mean_triage_time
Mean Time to Resolution	Displays the average time (in minutes) taken by the notable to reach its configured end status over the duration of a specified time period. Also, displays a trendline (in absolute value) that indicates how the mean time taken by the notable to reach its configured end status compares to the previous mean time taken by the notable to reach its configured end status over the same time period. For more information, see Take action on notable events in Splunk Enterprise Security. | tstats summariesonly=true earliest(_time) as _time FROM datamodel=Incident_Management BY "Notable_Events_Meta.rule_id" | rename "Notable_Events_Meta.*" as "*" | eval temp_time=time()+86400 | lookup update=true event_time_field=temp_time incident_review_lookup rule_id OUTPUTNEW time, status | `get_reviewstatuses` | search time=* AND status_end=true | stats first(_time) as create_time, last(time) as resolve_time by rule_id | eval diff=resolve_time-create_time, stat_type=if(create_time < relative_time(now(), "-7d@d"), "past", "current"), past=if(stat_type="past", 1, 0), current=if(stat_type="current", 1, 0), past_diff=if(stat_type="past", diff, 0), current_diff=if(stat_type="current", diff, 0) | stats sum(past) AS past, sum(current) AS current, sum(past_diff) AS past_diff, sum(current_diff) as current_diff | eval past = round(past_diff/past/60), current = round(current_diff/current/60) | table past, current | transpose | rename "column" as stat_type,"row 1" as mean_resolution_time | fillnull value=0 mean_resolution_time
Investigations Created	Displays the number of investigations created in the SOC over the duration of a specified time period. Also, displays a trendline (in absolute value) that indicates how the mean number of investigations created compares to the previous mean number of investigations created over the same time period. For more information, see Start an investigation in Splunk Enterprise Security. | inputlookup investigation_lookup | where create_time > relative_time(now(), "-14d@d") | stats count(eval(create_time < relative_time(now(), "-7d@d"))) AS past, count(eval(create_time >= relative_time(now(), "-7d@d"))) AS current | transpose | rename "column" as count_type, "row 1" as count

You can access the Key Performance Indicator (KPI) panel for Investigations Created on the Executive Summary Dashboard. Only the admin and the ess_admin roles have the manage_all_investigations capability by default. For all other roles such as ess_analystor ess_user, you see an error message on the Investigations Created KPI panel. An administrator can add the manage_all_investigations capability for users that allows other users to access the Investigations Created KPI panel on the Executive Summary dashboard. For more information on adding capabilities to a specific role, see Specify role capabilities.

Notables

Panel	Description and default search
Distribution by Urgency	Displays the distribution of the urgency level that is calculated based on the severity and priority level of a notable over the duration of a specified time period. The distribution is based on the following categories: Critical, High, Medium, Low, Information, and Unknown. For more information, see How urgency is assigned to a notable event in Splunk Enterprise Security. `get_notable_index` | eval `get_event_id_meval`, rule_id=event_id, temp_time=time()+86400 | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW severity | lookup update=true event_time_field=temp_time incident_review_lookup rule_id OUTPUT urgency as new_urgency | eval urgency=if(isnotnull(new_urgency),new_urgency,urgency) | `get_urgency` | eval urgency = upper(substr(urgency,1,1)).lower(substr(urgency,2)) | timechart span=1d count by urgency
Notables by Domain	Displays the classification of the notables by security domains, such as Access, Endpoint, Network, Threat, Identity, and Audit over the duration of a specified time period. | tstats summariesonly=true earliest(_time) as _time, first(source) as source FROM datamodel=Incident_Management BY "Notable_Events_Meta.rule_id" | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW security_domain | fillnull value="threat" security_domain | lookup update=true security_domain_lookup security_domain OUTPUTNEW label as security_domain_label | timechart span=1d count by security_domain_label
Untriaged Notables by Domain	Displays the classification the untriaged notables by security domain, such as Access, Endpoint, Network, Threat, Identity, and Audit over the duration of a specified time period. | tstats summariesonly=true earliest(_time) as _time, first(source) as source FROM datamodel=Incident_Management BY "Notable_Events_Meta.rule_id" | rename "Notable_Events_Meta.*" as "*" | eval temp_time=time()+86400 | lookup update=true event_time_field=temp_time incident_review_lookup rule_id OUTPUT time as triage_time | where isnull(triage_time) | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW security_domain | fillnull value="threat" security_domain | lookup update=true security_domain_lookup security_domain OUTPUTNEW label as security_domain_label | timechart span=1d count by security_domain_label
Top 10 Untriaged Notables by Source	Displays the top 10 untriaged notables by their sources over the duration of a specified time period. `get_notable_index` | eval `get_event_id_meval`, rule_id=event_id, temp_time=time()+86400 | lookup update=true event_time_field=temp_time incident_review_lookup rule_id OUTPUT time as triage_time | where isnull(triage_time) | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW rule_name | eval rule_name=if(isnull(rule_name),source,rule_name) | stats count by rule_name | sort - count | head 10
Untriaged Notables by Type	Displays the classification of notables based on whether they are risk notables or regular notables over the duration of a specified time period. `get_notable_index` | eval `get_event_id_meval`, rule_id=event_id, temp_time=time()+86400 | lookup update=true event_time_field=temp_time incident_review_lookup rule_id OUTPUT time as triage_time | where isnull(triage_time) | eval type=if(isnotnull(risk_object), "Risk Notable", "Notable") | timechart span=1d count by type
Frequent Notables Event Sources	Displays the sources that generate the most number of notables over the duration of a specified time period. `get_notable_index` | eval source=case(isNotNull(orig_source), orig_source, isNotNull(source_correlation_search), source_correlation_search, 1=1, source) | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW rule_name | eval rule_name=if(isnull(rule_name),source,rule_name) | stats count by rule_name | sort - count | head 10
	Displays the sources that generate the least number of notables over the duration of a specified time period. `get_notable_index` | eval source=case(isNotNull(orig_source), orig_source, isNotNull(source_correlation_search), source_correlation_search, 1=1, source) | lookup update=true correlationsearches_lookup _key as source OUTPUTNEW rule_name | eval rule_name=if(isnull(rule_name),source,rule_name) | stats count by rule_name | sort + count | head 10|}

Risk

Panel	Description and default search
Risk Notables vs Notable Events	Displays a comparison graph of regular notables versus risk notables in the SOC over the duration of a specified time period. `get_notable_index` | eval notable_type=if(isnotnull(risk_object) AND isnotnull(risk_object_type), "Risk Notable", "Notable") | fields notable_type, count | timechart span=1d count by notable_type
Risk Events Contributing to Risk Notables	Displays a comparison graph of risk events that generated risk notables versus the risk events that did not generate risk notables over the duration of a specified time period. (index=risk ) OR (`get_notable_index` risk_object=* ) | eval source=case(index="risk",source,isnull(orig_source),source_correlation_search,1=1,orig_source),search_time=if(index="notable",mvzip(info_min_time,mvsort(info_max_time)),null()),risk_id=if(index="risk",replace(_bkt,".*~(.+)","\1")."@@".index."@@".md5(_time._raw),null()), risk_id_time=if(index="risk",mvzip(risk_id,_time),null()) | stats values(index) AS index, values(risk_id_time) AS risk_id_time, values(search_time) AS search_time by source, risk_object, risk_object_type | mvexpand risk_id_time | mvexpand search_time | eval risk_id=if(isnull(risk_id_time),null(),mvindex(split(risk_id_time,","),0)),risk_time=if(isnull(risk_id_time),null(),mvindex(split(risk_id_time,","),1)),search_earliest=if(isnull(search_time),null(),mvindex(split(search_time,","),0)), search_latest=if(isnull(search_time),null(),mvindex(split(search_time,","),1)),contributing=if(isnull(search_earliest) OR isnull(search_latest) OR risk_time <= search_earliest OR risk_time >= search_latest,"false","true") | stats values(contributing) as contributing, values(risk_time) as _time by risk_id | eval contributed=if(contributing="true", "Contributed", "Not Contributed") | timechart span=1d count by contributed
Risk Event Types Not Contributing to Risk Notables	Displays a list in descending order of frequency of the type of risk events that did not generate risk notables over the duration of a specified time period. (index=risk ) OR (`get_notable_index` risk_object=* ) | eval source=if(index="notable",if(isnull(orig_source),source_correlation_search, orig_source), source) | stats count, values(index) as index by source | where index != "notable" | `get_correlations` | table rule_name, count | sort - count

Additional Metrics

Panel	Description and default search
Adaptive Response Actions Triggered	Displays a graph indicating the type and frequency of the adaptive response actions that were triggered over the duration of a specified time period. | tstats summariesonly=true count from datamodel=Splunk_Audit.Modular_Actions where Modular_Actions.is_Modular_Action_Invocations=1 by _time, Modular_Actions.action_mode, Modular_Actions.action_name | `drop_dm_object_name("Modular_Actions")` | eval action_mode=if(action_mode="saved","automated", action_mode), action_name=action_mode+"-"+action_name | fields - action_mode | timechart span=1d sum(count) as count by action_name
Sources with Notable Action vs Risk Action Enabled	Displays a graph indicating how many enabled sources have risk actions versus notables actions over the duration of a specified time period. | inputlookup correlationsearch_changes_lookup | where _time > relative_time(now(),"-7d@d") | sort - _time | bin _time span=1d | dedup label, _time | where (disabled == 0) | mvexpand actions | where actions="notable" OR actions="risk" | eval actions=if(actions="notable", "Notable Action", "Risk Action") | timechart span=1d count by actions
Correlation Searches Enabled vs Disabled	Displays a bar chart that provides a distribution of the correlation searches enabled versus correlation searches disabled in the SOC over the duration of a specified time period. | inputlookup correlationsearch_changes_lookup | where _time > relative_time(now(),"-7d@d") | sort - _time | bin _time span=1d | dedup label, _time | timechart span=1d count by disabled | rename 0 as Enabled, 1 as Disabled

For key indicator panels and time chart visualizations on the Executive Summary dashboard, some arguments in the underlying SPL searches may be dynamically updated based on the time range selected on the dashboard UI.