Design of a Secure AI-Based Framework for Zero-Touch Cloud based Distributed Workforce Management and Digital Privacy in Oracle Database Ecosystems
DOI:
https://doi.org/10.15662/IJRAI.2024.0705006Keywords:
Zero-touch automation, Oracle databases, workforce management, federated learning, differential privacy, natural language processing, runbook automation, identity & access management, policy-driven orchestration, explainable AIAbstract
Enterprises operating large Oracle database estates face mounting operational complexity: continuous provisioning, patching, schema changes, user-access requests, performance incidents, and compliance tasks. These activities consume substantial DBA and cloud-ops effort and create risk when manual processes are error-prone. We propose a secure, AI-based framework that delivers zero-touch workforce management for Oracle database ecosystems while enforcing strong digital-privacy guarantees. The framework blends intelligent discovery, role-aware automation, privacy-preserving learning, and policy-driven orchestration to automate routine operational workflows (access lifecycle, patch scheduling, workload placement, incident remediation) with auditable human-in-loop controls for high-risk actions. At the core is a machine intelligence layer that learns operator intent and runbook patterns from historical telemetry, change histories, and natural language artifacts (tickets, runbooks, chat logs). Privacy is preserved by applying on-site preprocessing (PII/PHI scrubbing), federated learning for cross-site model improvement, and differential-privacy mechanisms on shared artifacts; cryptographic protections (secure aggregation, encrypted logs) protect audit trails and model updates in transit. A zero-touch orchestration plane maps probabilistic recommendations into staged automation actions using a policy engine that encodes safety envelopes, compliance rules, and multi-party approval flows. The framework includes role-based access and consent controls integrated with Oracle IAM and cloud identity providers, immutable audit logging, and explainability modules that surface decision rationales to DBAs and compliance officers. We present a detailed methodology (data sources, model design, privacy stack, policy engine, orchestration workflows), a staged evaluation plan (simulation, controlled pilots), and metrics (automation coverage, time saved, incident regression rate, privacy leakage bounds, override rate). We discuss trade-offs — stricter privacy reduces model utility; aggressive automation increases rollback risk — and prescribe governance practices (policy lifecycle, safety gates, operator training). The proposed architecture aims to reduce operational toil, improve response speed, and preserve regulatory and privacy requirements for enterprises modernizing Oracle database operations.
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