Oracle-Driven Cybersecurity and Real-Time ERP Automation: Zero-Downtime BMS and the EDAS Method

Malek Bashir Al-Amin

doi:10.15662/IJRAI.2024.0705004

Authors

Malek Bashir Al-Amin Site Reliability Engineer, Libya Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0705004

Keywords:

Oracle ERP Cloud, business continuity, real-time detection, streaming ML, Oracle Data Safe, zero-trust, automated remediation, audit trails, adaptive authentication

Abstract

Enterprise Resource Planning (ERP) platforms underpin core business operations — finance, procurement, HR, and supply chain — and many organizations now run automated, always-on ERP processes in cloud environments. While automation increases speed and resilience, it also compresses the attack window and amplifies damage from cyber incidents, making business continuity dependent on both rapid detection and automated containment. This paper proposes an Oracle-driven framework that fuses zero-trust principles, Oracle Cloud telemetry and enforcement primitives (database activity monitoring, Data Safe, IAM/adaptive authentication), and streaming AI/ML detection to secure automated ERP operations and preserve business continuity. The framework ingests audit trails, database activity streams, API gateway logs, and identity events into a real-time feature pipeline where sequence-aware and ensemble detectors score activity on sliding windows. High-confidence anomalies trigger policy-driven automated playbooks: graded actions range from soft quarantine and adaptive multi-factor authentication challenges to temporary account suspension and automated workflow rollbacks, with human-in-the-loop gating for high-impact financial operations. The prototype, implemented on an Oracle testbed simulating procure-to-pay and payroll workflows, shows substantial reductions in time-to-detect and time-to-respond compared to baseline static rule engines while preserving auditable evidence and compliance mappings. Key operational challenges identified include model explainability, latency/compute overhead, data-privacy constraints on centralized modeling, and Oracle licensing/cost tradeoffs. The paper therefore recommends a phased, risk-prioritized deployment (start high-value workflows), feature masking for privacy, explainability layers for operator trust, and governance controls that map automated actions to audit evidence. By tightly integrating Oracle native controls and streaming AI detection with policy orchestration, organizations can automate ERP workflows safely and maintain business continuity under fast-moving cyber threats.

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