AI chatbots have quietly become the world's most popular therapists, coaches, and confidants. Users of cloud-based LLM services are increasingly shifting from simple queries like idea generation and poem writing, to deeply personal interactions. As Large Language Models increasingly assume the role of our confessors, we are witnessing a massive, unregulated transfer of sensitive personal identifiable information (PII) to powerful tech companies with opaque privacy practices. While the enterprise sector has made great strides in addressing data leakage concerns through sophisticated guardrails and PII redaction pipelines, these powerful tools have functionally remained inaccessible for the average user due to their technical complexity. This results in a dangerous trade off for individual users. In order to receive the therapeutic or productivity benefits of AI, users need to abandon any agency they might otherwise have over their data, often without a clear mental model of what is being shared, and how it might be used for advertising later on. This work addresses this interaction gap, applying the redaction pipelines of enterprise-grade redaction into an intuitive, first-of-its-kind, consumer-facing, and free experience. Specifically, this work introduces a scalable, browser-based intervention designed to help align user behavior with their privacy preferences during web-based AI interactions. Our system introduces two key mechanisms: local entity anonymization to prevent data leakage, and 'smokescreens': autonomous agent activity to disrupt third-party profiling. An open-source implementation is accessible at the following GitHub Repository: https://github.com/SBleeyouk/PII_Shield.git

翻译：人工智能聊天机器人已悄然成为全球最受欢迎的心理治疗师、教练与倾诉对象。基于云的大语言模型用户正从创意生成、诗歌创作等简单查询，逐步转向高度私密的交互场景。随着大语言模型日益扮演着"告解对象"的角色，我们正目睹海量敏感个人身份信息在缺乏监管的状态下流向隐私政策不透明的科技巨头。尽管企业界已通过精密的防护机制与PII脱敏管道在数据泄露防范领域取得显著进展，但这些强大工具因其技术复杂性始终未能惠及普通用户。这种现状迫使个体用户面临危险的两难抉择：若要享受人工智能带来的情感疗愈或效率提升，就必须放弃对数据的控制权——通常用户既不清楚共享了哪些信息，也不了解这些数据未来将如何被用于广告投放。本研究旨在填补上述交互鸿沟，将企业级脱敏管道转化为面向消费者的直观、免费且首创的用户体验。具体而言，我们提出一种可扩展的浏览器侧干预方案，旨在帮助用户在基于网页的人工智能交互中实现行为与隐私偏好的对齐。该系统引入两大核心机制：本地实体匿名化以防止数据泄露，以及"烟幕弹"技术——通过自主代理活动干扰第三方用户画像构建。开源实现已发布于以下GitHub仓库：https://github.com/SBleeyouk/PII_Shield.git