They should correlate identity events, SaaS connections, cloud role assumptions, and API token use instead of relying on application inventories alone. AI agents often enter through personal accounts or shadow integrations, so discovery has to follow execution paths, not just sanctioned logins. That gives security teams a live picture of the actual identity estate.
Why This Matters for Security Teams
AI agents that bypass the IdP are a discovery problem before they are an access-control problem. Once an agent authenticates through a personal account, a shadow OAuth app, a cloud role assumption, or a direct API token, the identity provider no longer shows the full execution path. That means inventories built from sanctioned logins miss the activity that actually matters.
This is why discovery has to follow identity telemetry across SaaS, cloud, and API layers, not just account creation events. The gap is already visible in the field: The State of Non-Human Identity Security reports that 85% of organisations lack full visibility into third-party vendors connected via OAuth apps. That kind of blind spot is exactly where agentic workloads hide, especially when they chain tools faster than analysts can reconcile alerts. Security teams also need to account for the behaviour described in AI Agents: The New Attack Surface report, where autonomous actions have already exceeded intended scope in many environments.
In practice, many security teams discover these agents only after a sensitive API call, data exposure, or cloud role misuse has already occurred, rather than through intentional inventory management.
How It Works in Practice
Effective discovery starts with correlation, not classification. Security teams should aggregate identity events from the IdP, then join them to SaaS OAuth grants, cloud role assumptions, M365 or Google Workspace app consent, API token creation, and service-to-service traffic. The goal is to reconstruct the execution path of the agent, even when the agent itself never appears as a first-class application in the IdP.
This approach aligns with current guidance from OWASP Top 10 for Agentic Applications 2026 and the CSA MAESTRO agentic AI threat modeling framework, both of which emphasise runtime context and tool-chain visibility over static app listings. For discovery, that usually means:
- Tracing OAuth consent events and identifying apps with delegated access that were not registered centrally.
- Matching cloud role assumptions to the upstream principal, then checking whether the principal is a human user, workload, or agent runtime.
- Reviewing API token issuance, rotation, and reuse patterns for signs of automated execution.
- Inspecting SaaS audit logs for unusual sequencing, such as rapid mailbox access followed by file export and external sharing.
- Linking AI platform logs to downstream system actions so the agent is visible even when the IdP is not the source of truth.
Workload identity signals are especially important when agents authenticate through machine identities, ephemeral tokens, or federated credentials instead of interactive login. This is where NHI governance and agentic AI governance overlap: NHI Lifecycle Management Guide is useful for understanding issuance and revocation discipline, while NIST AI Risk Management Framework reinforces the need to map risk to actual system behaviour. These controls tend to break down when agents operate inside unmanaged SaaS tenants or personal developer accounts because the telemetry is fragmented and the ownership boundary is unclear.
Common Variations and Edge Cases
Tighter discovery often increases telemetry and investigation overhead, requiring organisations to balance coverage against the cost of normalising logs from multiple identity planes. That tradeoff becomes sharper when agents use ephemeral credentials, outsourced automations, or contractor-owned SaaS environments.
There is no universal standard for this yet, but current guidance suggests prioritising the highest-risk blind spots first: shadow OAuth grants, service principals with broad cloud permissions, and any agent that can reach sensitive data stores without an approval workflow. Personal accounts used for testing, local development, or “temporary” automation are also common edge cases because they blur the line between human and machine activity.
For teams using OWASP NHI Top 10 and the MITRE ATLAS adversarial AI threat matrix, the practical lesson is to hunt for execution chains, not just named identities. Discovery gets harder when agents spawn sub-agents, proxy through browser sessions, or reuse a human’s SSO session token, because the agent becomes invisible to controls that assume one principal equals one workflow.
Best practice is evolving toward continuous graph-based identity discovery, but it is still immature in environments with heavy legacy integration or inconsistent logging retention.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Discovery must expose shadow agent paths and unauthorized tool use. |
| CSA MAESTRO | M1 | MAESTRO emphasizes agent context, lineage, and control-plane visibility. |
| NIST AI RMF | GOVERN | AI RMF governance requires accountability for autonomous system behavior. |
Correlate agent actions to tools and grants at runtime, not just approved app inventories.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
