Cyberhaven partners with MIT Technology Review on data lineage for agentic AI
A sales representative asks an AI agent for customer pricing, and the agent pulls data from restricted pricing systems, internal communications, and archived quotes. The output looks correct to the agent, so a second agent drafts a proposal that unintentionally includes unapproved, confidential and sensitive information. Because no human checks either handoff, the organization cannot trace where…
Key points
- AI agents can combine data from multiple internal sources without human checks, creating untraceable proposals
- Without data lineage, firms cannot identify where confidential or sensitive data entered the workflow
- Cyberhaven and MIT Technology Review say enterprises need data‑lineage tools to secure agentic AI
The scenario illustrates a growing gap in AI governance: enterprises that embed agentic AI in daily workflows must manage moving, fragmented data that is continuously ingested, copied, summarized, transformed and redistributed. Traditional security approaches that focus only on applications, networks or endpoints are no longer sufficient. In a partnership with Cyberhaven, MIT Technology Review argues that establishing data lineage is essential to provide visibility, accountability and trust across human, agentic and hybrid workflows.
The piece was produced by MIT Technology Review’s custom content arm, Insights, and was researched and written by humans with limited AI assistance under human oversight. It calls on firms to adopt data‑lineage tools as a core component of securing agentic AI at scale.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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