The AiVRIC Fabric AI Model Inspection layer continuously monitors every AI model for safety, bias, and hallucination risk.
As organizations deploy more AI models, the attack surface for unsafe outputs, biased decisions, and hallucinated facts grows. The Fabric runs continuous, repeatable evaluations against every registered model and feeds the results directly into GRC workflows, compliance evidence packages, and executive dashboards — so AI governance is an ongoing practice, not a quarterly audit.
Safety Evaluations
Run repeatable red-team and safety test suites against registered models to detect unsafe outputs before they reach users.
Bias Detection
Evaluate models across demographic and domain dimensions to surface systematic bias in outputs and recommendations.
Hallucination Monitoring
Continuously measure hallucination rates in production against grounded truth datasets, with trend tracking and alerts.
Risk Scoring
Aggregate evaluation results into a model risk score that integrates directly into the GRC risk register and dashboard.
Connect Models
Register AI models — internal, third-party, or open-source — with the Fabric inspection pipeline via API or native integration.
Run Evaluations
Scheduled and on-demand evaluation suites execute against each model using standardized and custom test cases.
Score & Export
Results are scored, trended over time, and exported as governance-ready evidence mapped to AI policies and control frameworks.
Outcomes you can expect
- Produce AI governance evidence for SOC 2, ISO 42001, and NIST AI RMF audits automatically.
- Reduce hallucination-related incidents by detecting model drift before it impacts users.
- Give boards and executives a continuously updated AI risk score with supporting evidence.
Ready to see AI Model Inspection in action?
Join a live platform walkthrough and see the Fabric at work across your environment.
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