MIT: Securing AI with Data Lineage
An MIT Technology Review Insights report, produced with Cyberhaven. Why data lineage is the new security perimeter for agentic AI, and a five-area roadmap to build it.
Why agentic AI turns data visibility into a trust issue
How provenance and context make AI outputs trustworthy
How to turn visibility into risk-based control
A five-area roadmap for lineage-first security
Trusted by security teams at
What the data shows
Four Numbers Behind the Gap Between AI Adoption and AI Governance Visibility
63%
Breaches without AI governance
of breached organizations lacked AI governance policies (IBM, via MIT Technology Review Insights, 2026).
20%
Breaches linked to shadow AI
of organizations experienced breaches linked to shadow AI (IBM, via MIT Technology Review Insights, 2026).
39.7%
Sensitive data in AI interactions
of all AI tool interactions involve sensitive data (Cyberhaven Labs, 2026 AI Adoption & Risk Report).
82%
Top GenAI tools rated risky
of the top 100 GenAI SaaS tools are rated medium, high, or critical risk (Cyberhaven Labs, 2026 AI Adoption & Risk Report).
"Data lineage is the memory of your data. Without that memory, AI has intelligence but no accountability or trust."