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02/08/2026 9:47 am
TL;DR: AI agents act at machine speed, inherit broad user permissions, and move sensitive data across tools in ways legacy DLP and IAM were not built to observe, according to Cyberhaven. The governing assumption that access can be reviewed after the fact breaks when the actor can execute, propagate context, and create exposure before any review cycle catches up.
NHIMG editorial — based on content published by Cyberhaven: How to Secure AI Agents in the Enterprise: A Practical Guide for CISOs
Questions worth separating out
Q: How should security teams manage permissions for AI agents?
A: Security teams should regularly assess and update the permissions granted to AI agents to ensure they align with their intended scope.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What breaks when security teams rely only on DSPM for AI agent governance?
A: DSPM shows where sensitive data exists, but it does not show how an agent moved that data, which tools it touched, or whether context was copied into a local store.
Practitioner guidance
- Map agent runtime identities separately from human users Inventory every AI agent, local model, and assistant process that can touch sensitive data, then record which human or service account sponsors it.
- Build lineage into your enforcement model Capture which data was touched, which tool was used, and the exact sequence of actions before you rely on policy decisions.
- Reassess inherited permissions for agent speed and scope Review whether access that is acceptable for humans becomes excessive when exercised by agents at machine speed across multiple resources.
What's in the full article
Cyberhaven's full blog post covers the operational detail this post intentionally leaves for the source:
- How its endpoint DLP and DSPM model connects data at rest with data in motion for AI agent workflows
- How data lineage is used to inventory agents and surface inherited access on endpoints
- How its Linea AI guardrails evaluate agent behaviour in context at the point of enforcement
- How the three-pillar model maps visibility, identity, and guardrails into one operational programme
👉 Read Cyberhaven's guide to securing AI agents in the enterprise →
AI agent guardrails and identity: are your controls keeping up?
Explore further
02/08/2026 12:39 pm
Data visibility is now an identity control, not just a DLP concern. In agentic environments, security cannot separate data governance from identity governance because the actor moving the data may be a non-human runtime system rather than a person. That means the control question is no longer only where data lives, but which identity, human or agent, moved it and through what chain of tools. Practitioners should treat lineage as the evidentiary layer that makes identity decisions defensible.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to the same report.
A question worth separating out:
Q: How do organisations reduce AI exposure without blocking useful access?
A: Organisations should reduce exposure by removing stale data, tightening access around high-risk combinations, and restricting AI to verified datasets instead of broad repositories. That approach lowers blast radius while preserving use cases. The goal is not to stop AI access, but to make access intentional, visible, and defensible.
👉 Read our full editorial: AI agent governance fails when data visibility lags runtime behavior
