Other GenAI Features
Last updated
Last updated
Nexthink can trigger multiple alerts to inform about ongoing IT issues in the digital workplace environment. Some of these issues are operational, affecting only a few devices and often going unnoticed by employees. Others are critical, directly impacting business operations and/or employee productivity. Quickly understanding the issue's impact is essential for accurately assessing its urgency to plan the resolution. It helps reduce the resolution time of issues that matter the most and minimize the impact on employee productivity and the business.
Refer to the documentation for more information.
Alert impact analysis leverages an LLM model to help better assess and categorize the issue's impact by understanding the alert's details and its meaning for its effect on the employee's productivity, business, and IT operations.
The LLM evaluates the impact of an alert issue using the following information:
Alert name, for example, Application errors increase.
Details of the triggered alert, as displayed in the , such as the application name, for example, Salesforce, without including Personal Data.
Monitored conditions with thresholds.
Details of the alert like the number of recent triggers, current status, and level of breached conditions.
Number of devices impacted by the issue with listed entities.
The following evaluation guidelines are part of the rules given to LLM:
Assess the application importance. Give higher impact to applications that are important for the business.
The alert has higher importance if it impacts a larger number of devices.
Give a higher impact to issues that directly affect employees.
The impact assessment is categorized into one of three levels based on this evaluation:
SIGNIFICANT
MODERATE
MINIMAL
Users cannot interact with the feature to change the evaluation criteria or input additional information.
The impact analysis serves as a recommendation to help evaluate the significance of the alert. Hence, users need to review and gather accurate information as required, considering that this assessment is generated by AI.
The Alert impact analysis aims to assess the impact of the issue across the digital workplace, rather than focusing on individual devices. Therefore, no GenAI components send any Personal Data or device-level information to LLM. Please note that the payload context of the alert never contains Personal Data, as any Personal Data information is stored separately and is not an input to the Alerts impact assessment.
The contextual information of the issue contains only information about the number of devices impacted without listing any details.
The LLM for processing the contextual alert information is operated directly by Nexthink within the AWS ecosphere, which is common with the Nexthink platform.
No, LLM cannot and does not use data submitted by customers via its APIs to train or improve its models.
The retention period for submitted data is 0s.
Please contact Nexthink customer support if you wish to deactivate the GenAI Alert impact analysis feature.
