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08/06/2026 4:35 pm
TL;DR: AI agents are scaling faster than many enterprises can map their underlying access, and one case study found 400 GPTs, 250+ active agents, and multiple high-risk exposures across BigQuery, Jira, and shared data sources, according to Astrix Security. The real control problem is not orchestration but identity-layer visibility, because governance cannot work when teams do not know what agents can reach.
NHIMG editorial — based on content published by Astrix Security: AI agent control planes and the identity layer behind them
By the numbers:
- Astrix Security found that 10% had direct API access to external systems such as BigQuery and Atlassian.
Questions worth separating out
Q: How should security teams govern AI agents that use non-human identities?
A: They should govern the underlying non-human identities first, because the agent itself is only the execution layer.
Q: Why do AI agents complicate IAM and NHI controls?
A: AI agents complicate IAM and NHI controls because they can scale faster than entitlement review and attach to many systems through reusable credentials.
Q: What breaks when an AI agent is connected to production data with a shared credential?
A: The access boundary breaks because one credential can expose multiple systems and data sets beyond the intended use case.
Practitioner guidance
- Inventory every agent identity Map each GPT, agent, or assistant to the API keys, OAuth tokens, and service accounts it actually uses.
- Separate agent access from developer convenience Review shared credentials, internal-use keys, and broad integrations that let one agent reach production data or external systems.
- Enforce access reviews on agent-connected systems Put agent-connected data stores, SaaS apps, and development tools into the same review cycle as other high-risk NHI access.
What's in the full article
Astrix Security's full article covers the operational detail this post intentionally leaves for the source:
- The step-by-step mapping of GPTs to identities, secrets, permissions, and connected systems.
- The first-week discovery workflow that surfaced risky agents, shared data sources, and sensitive access paths.
- The specific enforcement model used to move from visibility to revocation and policy application.
- The implementation detail behind the enterprise's internal ownership and control process.
👉 Read Astrix Security's analysis of AI agent control through non-human identities →
AI agent access visibility: what IAM teams are missing?
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08/06/2026 6:18 pm
Identity visibility is the control plane, not a supporting feature. The article shows that monitoring and orchestration do not equal governance when the organisation cannot see which non-human identities sit behind an AI agent. Access control is defined at the identity layer, so a control plane that lacks identity binding is structurally incomplete. Practitioners should treat agent governance as NHI governance first, and agent telemetry second.
A few things that frame the scale:
- The 2024 ESG Report: Managing Non-Human Identities found that 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks.
A question worth separating out:
Q: What should organisations do before approving more AI agents?
A: They should require ownership, inventory, and access scoping for every agent before broader rollout. That means documenting which non-human identities the agent uses, which systems it can reach, and which data sources it may touch. If those three items are not known, the organisation does not yet have enforceable governance.
👉 Read our full editorial: AI agent control planes fail when identity visibility is missing
