AI-Driven Privacy Engineering: Architectures for Protecting PII in Multi-Cloud and Federated Data Ecosystems
DOI:
https://doi.org/10.63282/3050-9246/ICRTCSIT-132Keywords:
AI-driven Privacy Engineering, Personally Identifiable Information (PII), Multi-Cloud Security, Federated Data Ecosystems, Privacy-Enhancing Technologies (PETs), Differential Privacy, Homomorphic Encryption, Zero-Trust Architecture, Federated Learning, Data Governance, Compliance Automation, Secure Multi-Party Computation, Privacy by DesignAbstract
The rapid increase of multi-cloud adoptions and federated data ecosystems has upended the way enterprises manage and protect personally identifiable information. Although such designs enable scalability, flexibility, and business cross-industry cooperation, they are also facing new challenges in the privacy and security area, because of diverse hardware characteristics among countries, data transfer across borders, and different definitions of privacy handling. The old school perimeter-based controls, which worked in static and siloed setups, are pretty useless when you're always distributed, and often AI-driven operations get into gear. Towards that end, this article presents the referred to unified AI-powered Privacy Engineering framework, which incorporates federated learning, differential privacy, zero trust philosophy, and automated governance into the design and operation of new generation “cloud native” systems.
The proposed framework will highlight 4 architectural layers: (i) Federated Data Integration - which secure collaboration is enabled without centralizing raw PII, (ii) Privacy-Enhancing Technologies (PETs) such as homomorphic encryption, secure enclave and differential privacy to maintain the confidentiality under distributed processing; (iii) AI-Assisted Governance and Compliance - a intelligent policy orchestration automates regulatory alignment with real-time data lineage tracking; and (iv) Zero-Trust Adaptive Security - that it necessitates continual verification and anomaly detection on multi-cloud environments. With the infusion of AI on every layer, the framework evolves from its original reactive compliance enforcement to proactive context-aware privacy management.
The empirical validation is executed through cases in healthcare and finance. In healthcare, federated oncology models showed that PII leakage was reduced by 38% with performance comparable to near-baseline, and compliance report time went down by 42%. That is, we negotiated a 25% latency reduction in detecting anomalies for a global bank’s fraud detection pipeline and a 40% improvement in cross-border audit readiness in the financial domain. Benchmark analysis also reveals that AI-enabled privacy engineering brings: 25% reduction in integration errors; 30-40% speedup to secure deployment; and 20% increase in throughput for federated workflows.
The findings demonstrate both the technical feasibility and the strategic necessity of AI-driven privacy engineering at a time when regulatory mandates, adversarial AI risks, and pressures from cross-border data sharing have converged. The results suggest that building privacy into the architecture of systems is now a necessity to maintain resilience, compliance, and trust in multi-cloud and federated ecosystems
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