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06/06/2026 2:03 am
TL;DR: AI systems still depend on familiar identity primitives, but service principals, managed identities, access keys and OAuth consent are now spreading across pods, SaaS tools and third-party tenants, creating three overlapping blind spots for IAM teams according to Oasis Security. The real issue is not the AI label, but the assumption that identity flows remain visible, stable and centrally governable.
NHIMG editorial — based on content published by Oasis Security: Three frontiers, one challenge
Questions worth separating out
Q: How should security teams govern AI applications that depend on multiple non-human identities?
A: They should map the full access chain, not just the visible app.
Q: Why do copied API keys and access tokens create long-term risk in AI and SaaS workflows?
A: Because they detach access from the original business use case.
Q: What do teams get wrong about third-party OAuth integrations?
A: They often treat consent as if it were the control, when consent is only the start of the access path.
Practitioner guidance
- Build a complete NHI inventory for AI workflows Map every service account, service principal, access key and OAuth grant used by AI-assisted processes, including hidden dependencies inside pods, wizards and third-party tenants.
- Separate copied keys from controlled identities Flag any access key or token that has been pasted into a SaaS setup flow, shared across replicas or reused across tools, then assign an owner and renewal path.
- Review third-party consent as machine access Reassess OAuth approvals and vendor-hosted integrations as active non-human identities with downstream data access, not as one-time user actions.
What's in the full article
Oasis Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step examples of how credentials appear inside support chat, Copilot-style integrations and vendor-hosted add-ons.
- Operational description of the See.Secure.Govern model and how it maps to live inventory and policy enforcement.
- Specific examples of how AI-era NHIs differ from traditional service accounts in cloud-native environments.
- The vendor's own explanation of how its workflow handles discovery, analytics and lifecycle orchestration.
👉 Read Oasis Security's analysis of three AI and NHI identity frontiers →
AI, cloud-native and traditional NHIs: what IAM teams miss?
Explore further
06/06/2026 3:30 am
Credential sprawl is now the dominant control problem in AI-enabled identity estates. The article shows the same pattern across traditional IT, cloud-native systems and AI workflows: invisible credentials sit inside configs, charts, pods and third-party tenants. That means the control failure is not lack of a single tool, but lack of a coherent identity inventory across every non-human execution path. Practitioners should treat visibility across NHIs as a baseline security requirement, not an optimisation.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
- That same research found only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, which shows the governance gap is broader than one workflow.
A question worth separating out:
Q: How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
A: Start with the identity mechanics. If the use case depends on service principals, managed identities, access keys or delegated OAuth grants, it needs NHI governance first. If it also makes runtime decisions about actions and tool use without approval gates, then autonomous behaviour adds another layer of control analysis.
👉 Read our full editorial: Three frontiers, one identity challenge for NHI governance
