Cloud-Native Testing Frameworks for Explainable Generative AI in Healthcare and Financial Systems

Abstract

This paper presents a comprehensive software-testing framework tailored for explainable generative artificial intelligence (XGenAI) systems deployed in cloud-based credit risk and threat modeling environments. As XGenAI components (conditional VAEs, tabular GANs, and hybrid generative–discriminative pipelines) become central to decisioning and security analytics, they introduce new failure modes that traditional ML testing approaches do not fully cover: synthetic-data fidelity issues, latent-space counterfactual implausibility, explanation fidelity drift, and adversarial probing of model endpoints. We propose a layered testing architecture combining unit, integration, system, and continuous monitoring tests specifically designed for XGenAI artifacts and their surrounding infrastructure. The framework contains (1) deterministic test harnesses for data preprocessing and feature pipelines (schema contracts, statistical invariants, lineage checks); (2) generative model verification (distributional distance tests, membership inference risk scans, mode-collapse detection, DP-privacy assertions when applicable); (3) explainability validation (explanation fidelity metrics, consistency and stability tests across local and global explainers, counterfactual plausibility checks using generative manifold constraints); (4) safety and security evaluation (adversarial query simulation, model inversion resistance tests, API rate-limit and authentication tests, threat analytics stress tests); (5) performance and latency tests for streaming architectures (end-to-end latency SLAs, backpressure resilience for Kafka/Flink-like pipelines); and (6) governance and compliance checks (automated model cards, audit trails, reproducible experiment artifacts). We detail test design patterns, synthetic and real-world test data strategies, and instrumentation required for in-situ validation. Experimental application of the framework to an enterprise-grade XGenAI credit-scoring and threat-detection stack (streaming ingestion, HANA-like in-memory serving, and model explainability services) shows that systematic testing reduces silent failures, prevents common privacy leaks during synthetic augmentation, and improves explanation stability under temporal drift. The framework integrates with CI/CD pipelines, enabling automated canary testing, staged rollout validation, and rollback triggers — thereby operationalizing safe deployment of explainable generative systems in critical financial contexts.