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25/06/2026 3:04 pm
TL;DR: Human IAM works for employees but fails for machines because service accounts, CI/CD pipelines, SaaS apps, and AI agents rely on static credentials, manual rotation, and fragmented controls, according to Aembit. The real issue is not secrets management alone, but an access model that cannot scale to non-human identity behavior.
NHIMG editorial — based on content published by Aembit: Human identity management feels solved in most companies, but non-human identity now exposes the limits of that model
Questions worth separating out
Q: How should security teams manage non-human identities that still depend on static secrets?
A: Start by identifying which workloads use shared keys, embedded passwords, or manually rotated tokens, then move the highest-risk ones to short-lived access issued on demand.
Q: Why do secrets managers not fully solve workload identity risk?
A: Because a secrets manager stores credentials, but it does not prove the workload’s identity or remove the need for a first trust decision.
Q: What is the difference between workload identity and secrets management?
A: Workload identity is about proving what a machine is and issuing access based on that proof.
Practitioner guidance
- Inventory non-human identities by access pattern Classify service accounts, CI/CD identities, SaaS integrations, and AI workloads by where they authenticate, what they access, and whether they still rely on static secrets.
- Replace stored secrets with short-lived workload credentials Move the highest-risk integrations toward ephemeral access that is issued on demand and expires automatically after use.
- Establish ownership and lifecycle control for machine identities Assign a clear owner for each non-human identity, define when it is provisioned, rotated, recertified, and retired, and make offboarding part of the same process.
What's in the full article
Aembit's full analysis covers the operational detail this post intentionally leaves for the source:
- The article's practical comparison of secrets managers, cloud-native IAM, and governance tooling in machine environments.
- The Snowflake case study details on how workload identity changed audit effort and credential handling.
- The discussion of how agentic AI and data-centre rebalancing complicate future workload identity planning.
- The article's explanation of why secretless access changes developer workflow and security operations.
👉 Read Aembit's analysis of why human IAM breaks at machine scale →
NHI scale mismatch: what IAM teams need to change now?
Explore further
25/06/2026 4:22 pm
Human IAM success has hidden the real problem. Once organisations solved employee login with MFA and SSO, they created the impression that identity was under control. That assumption fails for non-human identities because workloads do not behave like people, do not authenticate like people, and do not scale like people. The implication is that IAM maturity must be judged by machine governance, not by how well the employee experience has been standardised.
A few things that frame the scale:
- 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
- Only 19.6% of security professionals express strong confidence in their organisation's ability to securely manage non-human workload identities, a confidence gap that mirrors the article's governance warning.
A question worth separating out:
Q: How do teams know whether non-human identity controls are actually working?
A: Look for reduced use of shared credentials, fewer manually rotated secrets, shorter credential lifetimes, and clear ownership for each workload identity. If access still depends on long-lived material in code, tickets, or inboxes, the control plane is not working as intended.
👉 Read our full editorial: Human IAM breaks at machine scale: why NHI needs new identity models
