Automated Risk Calculation

  • Updated

Automated Risk Calculation enables organizations to assess, score, and summarize risk for AI systems and datasets using standardized risk assessment templates. Each assessment produces its own risk score, and RexCommand then calculates an aggregated (highest) risk recommendation for the assessed entity.

Purpose

Automated Risk Calculation helps organizations consistently evaluate AI and data risks using predefined assessment templates aligned with regulatory, industry, and responsible AI frameworks.

This capability allows you to:

  • Run structured risk assessments against AI systems and datasets.

  • Receive an individual risk score for each assessment.

  • Automatically generate a aggregated risk recommendation for the entity.

  • Maintain flexibility by allowing manual risk overrides when required.

This approach balances automation and governance guidance with user ownership and accountability.

 

 

New Risk Assessment Templates

RexCommand provides a growing library of risk assessment templates. Each template applies to either AI systems, datasets, or both.
The following new templates expand automated risk coverage across technical, operational, and human factors:

  • Explainable AI Assessment
    Applicability: AI Systems & Datasets
    Assess whether an AI model or system is explainable and understandable to relevant stakeholders.

  • AI System Lifecycle Risk Assessment
    Applicability: AI Systems & Datasets
    Assess end-to-end lifecycle risks, including provisioning, usage, maintenance, and decommissioning.

  • AI Model – Outdated Information
    Applicability: AI Systems
    Evaluate whether training data is current, relevant, and fit for purpose.

  • AI Behavior – Scope
    Applicability: AI Systems
    Assess the risk of the AI providing responses outside its intended context or scope.

  • AI Model – Supporting Process
    Applicability: AI Systems
    Evaluate risks associated with model supportability, maintenance, and operational processes.

  • Operational Risk
    Applicability: AI Systems
    Assess the potential impact of the AI model on broader organizational operations.

  • AI Model – Explainability
    Applicability: AI Systems
    Ensure the model’s behavior, logic, and decision-making processes are documented and explainable.

  • AI Model Adaptability Risk
    Applicability: AI Systems
    Assess whether the model is reusable across scenarios or overly hyper-customized.

  • Risks of Human–AI Collaboration
    Applicability: AI Systems
    Identify risks arising from human–AI interaction, including miscalibrated trust, misunderstood outputs, workflow misalignment, and unclear accountability.

  • AI Model – Accuracy
    Applicability: AI Systems
    Assess the risk of the AI producing incorrect or unreliable outputs.

How Automated Risk Calculation Works

Run One or More Risk Assessments
Users can run multiple risk assessments for the same AI system or dataset by selecting different templates.

Individual Assessment Risk Scoring
Each completed assessment generates a risk score and corresponding risk level (e.g., Low, Moderate, High) based on responses for that specific template.

Rolled-up Risk View (Aggregated view)
RexCommand aggregates all assessment results for the entity and produces a rolled-up risk recommendation representing the overall risk posture.

  • Example: If two assessments result in Low and Moderate risk, the system will recommend an overall Moderate risk label for that AI system or dataset.

Manual Override (Optional)
The rolled-up risk is a recommendation only. Users can manually set or adjust the final risk level for the entity based on organizational judgment or additional context.

Notes

  • Each risk assessment retains its own score and audit trail.

  • Rolled-up risk reflects the highest or most critical risk identified across assessments.

  • Automated risk calculation supports both AI systems and datasets, depending on template applicability.

  • Manual risk entry is always available and not restricted by automated results.

  • This feature improves consistency while maintaining governance flexibility and accountability.

     

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