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02/08/2026 11:33 am
TL;DR: Employees are submitting sensitive data into AI tools at scale, with Cyberhaven Labs finding that 39.7% of the data shared with AI tools is sensitive, exposing gaps in legacy DLP that was built for email, USB, and file transfer channels. The real issue is governance of data flow, context, and AI tool inventory, not just content inspection.
NHIMG editorial — based on content published by Cyberhaven: DLP for GenAI: How to Prevent Sensitive Data Leaks in AI Tools
By the numbers:
- 39.7% of the data employees share with AI tools is sensitive.
Questions worth separating out
Q: How should organisations control sensitive data in GenAI tools?
A: Organisations should treat prompts, uploads, and model outputs as governed data flows, then apply classification, inspection, and logging at the point of use.
Q: Why do AI prompts create more data loss risk than traditional file transfers?
A: AI prompts often copy data out of its original container without creating a classic file-transfer event, so perimeter DLP misses the move.
Q: What do organisations get wrong about DLP for AI use cases?
A: They assume keyword matching can distinguish legitimate work from sensitive exfiltration.
Practitioner guidance
- Implement lineage-aware DLP Track the origin of sensitive content so copied or pasted text inherits the classification of the source document before it reaches a GenAI tool.
- Inventory approved AI tools and agent workflows Maintain a continuously updated inventory of AI assistants, browser features, and agentic workflows, including how each tool stores, trains on, or forwards submitted data.
- Bind AI access to scoped identities Restrict which users, service accounts, and AI agents may submit sensitive data, then enforce policy by role, context, and destination.
What's in the full article
Cyberhaven's full post covers the operational detail this post intentionally leaves for the source:
- Endpoint workflow examples showing how copy, paste, and browser submission are observed in practice.
- Policy tuning guidance for allowing general AI use while restricting sensitive data classes.
- Inventory and classification detail for AI tools that train on submitted content or retain conversation history.
- Linea AI workflow coverage for agentic pipelines and downstream output monitoring.
👉 Read Cyberhaven's analysis of DLP for GenAI and sensitive data exposure →
DLP for GenAI and the governance gap legacy controls miss?
Explore further
02/08/2026 12:48 pm
AI data leakage is now an identity governance problem, not just a content filtering problem. Once employees and AI agents can move sensitive information through browser prompts, file uploads, and delegated workflows, the question becomes who is allowed to submit what data to which tool under which policy. Legacy DLP answers only part of that question because it was built for channels, not for identity-mediated data flows. Practitioners need governance that follows the user, the agent, and the data together.
A question worth separating out:
Q: When should institutions treat AI agents as identities rather than tools?
A: Institutions should treat AI agents as identities when the agent can authenticate, call APIs, move data, or take action without a person supervising each step. At that point, the agent affects access decisions and must be governed with the same ownership, logging, and revocation discipline as other non-human identities.
👉 Read our full editorial: DLP for GenAI exposes the limits of legacy data controls
