Integrating Interpretability and Cloud Intelligence in Oracle EBS: A Framework for Secure, Privacy-Aware Machine Learning in Software Ecosystems

Abstract

Industrial control and enterprise application domains are converging: modern facilities increasingly coordinate on-cloud enterprise resource planning (ERP) systems such as Oracle E-Business Suite (EBS) with edge controllers that manage power electronics (e.g., DC–DC converters) for data centers, medical devices, and critical infrastructure. This paper proposes a software-ecosystem framework that unifies interpretable machine learning, cloud intelligence, and privacy-aware data engineering to enable secure, auditable control of DC–DC converters while preserving enterprise governance and user privacy. The framework integrates (1) non-invasive EBS telemetry and asset/inventory metadata to inform control policies and maintenance schedules; (2) edge telemetry and control hooks for DC–DC converters with secure communication channels and fail-safe defaults; (3) privacy-preserving ML (federated learning, differential privacy, and encrypted inference) to train predictive maintenance and control models without centralizing sensitive operational or patient-related data; and (4) interpretability layers (rule-extraction, local explanations, model cards) so SecOps, engineers, and auditors can understand recommendations and synthesized control adjustments.

Architecturally, the system is microservice-based: data ingestion and normalization, a versioned model registry supporting encrypted model artifacts, an explainability service that exposes human-friendly rationales, and a policy-as-code enforcement plane that translates business and safety constraints into verifiable rules for both cloud and edge. Safety is emphasized through conservative control envelopes, human-in-the-loop approval for any closed-loop actuation, and layered redundancy (edge fallback controllers, immutable audit logs, and staged rollback). We evaluate the framework via (a) retrospective replay and synthetic fault injection on anonymized EBS and converter telemetry, (b) privacy and attack-surface analyses for encrypted and federated workflows, and (c) a shadow-mode pilot linking EBS maintenance workflows to edge control suggestions.

 Results indicate that privacy-preserving training achieves near-centralized performance on predictive tasks with substantially reduced data movement; interpretable outputs materially improve operator trust and decrease mean time to remediate (MTTR) in simulated incidents; and policy-as-code enforcement prevents unsafe automated actuation. We discuss trade-offs (latency for encrypted inference, governance complexity, and lifecycle of model interpretability artifacts) and provide a practical roadmap for staged adoption in regulated environments.