Topic starter
15/08/2026 6:11 pm
TL;DR: AI agents that can use tools with borrowed API keys create a trust gap that conventional secrets handling does not close, according to Beyond Identity. Hardware-bound identity shifts the control point from reusable credentials to device-anchored proof, which matters because agentic access must be governed as runtime identity, not just secret distribution.
Editorial analysis by NHI Mgmt Group, based on content published by Beyond Identity: “It’s finally time to go passwordless”.
Key questions
Q: What breaks when AI agents rely on long-lived API keys?
A: Long-lived keys turn a single leaked secret into persistent authority, and agents create more places for that secret to leak through prompts, logs, cache layers, and tool outputs.
Q: Why do borrowed secrets increase risk for AI agent access?
A: Because a shared secret says nothing about which device, process, or agent instance is using it.
Q: How do security teams know whether managed identities are working for agents?
A: Managed identities are working when there are no embedded secrets in code or config, each agent has a distinct identity, and privileges map cleanly to a small number of functions.
Practitioner guidance
- Define agent trust boundaries by execution context Map which AI agents are allowed to operate only when their access can be tied to a specific trusted device or runtime environment.
- Reduce reliance on borrowed API keys Identify agent workflows that depend on shared or reusable credentials and prioritise replacing those paths with stronger runtime proof of identity.
- Separate credential control from identity assurance Keep rotation and vaulting in scope, but add policy that asks whether the current executor is the same actor that was authorised.
Bottom line: AI agents create a governance gap when access depends on borrowed credentials that are not bound to the runtime doing the work.
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15/08/2026 6:18 pm
Hardware-bound identity addresses an assumption that no longer holds for AI agents: that possession of a credential is enough to establish trustworthy runtime access. That assumption was designed for access models where the requester and the execution context were relatively stable. It fails when an agent can present the same credential from different runtime conditions, so practitioners have to rethink how proof, context, and authorisation relate for machine actors.
A few things that frame the scale:
- Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing rising costs, unclear value and insufficient risk controls.
A question worth separating out:
Q: What should IAM teams do when AI agents need to reach enterprise APIs?
A: They should treat agent requests as policy decisions, not as ordinary user sessions or durable service accounts. That means evaluating the agent identity, the request context, and the target resource before issuing a short-lived credential. If the access path cannot be explained clearly afterward, the governance model is still incomplete.
👉 Read our full editorial: Hardware-bound identity is the missing control for AI agents