Designing a Secure AI Framework for Distributed Oracle Cloud Database Systems: Comparative Insights into Risk-Based Incident Management and Governance in Web Applications
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Abstract
Cloud databases form the backbone of modern web and enterprise applications, enabling scalable, data-driven operations across industries. However, the increasing integration of Artificial Intelligence (AI) into these environments introduces a broadened risk landscape characterized by data sensitivity, model leakage, and dynamically evolving cyber threats. To address these challenges, this paper presents a Secure AI Framework specifically designed for Oracle Cloud Infrastructure (OCI) and Oracle Database environments, focusing on safeguarding AI-enabled data systems within web applications.The proposed framework adopts a Zero Trust architecture as its foundational security model, enforcing continuous verification, least-privilege access, and micro-segmentation to prevent lateral movement within AI-data ecosystems. Complementing this is a risk-based incident management module, which applies AI-driven analytics for proactive threat detection, prioritization, and response orchestration. A governance and compliance layer aligns the framework with globally recognized standards and best practices, including NIST 800-series, ISO 27001, CIS Benchmarks, and OWASP Application Security Guidelines, ensuring both regulatory adherence and operational accountability. The paper’s key contributions include a vendor-aware architectural model optimized for Oracle Cloud services, a risk-informed operational playbook for incident response, and a mapping matrix that bridges governance frameworks to practical security controls.
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