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23/06/2026 7:25 pm
TL;DR: Agentic AI governance starts with discovery, because organisations cannot authorise or classify agents they cannot see, according to PlainID’s feature focus series. In multi-cloud environments, disconnected agent registries create blind spots that let new agents connect to internal systems before security teams know they exist, making visibility and continuous registration the first control problem.
NHIMG editorial — based on content published by PlainID: Agentic AI Observability
Questions worth separating out
Q: How should security teams govern AI agents across multiple cloud platforms?
A: They should start with a single governed inventory that discovers agents, gateways, and connected systems across every platform.
Q: Why does agent discovery matter before access control in AI governance?
A: Because access control cannot reliably evaluate identities it cannot see.
Q: What breaks when AI agent metadata is not maintained continuously?
A: Policy drift breaks first.
Practitioner guidance
- Define a governed agent inventory Establish one authoritative registry for discovered agents, gateways, and targets so security and IAM teams are not depending on separate platform views.
- Attach policy-ready metadata to every agent Standardise the attributes that matter for authorization, such as line of business, platform source, and system relationships, and require them to be populated before access is granted.
- Automate discovery refreshes across connected platforms Use scheduled or event-driven scans so new agents and changed connections are pulled back into the registry quickly enough to keep policy current.
What's in the full article
PlainID's full feature focus series covers the operational detail this post intentionally leaves for the source:
- How PlainID connects to AWS AgentCore and Microsoft Foundry to populate a unified agent registry
- How custom agent attributes become live policy conditions for access decisions
- How scheduled discovery updates keep new agents and changed platform metadata current
- How the unified access graph ties agents, gateways, targets, policies, and asset relations together
👉 Read PlainID’s analysis of agentic AI observability and discovery →
Agentic AI discovery gaps: is your authorization layer seeing new agents?
Explore further
25/06/2026 3:25 am
Discovery is the control plane for agentic AI governance, not a preparatory housekeeping step. Agentic systems can be created and connected independently across clouds and business units, which means the authoritative identity problem starts before authorization ever fires. If the discovery layer is fragmented, the organisation is governing an incomplete estate. The practitioner conclusion is simple: governance quality is bounded by discovery quality.
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.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI, even as adoption continues to accelerate across infrastructure teams.
A question worth separating out:
Q: Should organisations treat agent discovery as part of IAM or platform operations?
A: They should treat it as both, but with identity governance ownership. IAM defines the policy logic, platform teams maintain the registries, and security teams validate coverage. If the work sits only in operations, it becomes an asset inventory. If it sits only in IAM, it misses the runtime changes that matter.
👉 Read our full editorial: Agentic AI discovery is the missing layer in identity governance
