Topic starter
15/08/2026 8:29 pm
TL;DR: AI agents behave like software with human-like runtime judgment, and Aembit argues that workload identity alone cannot safely scope, attribute, or constrain that access because agent behaviour changes at runtime. The security issue is not deployment speed but access models built for deterministic workloads, where static credentials and fixed permission boundaries no longer hold.
Editorial analysis by NHI Mgmt Group, based on content published by Aembit: “10 Identity Security Vendors for AI Agents: Strengths, Tradeoffs and How They Fit”.
Key questions
Q: How should security teams govern AI and workload identities at runtime?
A: Security teams should govern runtime identities by combining least privilege, continuous telemetry, and approval-gated containment.
Q: Why do AI agents increase access risk compared with fixed workloads?
A: Because an agent can assemble its access path at runtime, the true blast radius is not always known at provisioning time.
Q: What breaks when an AI agent is given a generic service credential?
A: A generic service credential breaks the link between task scope and access scope.
Practitioner guidance
- Define a blended identity policy for agents Require both agent identity and user context before an agent can access enterprise resources.
- Replace standing agent credentials with task-scoped issuance Issue short-lived credentials at runtime and expire them when the task completes.
- Separate visibility from enforcement Inventory which agents are present, but also enforce policy before the request reaches the target system.
Bottom line: AI agents do not fit cleanly into workload identity because their access path is inferred at runtime, not known in advance.
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15/08/2026 8:29 pm
Blended identity is the right category for AI agents because neither workload identity nor human identity fully explains their access behaviour. AI agents combine machine execution with user-delegated intent, which makes them structurally different from deterministic services and from people using fixed credentials. That is why the identity model has to preserve both agent context and user context. The practitioner conclusion is simple: classify the actor correctly before you decide how to govern it.
A few things that frame the scale:
- Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously.
A question worth separating out:
Q: What is the difference between agent visibility and runtime enforcement for access control?
A: Visibility tells you which agents exist and what they touched. Enforcement decides, at the moment of access, whether a specific agent acting for a specific user should be allowed to proceed. Visibility documents the problem; enforcement changes the outcome.
👉 Read our full editorial: AI agents need blended identity, not plain workload identity