AI-Driven Software Ecosystem for Online Automated Applications: Integrating Oracle EBS, SAP, Cloud Computing, and Cyber-Physical Optimization with Secure Data Vaults and Firewall Intelligence

Abstract

Modern enterprises increasingly link enterprise resource planning platforms such as Oracle E-Business Suite (EBS) with cloud services and cyber-physical assets (network infrastructure, edge controllers, and power subsystems). This coupling creates opportunity for operational optimization—automated firewall intelligence, asset-aware lifecycle management, and cyber-physical energy efficiency—but also introduces risks around availability, privacy, and forensic readiness. We propose an interpretable AI-driven software ecosystem that unites three core capabilities: (1) application-aware firewall intelligence that synthesizes safe, explainable rule refinements from correlated EBS telemetry and network flows; (2) privacy-preserving, cloud-native model training and inference (federated and encrypted patterns) to enable cross-site learning without centralizing sensitive telemetry; and (3) secure data-vault based redundancy (immutable snapshots, air-gapped forensic copies, and policy-as-code retention) to guarantee auditability and rapid recovery.
The architecture is microservice-based: non-invasive EBS connectors normalize asset and workflow metadata; a unified feature store binds semantic EBS concepts (modules, integrations, maintenance records) to network and OT telemetry; an interpretable-model layer (rule lists, GAMs, small trees, and local explanation surfaces) powers SecOps and operations decisions; and a vault orchestration layer enforces retention, immutability, and staged recovery. Human-in-the-loop controls, canary enforcement, and simulation against historical traces are central to preventing disruption. Evaluation blends retrospective replay, synthetic fault/intrusion injection, shadow pilots, and staged production canaries with metrics for detection fidelity, rule-safety, recovery time objectives (RTO/RPO), and operator acceptance.
Key contributions are a practical engineering blueprint for safe, explainable automation in EBS-linked estates; design patterns for reconciling immutable forensic needs with deletion/consent obligations (tokenization and layered metadata); and an MLOps + policy governance stack that supports reproducible, auditable model updates. We discuss trade-offs—latency and cost of privacy mechanisms, governance overhead, and model utility vs interpretability—and provide an operational roadmap for incremental adoption that minimizes operational risk while delivering measurable security and operational benefits.