Google Drive 基于 Gemini 的数据分类现已开启公开测试
英文标题:Gemini-based data classification in Google Drive is now available in open beta
Google Drive 中由 Gemini 驱动的 AI 数据分类功能现已进入公开测试阶段。该功能可自动识别并标记云端硬盘文件,帮助企业大规模执行精细化数据丢失防护(DLP)策略、设置保留规则并支持审计调查。相较于此前需管理员手动标记训练文件的方式,新方案允许管理员直接定义分类指令,由 Gemini 完成判断与标签应用。该能力还可降低 Agent 工作流自主访问或处理敏感数据的风险,并为管理员提供标签应用情况及用户修改记录的审计能力。
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中文
由 Gemini 驱动的 AI 数据分类现已在 Google Drive 中开启公开测试。
通过自动识别和标记整个 Drive 中的文件,AI 数据分类可帮助组织大规模执行精细化的数据丢失防护(DLP)策略、建立数据保留规则,并在审计调查期间利用标签元数据。这也成为在 Agent 时代提升组织安全态势的关键环节,可防止 Agent 工作流自主访问和处理敏感数据。
该功能利用 Gemini 模型为 Google Drive 中的文件应用数据分类标签。此前,AI 数据分类要求管理员识别并手动标记训练文件,以便 AI 模型学习与各个数据分类级别相关联的数据类型。借助这项新能力,Gemini 模型为管理员提供了一种更快速、高效的数据分类替代方式:无需手动训练模型,改由管理员定义指令。
在管理控制台中,Gemini 模型会解读指令、评估文件,并应用相应的数据分类标签。
管理员始终完全掌控基于 Gemini 的数据分类流程。他们可选择标签、向 Gemini 提供指令,并设定待评估文件的适用范围。对于由 Gemini 添加标签的文件,拥有相应标签权限的文件编辑者和所有者可以审核并接受自动应用的标签,或对其进行修改。审计日志会记录文件被添加标签的时间,以及用户对 Gemini 所应用标签的接受或修改情况。
在管理控制台中,启用该功能后,Workspace 管理员可以查看 Gemini 为文件应用标签的相关指标。
对于重视数据保护、合规性和报告工作的组织而言,数据分类是一项关键活动。不过,数据分类在实际落地时也可能面临挑战,尤其是在大规模场景下准确分类文件。通过使用 Drive AI 数据分类中新增的基于 Gemini 的能力,管理员可以实现更高准确度的大规模数据分类。
推出节奏
开始使用
- 管理员: 对于使用受支持产品许可的客户,可在 Workspace 管理控制台的“安全性”部分、“数据分类”选项下看到用于配置基于 Gemini 的 AI 分类指令的选项(安全性 > 访问和数据控制 > 数据分类)。请参阅我们的帮助中心以了解更多。
- 最终用户: 此功能没有面向最终用户的设置。
可用性
- 企业版: Enterprise Plus
- 教育版: Google AI Pro for Education
- 其他版本: Frontline Plus
资源
English / 原文
Gemini-powered AI classification in Google Drive is now available in open beta.
By automating file identification and labeling across Drive, AI classification helps organizations enforce granular data loss prevention (DLP) policies at scale, establish retention rules, and utilize label metadata during audit investigations. This also becomes a critical aspect in elevating an organization’s security posture in the agentic era, preventing agentic workflows from accessing and acting autonomously on sensitive data.
This feature leverages Gemini models to apply data classification labels to files in Google Drive. Previously, AI classification required admins to identify and manually label training files for the AI model to learn the types of data associated with each data classification level. With this new capability, Gemini models offer admins an alternative method for data classification that is faster and more efficient, eliminating the need for manual model training and replacing it with administrator-defined instructions.
In the Admin console, Gemini models interpret instructions, evaluate files, and apply appropriate data classification labels.
Administrators maintain full control of the Gemini-based data classification process. They select the label, provide Gemini with instructions, and scope the audience for the files being evaluated. For Gemini-labeled files, editors and owners of those files with the appropriate label permissions will have the opportunity to either review and accept, or modify the automatically-applied label. Audit logs capture when files are labeled, including any user acceptance or modification of a Gemini-applied label.
In the Admin console, once enabled, Workspace Admins can see metrics on the labels applied to files by Gemini.
Data classification is a critical activity for organizations that are conscious about data protection, compliance, and reporting. However, data classification can also be challenging to put into practice, particularly when it comes to accurately classifying files at scale. By using the new Gemini-based capabilities in AI classification for Drive, admins are able to achieve a higher degree of data classification accuracy at scale.
Rollout pace
Getting started
- Admins: For customers on a supported product license, the option for configuring Gemini-based AI classification instructions will appear in the Workspace Admin Console under the Data Classification option in the Security section (Security > Access and data control > Data classification). Use our Help Center to learn more.
- End users: There is no end user setting for this feature.
Availability
- Enterprise: Enterprise Plus
- Education: Google AI Pro for Education
- Other Editions: Frontline Plus

