以數據溯源保障採用 AI 智能體的企業安全
Securing the agentic enterprise with data lineage
企業要大規模保障 AI 智能體安全，須掌握數據在系統、檔案及智能體之間的流動，並建立數據溯源。傳統只針對應用程式、系統、網絡或端點的安全措施已不足夠；數據溯源有助管理人類、智能體及混合工作流程中的管治、問責和信任。
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.
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.
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.
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.
來源：MIT Technology Review · AI · technologyreview.com