Topic starter
09/06/2026 11:13 pm
TL;DR: AI agent discovery remains the most common gap security teams report as organisations build low-code and no-code agents across AWS Bedrock, Azure AI Foundry, Copilot Studio, Vertex AI, and other environments, according to Lasso Security. Without a continuously updated inventory, security teams cannot govern model choice, tool access, or runtime behaviour across separate platforms.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Discover Every AI Agent Your Organization Builds, Across Every Major Cloud Platform”.
Key questions
A: Security teams should evaluate whether the platform can inherit existing cloud controls without creating a parallel trust domain.
Q: Why does incomplete AI asset inventory create so much risk for AI security testing?
A: Incomplete inventory breaks the testing programme before it starts because you cannot validate systems you cannot see.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Map every agent source to a single inventory Connect managed cloud AI services, no-code builders, cloud environments and CI/CD repositories into one continuously updated discovery process so the organisation can see every agent in one place.
- Require an AI-BOM before approval Make model version, system prompt, tools, retrieval sources, guardrails and orchestration framework mandatory fields before an agent can move into production review.
- Trace delegation chains for every multi-agent flow Document which parent agents can pass permissions to sub-agents, APIs and databases so hidden privilege propagation can be reviewed against intended scope.
Bottom line: Cross-cloud AI agent discovery is now a governance requirement because agents are being created in multiple environments outside a single control plane.
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This topic was modified 2 days ago by NHI Mgmt Group
10/06/2026 1:30 am
Cross-cloud AI agent discovery is now an identity control, not just an inventory task. Once agents can be created in Bedrock, Azure AI Foundry, Copilot Studio, Vertex AI, or code repositories, the old assumption that security can govern from a single console breaks down. The estate becomes distributed before it becomes visible, which means governance starts from absence rather than control. Practitioners should treat discovery as the first identity boundary for agentic systems.
A few things that frame the scale:
- 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 separate finding from the same survey shows that only 44% of organisations have implemented any policies to manage their AI agents, even though 92% agree that governing them is critical to enterprise security.
A question worth separating out:
Q: How should organisations govern model selection for AI agents?
A: Organisations should treat model selection as a governance decision, not a local implementation detail. Approved models, restricted models, and external-facing models should be defined centrally so business teams cannot expand risk simply by choosing a different foundation model.
👉 Read our full editorial: Cross-cloud AI agent discovery is becoming an IAM control point
11/06/2026 12:35 am
Cross-cloud AI agent discovery is now an identity control, not just an inventory task. Once agents can be created in Bedrock, Azure AI Foundry, Copilot Studio, Vertex AI, or code repositories, the old assumption that security can govern from a single console breaks down. The estate becomes distributed before it becomes visible, which means governance starts from absence rather than control. Practitioners should treat discovery as the first identity boundary for agentic systems.
A few things that frame the scale:
- 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 separate finding from the same survey shows that only 44% of organisations have implemented any policies to manage their AI agents, even though 92% agree that governing them is critical to enterprise security.
A question worth separating out:
Q: How should organisations govern model selection for AI agents?
A: Organisations should treat model selection as a governance decision, not a local implementation detail. Approved models, restricted models, and external-facing models should be defined centrally so business teams cannot expand risk simply by choosing a different foundation model.
👉 Read our full editorial: Cross-cloud AI agent discovery is becoming an IAM control point
12/06/2026 2:08 am
Cross-cloud AI agent discovery is now an IAM dependency, not an inventory nice-to-have. The moment agents can be created in multiple clouds and no-code platforms, governance loses the ability to answer basic ownership and scope questions. That makes discovery the point where IAM, NHI and AI security intersect, because access cannot be governed for an identity that is not yet visible. Practitioners should treat undiscovered agents as unmanaged identities until proven otherwise.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What happens when multi-agent delegation is not mapped?
A: Permissions can propagate farther than intended, especially when parent agents, sub-agents, APIs and databases are connected through undocumented chains. The result is an expanded attack and misuse surface that policy cannot reliably constrain because no one can prove where authorisation begins or ends.
👉 Read our full editorial: Cross-cloud AI agent discovery is becoming an IAM control point
This post was modified 2 days ago by NHI Mgmt Group