A security architect needs to implement transparency controls for an AI system that makes automated credit decisions. Regulators require that the organization be able to explain to any affected individual why they received a specific credit decision from the AI system, in plain language. Which combination of Azure services supports meeting this regulatory transparency requirement?

A is correct because meeting the regulatory transparency requirement for individual credit decisions requires two components: (1) Technical explainability—Azure Machine Learning's Responsible AI dashboard with SHAP explainability generates quantitative feature importance values for each individual prediction, showing which factors (income, credit utilization, payment history) most influenced the decision. (2) Customer communication—the application layer translates the raw SHAP values into plain-language explanations (e.g., 'Your application was declined primarily because of a high debt-to-income ratio') suitable for communicating to affected individuals. Together, these meet the regulatory requirement for explainability. B is wrong because logging prediction inputs and outputs provides auditability (showing what was fed in and what came out) but does not explain why the model made its specific decision—logging alone does not satisfy explainability requirements. C is wrong because Purview sensitivity labels classify and protect data but do not generate explanations of model decisions. D is wrong because prompting a separate LLM (Azure OpenAI) to generate explanations is an unreliable approach that can hallucinate incorrect reasoning—SHAP-based explanations are grounded in the actual model's mathematical computation.