Data lineage is emerging as the new security perimeter for enterprises adopting AI at scale. When a sales representative queries an AI agent for customer pricing, the agent pulls information from restricted pricing systems, internal communications, and archived quotes. The resulting output appears accurate, prompting a second agent to draft a proposal without human oversight. The hidden presence of unapproved, confidential data slips through, and the workflow offers no visibility into how it entered or spread.
This scenario underscores the need for AI governance that can keep pace with dynamic data flows. As organizations integrate agentic AI into daily operations, they must manage not just static files but moving, fragmented data that is continuously ingested, copied, summarized, transformed, reused, and redistributed. Traditional security models focused on applications, systems, or endpoints are insufficient. Effective governance, accountability, and trust require comprehensive visibility into data movement.
The solution is to establish robust data lineage practices that map data journeys across human, agentic, and hybrid workflows. By doing so, teams can trace problematic inputs, ensure compliance, and maintain trust. This content was produced by Insights, MIT Technology Review’s custom arm, and was overseen by editorial standards. Download the full report to learn how to secure your AI ecosystem.
Source: MIT Technology Review AI · Summarized by HeadlinesBriefing