安全风险管理不仅是成本中心，而是企业在AI浪潮中构建品牌声誉并赢得市场信任的价值引擎。 硬·AI 作者 | 硬 AI 编辑 | 硬 AI 将安全合规从被动的 "约束条件"转变为主动的"战略优势"，是AI企业在技术创新趋于同质化后的关键胜负 手。 这是安永大中华区网络安全与隐私保护咨询服务主管合伙人高轶峰，在今年世界人工智能大会（ WAIC） 期间向我们提出的核心论断。 他认为，安全已不再是单纯的运营成本，而是直接决定企业信任与市场估值的核心资产。 以下为本次对话的重点梳理： AI风险 早已 " 在身边 " 。 近期知名开源大模型工具 Ollama被曝出默认开放且无鉴权的端口漏洞，表明AI 风险已从实验室走向实际场景。此外，因算法黑箱与模型幻觉导致的风险隐蔽性强、责任归属难度高，企 业必须建立适应新特性的AI安全防护体系。 "以隐私换便利"不可取。 在 AI背景下，"以隐私换便利"这种观念带来的风险是不可逆的。AI能够通过碎片 化数据精准重建个人画像，推断用户尚未意识到的敏感信息，可能导致歧视性定价、精准诈骗等安全风 险。这类风险一旦爆发，其危害远超普通的信息泄露事件。 AI风险是个"新物种"， 传统方法难以应 ...

Open Source LLM Framework - A framework connects any LLM to any MCP server (open-source) [1] - The framework enables building custom MCP Agents without closed-source apps [1] - Compatible with Ollama, LangChain, etc [1] - Allows building 100% local MCP clients [1]

LLM & MCP Integration - A framework enables connecting any LLM to any MCP server [1] - The framework facilitates building custom MCP Agents without relying on closed-source applications [1] - It is compatible with tools like Ollama and LangChain [1] - The framework allows building 100% local MCP clients [1]

We have fine-tuned DeepSeek (distilled Llama).Now we can interact with it like any other model running on Ollama using:- The CLI- Ollama's Python package- Ollama's LlamaIndex integration, etc. https://t.co/bCNUqtLgaJ ...

编译 | Tina、核子可乐 2020 年，开源数据库 Supabase 刚成立时，CEO Paul Copplestone 或许难以预见，它将在 2025 年 站上"Vibe Coding"这一开发趋势的风口。 本周，Supabase 的发展已经迎来高光时刻：据《财富》杂志报道， Supabase 宣布完成 2 亿美元 D 轮融资，投后估值 20 亿美元。本轮由 Accel 领投，Coatue、Y Combinator、Craft Ventures 及老股 东 Felicis 参投。距离其上一轮 8000 万美元融资仅过去 7 个月，累计融资已达近 4 亿美元。 Supabase 的崛起也反映出开源数据库在 AI 应用时代的新定位。凭借"开箱即用"的数据库体验， Supabase 极大降低了 SQL 数据库在实际开发中的接入门槛，也因此成为 Lovable 等快速增长的 Vibe Coding 工具的首选后端。其 Slogan "Build in a weekend, scale to millions（周末搭建，支撑百 万级用户）"，精准切中新一代 AI 原生应用的需求。 Vibe Coding 流程通常 ...

Core Viewpoint - Trustworthy digital finance should possess characteristics such as model reliability, strong interpretability, and high security, while also clarifying the legal status, behavioral boundaries, and responsibilities of financial intelligent agents [2][12]. Group 1: Breakthroughs in AI Models - China's DeepSeek-V3 has received high praise in global AI model rankings, being compared favorably to GPT-4o, with training costs significantly lower at under $6 million compared to GPT-4o's $100 million [4]. - Innovations in algorithms, such as MLA multi-head potential attention mechanisms and MoE mixed expert architecture, are crucial for the future of AI development in China, particularly for financial institutions [4][5]. Group 2: Challenges in AI Technology - Security risks remain prominent, including unauthorized access to models, data theft, and malicious attacks that can compromise model integrity and stability [8]. - The phenomenon of "model hallucination" persists, with various models including Grok-3 and GPT-4 exhibiting certain levels of hallucination rates [9]. - Issues such as model bias, algorithmic resonance, and privacy breaches continue to pose challenges, complicating the interpretability of AI models [10]. Group 3: Digital Finance Innovation - The evolution of digital finance must balance security and efficiency, transitioning from mere usability to leading-edge capabilities [12][13]. - Trustworthiness in digital finance innovation is essential, requiring proactive measures to prevent AI pitfalls and ensure model reliability and interpretability [13]. Group 4: Pathways to Building Trustworthy Digital Finance - High reliability is critical, necessitating the implementation of advanced security measures, including firewalls and zero-trust architectures, to protect against malicious attacks [15]. - Interpretability is a key requirement, enabling the transformation of model behavior into understandable rules and utilizing visualization tools to clarify model processes [15]. - Legal frameworks must be established to define the status and responsibilities of financial intelligent agents, ensuring they operate within clear boundaries [16]. - Economic efficiency can be achieved by pre-training industry-level financial models and customizing enterprise-level applications, fostering collaboration between tech firms and financial institutions [16].