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DOCS-606 - Wistia video links (#4905)
* First set * Next set * Another set * Remove old videos * Add final videos * Remove video from support article * Remove admin mode video
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‎docs/alerts/monitors/alert-response.md

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Learn how to use alert response.
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<Iframe url="https://fast.wistia.net/embed/iframe/elkucyy4ap?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: Using Alert Response Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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<Iframe url="https://www.youtube.com/embed/3FHomBuFyV8?rel=0"
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width="854px"
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height="480px"
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allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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/>
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-->
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:::
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‎docs/alerts/monitors/create-monitor.md

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---
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import Iframe from 'react-iframe';
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This guide will walk you through the steps of creating a monitor in Sumo Logic, from setting up trigger conditions to configuring advanced settings, notifications, and playbooks.
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#### Anomaly
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Leverage machine learning to identify unusual behavior and suspicious patterns by establishing baselines for normal activity. This [*AI-driven alerting*](https://www.youtube.com/watch?v=nMRoYb1YCfg) system uses historical data to minimize false positives and alerts you to deviations.
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Leverage machine learning to identify unusual behavior and suspicious patterns by establishing baselines for normal activity. This *AI-driven alerting* system uses historical data to minimize false positives and alerts you to deviations.
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* **Model-driven detection**. Machine learning models create accurate baselines, eliminating guesswork and noise.
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* **AutoML**. The system self-tunes with seasonality detection, minimizing user intervention and adjusting for recurring patterns to reduce false positives.
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* **Auto-diagnosis and recovery**. The Automation Service handles diagnosis and resolution, closing the loop from alert to recovery.
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* **Customizable detection**. Use advanced rules like "Cluster anomalies" to detect multiple data points exceeding thresholds within a set timeframe.
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:::sumo Micro Lesson
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Learn about AI-driven alerting.
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<Iframe url="https://fast.wistia.net/embed/iframe/8z9b2zqtc3?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: AI-driven Alerting Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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<!-- old
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<Iframe url="https://www.youtube.com/embed/nMRoYb1YCfg?rel=0"
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width="854px"
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height="480px"
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id="myId"
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className="video-container"
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display="initial"
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position="relative"
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allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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/>
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-->
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:::
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**Use Outlier**
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If you want to trigger alerts on outlier direction rather than anomaly detection, select **Anomaly** and enable **Use Outlier**. This detects unusual changes or spikes in a time series of a key indicator. Use this detection method when you are alerting on KPIs that don't have well-defined constant thresholds for what's good and bad. You want the monitor to automatically detect and alert on unusual changes or spikes on the alerting query. For example, application KPIs like page request, throughput, and latency. <br/><img src={useBaseUrl('img/alerts/monitors/monitor-detector-types-for-anomaly.png')} alt="Screenshot of the Monitor Type and Detection Method options in Sumo Logic's 'New Monitor' setup page. Logs is selected as the Monitor Type, and Anomaly is selected as the Detection Method. There is an option to use Outlier detection, which is currently toggled off." width="300"/>

‎docs/alerts/monitors/use-playbooks-with-monitors.md

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import useBaseUrl from '@docusaurus/useBaseUrl';
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import Iframe from 'react-iframe';
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This article describes how to configure automated playbooks in monitors. An *automated playbook* is a [playbook in the Automation Service](/docs/platform-services/automation-service/automation-service-playbooks/), and is a predefined set of actions and conditional statements that run in an automated workflow to respond to an event. For example, suppose that a monitor detects suspicious behavior that could indicate a security problem. When the monitor sends the alert, it could also run an automated playbook to respond to the event.
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Weekly seasonality detection is turned off by default to optimize performance. [Contact Sumo Logic Customer Support](https://support.sumologic.com/support/s/contactsupport) to activate it for specific monitors. (*Weekly seasonality detection* is the optimization of baseline calculations to account for the variations of data flow that can occur in a work week.)
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:::sumo Micro Lesson
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Watch this micro lesson to learn about anomaly monitors.
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<Iframe url="https://fast.wistia.net/embed/iframe/8z9b2zqtc3?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: AI-driven Alerting Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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<!-- old
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<Iframe url="https://www.youtube.com/embed/nMRoYb1YCfg?rel=0"
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width="854px"
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height="480px"
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id="myId"
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className="video-container"
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display="initial"
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position="relative"
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allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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/>
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import Iframe from 'react-iframe';
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width="854px"
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height="480px"
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id="myId"
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className="video-container"
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display="initial"
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position="relative"
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allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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/>
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-->
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:::
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To create an anomaly monitor that runs an automated playbook in response to an alert:
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‎docs/apm/real-user-monitoring/configure-data-collection.md

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:::sumo Micro Lesson
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Using the RUM HTTP Traces App for Manual Testing.
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<Iframe url="https://fast.wistia.net/embed/iframe/qmxk5wxqu5?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Using the RUM HTTP Traces App for Manual Testing Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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## Prerequisites

‎docs/apm/real-user-monitoring/index.md

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:::sumo Micro Lesson
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See Real User Monitoring in action.
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<Iframe url="https://fast.wistia.net/embed/iframe/jfptjgwql1?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: Real User Monitoring (RUM) 2.0 Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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‎docs/apm/traces/get-started-transaction-tracing/opentelemetry-instrumentation/java/index.md

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Tutorial: Auto-instrumentation of a Java app by OpenTelemetry for K8s Environment.
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<Iframe url="https://fast.wistia.net/embed/iframe/p46o4kivj4?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Tutorial: Auto-instrumentation of a Java app by OpenTelemetry for K8s Environment Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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/>
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‎docs/apm/traces/quickstart.md

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[**Classic UI**](/docs/get-started/sumo-logic-ui-classic). To access Traces, go to the **Home** screen and select **Traces**.
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[**New UI**](/docs/get-started/sumo-logic-ui/). To access Traces, in the main Sumo Logic menu, select **Observability**, and then under **Application Monitoring**, select **Transaction Traces**. You can also click the **Go To...** menu at the top of the screen and select **Transaction Traces**.
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## Micro Lesson
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## Micro lesson
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This micro lesson can help you get started with Tracing.
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<Iframe url="https://fast.wistia.net/embed/iframe/zcg3x7r420?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: Introduction to Tracing Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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/>
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:::
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Trace data is visualized through filtered trace lists and icicle charts allowing you to find and troubleshoot faulty transactions easily. See how easy it is to [view and investigate traces](view-and-investigate-traces.md).
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‎docs/apm/traces/spans.md

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This micro lesson provides an overview of Span Analytics, and describes the term Span in the distributed tracing and the benefits of Span Analytics. It also explains how to perform Span Analytics in Sumo Logic UI.
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<Iframe url="https://fast.wistia.net/embed/iframe/t2q0n8etid?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: Span Analytics Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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allowfullscreen
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/>
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‎docs/cloud-soar/incidents-triage.md

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[**New UI**](/docs/cloud-soar/overview#new-ui). To access incidents, in the main Sumo Logic menu select **Cloud SOAR > Incidents**. You can also click the **Go To...** menu at the top of the screen and select **Incidents**.
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:::sumo Micro Lesson
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<Iframe url="https://fast.wistia.net/embed/iframe/faxotvoq3j?web_component=true&seo=true&videoFoam=false"
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title="Micro Lesson: Cloud SOAR Incidents Video"
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className="video-container"
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display="initial"
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Cloud SOAR's **Dashboards** section highlights the most important pieces of data to the user or investigator who is logged into the platform. This data is presented through the use of multiple widgets that you can add, remove, and customize to include all data relevant to your job functions and duties.
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Watch the following micro lesson to learn about dashboards.
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<Iframe url="https://fast.wistia.net/embed/iframe/gqclutn3d1?web_component=true&seo=true&videoFoam=false"
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width="854px"
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height="480px"
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title="Micro Lesson: Cloud SOAR Dashboards &amp; KPI Reports Video"
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id="wistiaVideo"
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className="video-container"
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display="initial"
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position="relative"
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allow="autoplay; fullscreen"
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‎docs/cloud-soar/legacy/legacy-global-functions-menu.md

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ARK assists operators during investigations in two main areas: Automatically suggesting/prompting next actions/tasks in Playbooks (until version 5) and Correlation/ Deduplication of similar threats into 1 unique incident.
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