Topic starter
25/10/2026 11:00 am
TL;DR: AI agent security still lacks the identity and app-aware permissions layer that links each action to a real employee, Willow argues. Identity governance must bind agents to owned identities and narrow permissions or authorised activity will keep producing unauthorised outcomes.
Editorial analysis by NHI Mgmt Group, based on content published by Willow: “The 8 AI Agent Security Tools Enterprise Teams Should Evaluate in 2026”.
By the numbers:
- 48% of cybersecurity professionals expected agentic AI to become the number-one attack vector in 2026.
Key questions
Q: Why do embedded AI agents increase enterprise risk so quickly?
A: Embedded agents widen the trust boundary because they add natural-language input, delegated tool access, and a reasoning layer inside software that was previously static.
Q: How do security teams know whether an AI agent control stack is actually working?
A: Look for three things: every agent has a traceable identity, permissions are narrow enough to explain in operational terms, and actions can be audited end to end.
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 the agent control gap by category Separate discovery, runtime detection, prompt protection, tool-call governance, and identity governance before buying.
- Bind agents to accountable identities Require each agent to map to a traceable owner and a real identity relationship, so permissions can be revoked when the task, project, or employee role changes.
- Scope permissions at the action level Grant read, write, delete, and approve permissions separately inside the connected application instead of giving the agent broad application access.
Bottom line: AI agent risk is not just about detecting suspicious behaviour, but about controlling what an agent is allowed to do inside the systems it can reach.
Glossary terms: AI Agent Security Risk Review, Function-Level Permissions, Shadow AI, AI Agent Identity Governance
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:18 pm
Identity and access governance is now the decisive layer for AI agent risk. The market splits into categories, but the central practitioner question is not which detector is best. It is whether the organisation can bind agent identity to business identity and restrict action scope inside the tool itself. That is the only layer that converts an agent from a loosely supervised actor into a governed one. The practical conclusion is that visibility without permission scoping is incomplete control.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between app discovery and agent discovery in identity governance?
A: App discovery identifies software that users are accessing, including shadow IT and usage context such as last-used dates and associated accounts. Agent discovery identifies AI agents and the applications connected to them, so teams can assign ownership and evaluate operational risk. Both are inventory functions, but they expose different governance problems and require different controls.
👉 Read our full editorial: AI agent security tools expose a missing identity layer
This post was modified 7 days ago by NHI Mgmt Group