Topic starter
06/09/2026 6:20 pm
TL;DR: Most enterprises undercount active AI agents by 3 to 10 times and 80% of current deployments already show rogue behaviour, according to Trust3, while Wiz extends CNAPP coverage into AI models and agents. The real issue is that discovery and runtime authorization for agent identities cannot be treated as the same control problem as cloud posture correlation.
Editorial analysis by NHI Mgmt Group, based on content published by Trust3: “Trust3 AI vs. Wiz: Agent Security Control Plane or Cloud Platform”.
By the numbers:
- Most enterprises undercount active agents by 3 to 10 times, according to Trust3.
- According to a survey of 437 enterprise security leaders, most enterprises don’t have a complete inventory of their AI agents.
Key questions
Q: What breaks when AI agents are governed only through CNAPP visibility?
A: Visibility alone leaves a gap between knowing an agent exists and controlling what it can do at runtime.
Q: Why do AI agents create more cloud access risk than human users?
A: AI agents can chain API calls quickly, interact with multiple services in one session, and operate without the familiar human signals that security tools expect.
Q: How can security teams tell whether agent access is actually under control?
A: Look for evidence that the team can trace every tool call, secret use, and cross-system action back to a named owner and a valid approval path.
Practitioner guidance
- Map every production AI agent to an owner and purpose Build a current inventory of agents, including shadow agents in developer tools, copilots, and workflow systems.
- Separate cloud exposure review from agent authorisation Use CNAPP for environment-wide correlation, but add a distinct control for request-time approval of agent actions.
- Scope MCP and data-platform access to the current task Limit Snowflake, Databricks, and MCP access to the exact operation the agent is performing, then expire that access when the task ends.
Bottom line: AI agent security cannot be reduced to cloud posture, because the real governance problem is whether an agent can be authorised at the moment it acts.
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06/09/2026 6:21 pm
Agent security and CNAPP are solving adjacent but non-identical identity problems: CNAPP excels at correlating cloud risk, but it does not by itself answer who the agent is, what it is allowed to do right now, or whether that permission should exist for this specific action. Trust3's framing reflects a broader shift in identity governance: AI agents are now operational actors, not just workloads with cloud metadata. Practitioners should stop treating agent visibility as a substitute for agent authorisation.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: Should teams centralise AI agent security inside CNAPP or use a dedicated control plane?
A: Use both when agents are in production. CNAPP is better for broad environment context and attack-path correlation, while a dedicated control plane is needed when the question is whether an agent may perform a specific action right now. The decision usually turns on runtime enforcement, not just asset visibility.
👉 Read our full editorial: Trust3 AI and Wiz expose the split between agent security and CNAPP