AI-Driven Cloud Migration Framework for Distributed Oracle Databases: A Privacy-Preserving and Zero-Touch Automation Approach

Abstract

Migrating enterprise Oracle databases to the cloud remains a high-value but high-risk activity: it delivers scalability, elasticity, and operational efficiency, yet exposes sensitive data, requires complex application and schema transformations, and often demands lengthy, costly manual orchestration. This paper proposes an AI-driven, privacy-preserving, zero-touch automation framework for Oracle database cloud migration that reduces manual effort, preserves data confidentiality, and accelerates safe cutover. The framework combines (1) an intelligent discovery and dependency-mapping layer that uses static analysis, runtime telemetry, and natural language signals from runbooks to build a rich dependency graph; (2) an ML-based transformation planner that predicts schema and code change patterns, recommends data-motion strategies (online replication, logical replication, or bulk transfer), and estimates resource needs and risk; (3) a privacy stack that integrates data classification, automated redaction/de-identification, policy-driven tokenization, and differential-privacy-aware analytics to limit exposure during testing and model training; (4) a secure orchestration plane that implements zero-touch workflows (infrastructure provisioning, schema migration, data sync, application cutover, rollback) via infrastructure-as-code, policy engines, and automated verification gates; and (5) a human-in-the-loop governance console for audit, manual checkpoints, and safety overrides. We describe algorithms for dependency inference, a training regimen for migration outcome prediction, and techniques for generating synthetic or privacy-preserving test datasets. An evaluation plan (retrospective replay on historical migrations, controlled pilot migrations, and privacy leakage assessment) is presented along with key metrics (migration time, downtime, data fidelity, number of manual interventions, privacy leakage bounds). We discuss trade-offs between automation aggressiveness and safety, and show how staged zero-touch adoption (assist → advise → automate) preserves operational continuity. The proposed architecture aims to shorten migration cycles, reduce human error, and protect sensitive data while providing traceable, auditable automation for enterprise Oracle migrations.