A security team is implementing responsible AI governance controls for a financial services organization deploying predictive AI models. The governance framework requires a human review process for all AI-driven decisions that exceed a certain financial impact threshold. Which Azure capability best supports implementing this human-in-the-loop control?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you need a workflow that can catch high-impact AI outputs and send them to a human reviewer. Azure Logic Apps does that by triggering on score thresholds and routing the decision for review before it’s finalized.
Full Explanation
The correct capability is an Azure Logic Apps workflow triggered by AI model output score thresholds because implementing human-in-the-loop review for high-impact AI decisions requires a workflow orchestration solution. Azure Logic Apps can be triggered by AI model outputs that exceed defined thresholds, automatically routing the decision to a human reviewer before finalizing the action. The Azure Machine Learning model monitoring option with data drift detection is incorrect because it identifies when input data distribution changes, triggering retraining—not human review of individual high-impact decisions. The Azure AI Foundry evaluation flows with human feedback integration option is incorrect because evaluation flows assess model performance during development, not production decision routing. The Azure Machine Learning Responsible AI dashboard with fairness analysis option is incorrect because it provides fairness and interpretability analysis during model development, not production-time human review routing.