After enterprises introduce agentic AI, the security focus shifts to data lineage
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As enterprises bring agentic AI into daily workflows, the core issue of AI security has shifted from the tool level to data visibility. Agents continuously ingest, copy, summarize, transform, and redistribute fragmented data, and traditional security measures centered on applications, systems, networks, or endpoints are no longer sufficient to address this. The report making this assessment states that enterprises need to establish data lineage in order to achieve governance, accountability, and trust across human, agent, and hybrid workflows.
AI overview based on source reports · Updated 2 days ago
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- MIT Technology Review · AISecuring the agentic enterprise with data lineage
This MIT Technology Review Insights piece describes a scenario in which AI agents pull from restricted pricing systems, internal communications and archived quotes, and a second agent drafts a proposal without human checks, leaving no way to trace where confidential data entered the workflow. It argues that AI security is a data visibility problem, and that securing agentic AI at scale requires data lineage to manage governance, accountability and trust across human, agentic and hybrid workflows.
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