Topic starter
15/12/2025 3:31 pm
TL;DR: Akeyless shows that human identity providers were built for people, not for workloads, pipelines, containers, or AI agents that authenticate at machine speed and rely on short-lived cryptographic credentials. Access review and HR-paced lifecycle controls assume identity stays stable long enough to be observed, while non-human access can appear and disappear before human governance catches up.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “Human Identity Providers vs Machine & AI Agent Identity Providers”.
Key questions
Q: Where does human IdP governance fail for machine and AI agent identities?
A: It fails where governance assumes an identity is stable long enough to be reviewed, approved, and offboarded through human cadence.
Q: When do short-lived credentials become insufficient for AI agent risk?
A: Short-lived credentials become insufficient when an organisation cannot see, scope, and revoke every grant the agent touches.
Q: What are the signs that non-human identity governance is starting to slip?
A: Warning signs include reliance on manual upgrade paths, ad hoc secret handling, and fragmented access management across tools and environments.
Practitioner guidance
- Separate human and non-human identity governance Create a distinct operating model for workloads, pipelines, containers, and agents so their authentication, authorization, and audit requirements are not forced through human IdP workflows.
- Move control to issuance time Require short-lived credentials, explicit scopes, and automated revocation for every machine and AI agent identity instead of relying on periodic access reviews.
- Inventory non-human identity types Classify service accounts, tokens, certificates, and AI agent identities separately so ownership, purpose, and lifecycle can be governed without ambiguity.
Bottom line: Human IdPs are optimized for people, but machine and AI agent identities need governance that works at issuance time and at runtime.
What's in the full article
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- How the machine and AI agent IdP model maps to concrete authentication choices such as IAM roles, OIDC tokens, certificates, and service accounts
- The platform comparison table that contrasts human identity with machine and AI agent identity across lifetime, authorization, governance, and audit
- Descriptions of short-lived credential issuance, federation, and scoped permissions for autonomous systems
- The AI agent security features the article associates with secretless access, privileged access, and unified observability
👉 Read Akeyless's analysis of why machine and AI agent identity needs a new IdP model →
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This topic was modified 10 months ago 2 times by Abdelrahman
This topic was modified 6 days ago by NHI Mgmt Group
07/10/2026 8:18 pm
Human IdP assumptions no longer describe the identity estate: The article shows that identity governance built around people cannot fully describe the access reality of workloads, containers, pipelines, and agents. These actors are not just a larger population; they authenticate, expire, and execute on a different clock. The practitioner conclusion is that the identity model itself now has to distinguish human pace from machine pace.
A few things that frame the scale:
- Across one million observed logins, 1 in 4 were password-based rather than SSO, 2 in 5 were not protected by MFA and 1 in 5 used a weak, breached or reused password.
A question worth separating out:
Q: What should security teams do when an AI agent can act autonomously across systems?
A: They should bound the agent’s allowed actions, require explicit scopes for each task, and design controls around runtime authority rather than after-the-fact review. The goal is to prevent one identity from chaining read, call, and execute steps across systems without visible governance.
👉 Read our full editorial: Machine and AI agent identity security needs a new IdP model