GenAI-Enabled Neural Network–Driven Real-Time LDDR Optimization on Secure Apache–SAP HANA Cloud Infrastructure for Risk and Clinical AI

Abstract

This paper presents an integrated, production-ready architecture and evaluation of a GenAI-enabled neural network framework for real-time Low-Latency Data Distribution and Routing (LDDR) optimization deployed on a secure, hybrid Apache–SAP HANA cloud infrastructure tailored for risk analytics and clinical AI workloads. Modern clinical and risk-management applications require millisecond-scale data routing and inference across heterogeneous data sources — electronic health records (EHR), streaming telemetry from medical devices, real-time market feeds, and privacy-sensitive patient registries. LDDR is the class of systems and algorithms that minimize end-to-end latency while maintaining reliability, policy-aware routing, and regulatory compliance. We propose a hybrid approach that combines (1) a lightweight GenAI orchestration layer that performs contextual routing decisions and dynamic policy synthesis, (2) a family of neural-network-driven LDDR models that learn optimal routing and replication policies under variable load and failure patterns, and (3) an underlying secure data plane built on Apache components (Kafka, Flink) integrated with SAP HANA for in-memory transactional/analytical processing and strong data governance. The GenAI agent acts as an adaptive planner that translates high-level clinical or regulatory intents into routing constraints and objectives, while the neural LDDR core maps network/compute observables to actions that minimize latency and maximize utility (e.g., inference freshness, fairness across patient cohorts, or risk-exposure reduction).

We detail model choices — lightweight convolutional-recurrent hybrid networks with attention mechanisms for time-series and topological features, reinforcement-learned policy networks for routing decisions, and continual-learning techniques to adapt to distribution shift without violating auditability requirements. The secure infrastructure uses encrypted channels, role-based access control, and SAP HANA’s in-memory tables for fast stateful lookups; Apache components handle stream buffering, backpressure, and exactly-once semantics where needed. We describe an end-to-end training and validation pipeline that uses a combination of synthetic stress traces, anonymized clinical datasets, and replayed production telemetry to produce models that operate within strict latency and privacy constraints.

 In evaluation across representative clinical-AI tasks (real-time sepsis risk scoring, cardiology monitoring alarms) and financial-risk simulations (intraday liquidity and counterparty-risk monitoring), our system reduced median routing+inference latency by 34–56% compared to baseline static routing and rule-based orchestration, while improving the freshness of inference results (staleness window reduction 22–48%). In scenarios requiring regulatory constraints (GDPR-like data residency, HIPAA-style access controls), the GenAI orchestration achieved policy compliance conversion with >98% accuracy of intent-to-constraint translation and maintained end-to-end audit trails. We analyze failure modes, including distributional drift, mis-specified high-level intents, and catastrophic network partitioning, and present mitigation strategies: uncertainty-aware routing, shadow training, and rollback-safe model updates.

 Finally, we discuss deployment considerations for the combined Apache–SAP HANA stack: cost-quality trade-offs, observability needs, and recommended SLOs/SLA enforcement techniques. The contribution is a practical blueprint and experimental validation for deploying GenAI-driven LDDR systems in highly regulated, latency-sensitive domains where both performance and governance are paramount.