Securing the agentic enterprise with data lineage

SponsoredIn partnership withCyberhaven A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff. When it’s discovered that the proposal includes unapproved, confidential, and sensitive information, no one can trace where the problematic data entered the workflow or how it spread across systems, files, and agents. DOWNLOAD THE REPORT

由 Cyberhaven 赞助。一位销售代表向 AI 智能体询问客户定价，智能体从多个来源提取数据：受限的定价系统、内部通讯和存档的报价。对于智能体而言，输出结果看起来很准确，因此第二个智能体使用这些数据为销售代表起草提案。没有任何人工检查这两个交接环节。当发现提案中包含未经批准的、机密且敏感的信息时，没有人能追踪到这些有问题的数据是在哪里进入工作流的，或者它们是如何在系统、文件和智能体之间传播的。下载报告。

This scenario illustrates why AI governance needs to evolve, rapidly and dynamically. As more enterprises begin to use agentic AI in daily workflows, they’re realizing that they must manage more than static files; they must manage moving and fragmented data that is continuously ingested, copied, summarized, transformed, reused, and redistributed in new forms. In short, AI security isn’t a tool problem; it’s a data visibility problem.

这种情况说明了为什么 AI 治理需要快速且动态地演进。随着越来越多的企业开始在日常工作流中使用智能体 AI，他们意识到自己必须管理的不仅仅是静态文件；他们必须管理不断被摄取、复制、汇总、转换、重用并以新形式重新分发的移动且碎片化的数据。简而言之，AI 安全不是一个工具问题，而是一个数据可见性问题。

Consequently, traditional security approaches built solely around applications, systems, networks, or endpoints are no longer sufficient. Securing agentic AI at scale requires understanding how data moves. Teams, therefore, need to establish data lineage in order to effectively manage governance, accountability, and trust across human, agentic, and hybrid workflows. Download the report

因此，仅围绕应用程序、系统、网络或端点构建的传统安全方法已不再足够。大规模保护智能体 AI 需要了解数据的移动方式。因此，团队需要建立数据血缘关系，以便在人类、智能体和混合工作流中有效地管理治理、问责制和信任。下载报告。

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

此内容由《麻省理工科技评论》的定制内容部门 Insights 制作，而非其编辑团队。它由人类研究并撰写，所使用的任何 AI 工具仅限于在人类监督下的生产流程中。

Deep DiveArtificial intelligenceDon’t be fooled—LLMs don’t reasonTen years after AlphaGo’s match against Go champion Lee Sedol, today’s AI still isn’t tapping into the machinery that made that win possible. By Thore Graepelarchive page

深度解析：人工智能。别被骗了——大语言模型不会推理。在 AlphaGo 与围棋冠军李世石对弈十年后，今天的 AI 仍然没有利用到使那次胜利成为可能的机制。作者：Thore Graepel，存档页面。

“We were looking at it wrong”: The explosive claim that’s upended the AI worldAn exclusive interview with Nick Cammarata, the researcher whose 26,000-word viral blog post has AI safety folks celebrating. By Will Douglas Heavenarchive page

“我们看错了方向”：颠覆 AI 界的爆炸性主张。对研究员 Nick Cammarata 的独家专访，他那篇 26,000 字的病毒式博客文章让 AI 安全领域的人士欢呼雀跃。作者：Will Douglas Heaven，存档页面。

Don’t be fooled by this summer of AI hype Breathless claims about AGI and new capabilities fall apart pretty quickly under scrutiny. By Timnit Gebruarchive pageEmily M. Benderarchive page

别被这个 AI 炒作之夏所迷惑。关于通用人工智能（AGI）和新能力的夸大宣传在审查下很快就会破灭。作者：Timnit Gebru，存档页面；Emily M. Bender，存档页面。

The US spent billions on border surveillance. Why can’t it catch people before they die?Surveillance towers, increasingly powered by AI, promised faster responses to crossings. Our investigation of deaths near the towers found they are failing to deliver. By James O’Donnellarchive pageEileen Guoarchive page

美国在边境监控上花费了数十亿美元。为什么它不能在人们死亡前拦截他们？日益由 AI 驱动的监控塔承诺对越境行为做出更快的反应。我们对监控塔附近死亡事件的调查发现，它们未能实现这一目标。作者：James O’Donnell，存档页面；Eileen Guo，存档页面。

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