Topic starter
02/08/2026 11:38 am
TL;DR: Governance fails when AI agents can chain tool calls across enterprise systems without continuous oversight, according to Cyberhaven. Runtime lineage and discovery become necessary to reconstruct agent actions and limit the blast radius.
Editorial analysis by NHI Mgmt Group, based on content published by Cyberhaven: “Agentic AI Security: Visibility and Control for AI Agents at Work”.
Key questions
Q: Why do agentic AI workflows create more risk than browser-based chat prompts?
A: Browser prompts are discrete interactions, but agentic workflows can chain tool calls, access data sources, and trigger follow-on actions.
Q: How do security teams know if agent observability is actually working?
A: Observability is working only when teams can tie together token activity, tool calls, and latency for a specific agent session.
Q: What breaks when AI agents are given broad inherited permissions?
A: Broad inherited permissions break the assumption that access is tied to a narrow business need.
Practitioner guidance
- Define agent-specific identity scope Separate AI agent permissions from the human user’s full access wherever possible, and document which data sources, APIs, and SaaS systems each agent may touch.
- Inventory unmanaged agents and connectors Continuously discover local agents, IDE assistants, MCP servers, and unsanctioned connectors so authorisation decisions are based on current inventory, not assumed approvals.
- Correlate agent actions into a single execution record Track prompt context, tool calls, data access, and downstream writes as one chain so security teams can judge whether an agent’s behaviour crossed a boundary.
Bottom line: AI agents inherit user permissions and can act across multiple enterprise systems without the continuous oversight that existing IAM controls assume.
Glossary terms: Agentic AI Security, Inherited Permissions, Data Lineage, Shadow Agent
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This topic was modified 3 days ago by NHI Mgmt Group
02/08/2026 12:51 pm
Agentic AI exposes an inherited-access model that enterprise IAM was never designed to govern: the article makes clear that agents often act with the permissions of the deploying user, not with a purpose-built identity and scope. That sounds convenient until the delegated software actor starts reading, writing, and combining data across tools at machine speed. The governance problem is not merely broader access, but access exercised by a non-human actor that does not behave like the person it represents. Practitioners need to treat delegated agent authority as a first-class identity domain.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern shadow agents and unsanctioned MCP connections?
A: Treat them as identity and authorisation problems, not just software sprawl. Organisations need continuous discovery, ownership, and approval boundaries for any agent or connector that can reach corporate data or invoke enterprise actions.
👉 Read our full editorial: Enterprise IAM needs runtime visibility for AI agent actions
This post was modified 3 days ago by NHI Mgmt Group