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24/06/2026 10:58 pm
TL;DR: Identity security is a single control problem spanning workforce, machine, and AI identities, according to CyberArk, backed by a claim that 87% of organisations have experienced at least two successful identity-related breaches. The real issue is not breadth of coverage, but whether privilege, lifecycle, and governance controls can operate consistently across identity types that behave very differently.
NHIMG editorial — based on content published by CyberArk: Identity security for every identity across human, machine, and AI
By the numbers:
Questions worth separating out
Q: How should security teams govern machine and AI identities in the same IAM programme?
A: Treat them as separate actor classes with shared governance patterns.
Q: Why do service accounts and API keys increase breach risk when privilege is standing?
A: Standing privilege turns a stolen or forgotten identity into a persistent access path.
Q: What do IAM teams get wrong about visibility into non-human identities?
A: They often stop at discovery.
Practitioner guidance
- Inventory identities by actor type Separate workforce accounts, service accounts, workload identities, and AI-connected identities in your inventory so review cadence and ownership are not one-size-fits-all.
- Bind privilege to task scope Replace standing grants with task-scoped access where the workload or identity purpose is short-lived, and require a documented expiry condition for elevated rights.
- Automate offboarding for non-human identities Define revocation triggers for services, integrations, and credentials so decommissioned workloads do not retain usable access after their business purpose ends.
What's in the full article
CyberArk's full article covers the operational detail this post intentionally leaves for the source:
- How the platform describes discovery, lifecycle management, and policy automation across workforce, machine, and AI identities.
- The vendor's account of privilege controls across credentials, sessions, and entitlements in one operating model.
- Customer-story context showing how other organisations frame identity security across human and non-human identities.
- The analyst-report references and product positioning that sit behind the article's claims about breadth and maturity.
👉 Read CyberArk's analysis of identity security across human, machine, and AI identities →
Identity security for every identity: what should IAM teams change?
Explore further
25/06/2026 7:47 am
Identity security has become a cross-actor governance problem, not a tool category. The article's core claim is that workforce, machine, and AI identities now sit inside the same privilege ecosystem. That means the failure mode is not isolated misconfiguration but inconsistent governance across actor types. IAM, PAM, and IGA teams should read this as a warning that programme boundaries, not just product gaps, are now where exposure accumulates.
A few things that frame the scale:
- 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, according to Ultimate Guide to NHIs.
- 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures.
A question worth separating out:
Q: Should organisations apply the same access review process to human and non-human identities?
A: No. Human access reviews can work on periodic certification cycles, but non-human identities often change faster and need event-based review tied to deployment, rotation, or decommissioning. The better model is shared governance with different review mechanics, so the process matches how each identity class is created, used, and retired.
👉 Read our full editorial: Identity security for every identity: what CyberArk's message changes
