The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and privacy challenges. When data traverses borders, it becomes subject to conflicting legal regimes, such as the EU's General Data Protection Regulation and China's Personal Information Protection Law, compounded by technical vulnerabilities like model memorization. Current static encryption and data localization methods are fragmented and reactive, failing to provide adequate, policy-aligned safeguards. This research proposes a Jurisdiction-Aware, Privacy-by-Design architecture that dynamically integrates localized encryption, adaptive differential privacy, and real-time compliance assertion via cryptographic proofs. Empirical validation in a multi-jurisdictional simulation demonstrates this architecture reduced unauthorized data exposure to below five percent and achieved zero compliance violations. These security gains were realized while maintaining model utility retention above ninety percent and limiting computational overhead. This establishes that proactive, integrated controls are feasible for secure and globally compliant AI deployment.

翻译：大型语言模型与物联网系统对大规模、全球分布式数据流的依赖，带来了系统性的安全与隐私挑战。当数据跨境流动时，其将面临相互冲突的法律制度（如欧盟《通用数据保护条例》与中国《个人信息保护法》）的约束，同时叠加模型记忆等技术漏洞。当前静态加密与数据本地化方法零散且被动，无法提供充分且与政策一致的保障。本研究提出一种“具备管辖权感知、设计即隐私”的架构，该架构通过密码学证明，动态整合本地化加密、自适应差分隐私与实时合规声明。在多司法管辖区的模拟环境中进行的实证验证表明，该架构将未授权数据泄露降至百分之五以下，并实现了零合规违规。这些安全增益是在保持模型效用留存率超过百分之九十并限制计算开销的同时实现的。这证明，主动、集成的控制措施对于安全且全球合规的人工智能部署是可行的。