Topic starter
02/01/2026 10:38 am
TL;DR: AI agents can access multiple applications at machine speed, which turns small entitlement errors into large authorization risks and weakens least-privilege assumptions, according to Andromeda Security. The core problem is that access review and scoped authorization were designed for slower, human-paced identity behaviour, not autonomous execution.
Editorial analysis by NHI Mgmt Group, based on content published by Andromeda Security: “Agentic AI Security: The Identity & Authorization Challenge”.
Key questions
Q: What breaks when AI agents are given broad inherited permissions?
A: Broad inherited permissions break the assumption that access is tied to a narrow business need.
A: Over-privileged AI systems expand blast radius because they can move faster, touch more systems, and make repetitive mistakes at scale.
Q: What are the signs that agent authorization controls are failing in practice?
A: The clearest warning signs are shared credentials, unclear ownership, and audit logs that show activity without proving delegation.
Practitioner guidance
- Constrain agent permissions to task scope Define each agent’s access around the exact applications and actions required for one workflow, and remove any write or admin entitlement the task does not need.
- Separate human context from shared agent access Prefer delegated authorization over pooled agent credentials, and keep per-user accountability visible when an agent acts on behalf of a person.
- Shorten token usefulness windows Use short-lived tokens, refresh token rotation, and PKCE where the agent depends on OAuth flows so stolen credentials lose value quickly.
Bottom line: AI agents can turn modest entitlement mistakes into fast, multi-system authorization events because they operate at machine speed.
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This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 6:59 pm
Machine-speed execution collapses the assumption that least privilege can be enforced after access is granted. Least privilege was built around identities whose behaviour is bounded by human pace and review cycles. Agentic AI can exercise privileges across multiple applications in milliseconds, so the governance model stops being about permission size and becomes about whether the permission model can survive autonomous runtime decisions. The implication is that entitlement governance has to be designed around execution speed, not just access scope.
A few things that frame the scale:
- 40 percent of financial and software companies have already deployed agentic AI systems, and deployments are expected to double by 2028.
A question worth separating out:
Q: Should organisations use delegated access or separate agent credentials?
A: Delegated access is usually safer when the human’s own permissions are tightly scoped, because it preserves accountability and keeps the agent inside an existing governance model. Separate agent credentials can work, but only if the organisation can govern them with the same rigour as any other privileged non-human identity.
👉 Read our full editorial: AI agent authorization is exposing a new least-privilege gap