Nexthink Insights - AI Model Card
Model details
Description
Nexthink Insights is a set of AI-powered capabilities within the Nexthink Infinity platform designed to enhance Digital Employee Experience (DEX) troubleshooting, prioritization, and root cause analysis. Leveraging GenAI models operated by Nexthink within the Nexthink AWS environment in your region, Insights provides contextual descriptions, recommendations, and impact analysis to help IT teams detect, understand, and resolve issues faster.
Insights capabilities are embedded in multiple parts of Infinity, including Experience Central, VDI Experience, Network View, Device View, and Alert Impact Analysis. These AI functionalities interpret technical and contextual data to produce clear, actionable insights—such as identifying likely root causes of poor performance, summarizing employee sentiment trends, or prioritizing alerts based on potential business impact.
Refer to the FAQ documentation for more information about Nexthink and to the links below for more information about Nexthink Insights.
Inputs and outputs
Inputs:
Telemetry data and metrics from Nexthink modules (e.g., device metrics, network data, application performance metrics, session events)
Employee-provided open-text feedback from sentiment campaigns
Alert context, monitored thresholds, and triggering conditions
Organizational metadata (e.g., departments from Microsoft Entra ID)
User interactions, such as selecting health events, hovering over network nodes, or filtering feedback topics
Outputs:
AI-generated contextual explanations of detected patterns or anomalies
Summarized employee sentiment trends and recommended actions
Root cause hypotheses for performance or reliability issues
Prioritized alert impact analysis with affected entities and urgency scoring
Visual and textual summaries embedded within dashboards and right-side panels
Intended use
Primary intended users
IT operations teams, digital workplace specialists, and Nexthink Infinity users responsible for monitoring and improving DEX.
Out-of-scope use cases
Insights is designed for DEX-related analysis only. It should not be used to make non-DEX business decisions or to process data unrelated to employee experience and IT performance. AI-generated recommendations are meant to guide IT improvements, not replace formal operational procedures.
Model data
Data flow
Insights executes AI-powered analysis in several steps:
Data Collection: Relevant telemetry, employee feedback, and contextual metadata are securely gathered from the customer’s Nexthink environment. Examples include device performance metrics, VDI session details, failed connection statistics, and open-text sentiment campaign responses.
AI Processing: Within the AWS-hosted environment in the customer’s region, LLMs process this input to identify trends, classify topics, detect anomalies, or prioritize issues. For example:
In Employee Insights, open-text comments are processed daily for sentiment and topic detection, then summarized weekly with recommended actions.
In Network View, hovering over nodes or lines triggers AI analysis to identify purpose, compliance status, and whether connection metrics indicate abnormal or risky behavior.
In Device View, AI interprets high CPU/memory consumption, crashes, or binary issues to suggest potential causes.
In Session View, In Session View, AI reviews performance data collected from your VDI environment, including duration-based health indicators, performance metrics and detected anomalies, to uncover patterns and help pinpoint likely root causes.
In Alert Impact Analysis, AI evaluates the scope, severity, and context of alerts to prioritize remediation efforts.
Output Generation: AI results are formatted into summaries, visual indicators, and actionable recommendations, displayed within the appropriate Infinity module.
All processing occurs entirely within the Nexthink AWS environment in the customer’s geographical region.
Evaluation data
Nexthink employs a set of performance metrics, including precision, recall, and proprietary in-house metrics, tailored to specific components of the system. Test datasets are used to validate model updates, ensuring the AI system’s accuracy and reliability align with company standards. Additionally, continuous monitoring of model performance allows Nexthink to proactively address potential issues, reducing errors and improving response quality over time.
Human oversight remains essential: users should review and verify AI-generated results, applying their expertise and judgment before acting on the information provided.
Training data
Insights uses off-the-shelf LLMs hosted on AWS Bedrock. Nexthink does not fine-tune this model and does not use Customer Data to train AI models.
Preprocessing data
For Employee Insights, the system processes comments that employees provided in the form of free text when answering the DEX Score Sentiment Engage Campaign. For other Insights features, AI operates on product telemetry already collected by Infinity (e.g., crashes, CPU/memory usage, connection metrics, alert context) within the customer’s AWS environment.
Implementation information
Hardware
Models run within AWS infrastructure in the customer’s geographic region using AWS Bedrock.
Software
Insights uses off-the-shelf LLMs hosted via AWS Bedrock.
Security
Nexthink employs HTTPS and AES-256 encryption to secure data both in transit and at rest. Nexthink's use of standard encryption methods aligns with industry best practices to prevent unauthorized access and protect data processed by AI features. Visit Nexthink Security Portal to learn more about Nexthink's commitment to information security.
Caveats and recommendations
Risk management
AI tool availability
In cases where the AI-powered Insights capabilities are temporarily unavailable, users can fall back on manually analyzing telemetry data, employee feedback, and alert details using existing Nexthink dashboards, investigations, and filtering tools. This resilience plan ensures uninterrupted access to core DEX monitoring and troubleshooting functionality, allowing users to continue diagnosing and addressing issues without disruption.
Hallucination and bias propagation
Model hallucinations and biases are mitigated by Nexthink through continuous performance monitoring and regular model updates. Sources used to create a response are provided to users who want to check the accuracy of its responses. Users should always verify AI generated content to further mitigate the risks of hallucination and bias.
Inaccuracy of outputs
Nexthink also employs a set of performance metrics, including precision, recall, and proprietary in-house metrics, tailored to specific components of the system. Test datasets are used to validate model updates, ensuring the AI system’s accuracy and reliability align with company standards. Additionally, continuous monitoring of model performance allows Nexthink to proactively address potential issues, reducing errors and improving response quality over time. Also, in Nexthink Insights, users can rate the results of AI-provided queries, helping to identify inaccuracies and improve model performance. This feedback mechanism allows Nexthink to continually refine the AI model’s output accuracy. Nevertheless, even though AI is improving every day, it still makes mistakes. This implies that AI features / functionalities are there for enhancing the DEX experience of end users, but they should still ensure careful review of the outputs provided and verify accuracy.
Unauthorized access or misuse
All AI processing occurs entirely within the customer’s secure AWS environment, in the same geographic region as their Nexthink instance. Data is encrypted in transit and at rest using industry-standard protocols such as HTTPS and AES-256, ensuring protection against interception or tampering. Additionally, Insights operates only on data already accessible to the user within their existing Nexthink environment, preventing exposure of information outside established access rights. This design safeguards sensitive data and ensures AI-generated outputs remain consistent with the organization’s existing security boundaries.
Ethical considerations
Nexthink follows both national and international AI guidelines and best practices, emphasizing responsible and ethical AI development. In compliance with the EU AI Act, Nexthink has developed a comprehensive AI compliance framework. Each AI component is reviewed by a dedicated AI Compliance Team comprising Legal, Privacy and Security experts, among others.
When an AI functionality involves the processing of personal data, Nexthink ensures it undergoes a thorough privacy assessment, as required by applicable data protection laws and regulations. While the processing of personal data is not a core component of Nexthink Insights, Nexthink's privacy team has conducted a Data Protection Impact Assessment (DPIA). This assessment aligns with our internal policies, established standards, and recognized best practices in privacy management.
AI Limitations
While Insights can significantly speed up troubleshooting and decision-making, it can produce occasional errors or incomplete conclusions. To mitigate these risks, Customers may:
Cross-Check AI Outputs: Validate AI-generated results against reliable sources or internal benchmarks.
Implement Human Oversight: Use AI as a supporting tool rather than a decision-making authority, ensuring that critical outputs are reviewed by qualified individuals.
Provide Feedback: When inaccuracies are identified, share them with Nexthink to contribute to model improvement.
FAQ
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