TL;DR: Machine identities are harder to manage than human identities for 72% of identity professionals, and 66% say the work requires more manual steps, according to SailPoint’s study. The editorial point is that visibility, ownership, and policy consistency now determine whether machine identity risk stays containable or becomes structural.
At a glance
What this is: SailPoint argues that machine identity risk is exposing enterprise IAM gaps because many organisations cannot consistently see, own, or govern machine accounts.
Why it matters: IAM, IGA, and PAM teams need machine identity visibility and lifecycle control because unmanaged service accounts can create persistent access blind spots across critical resources.
By the numbers:
- 72% of identity professionals say machine identities are more difficult to manage than human identities.
- 66% of study respondents report that managing machine identities requires more manual steps than managing human identities.
- 62% of companies surveyed said they have machine identities active without any visibility.
Context
Machine identity risk is an IAM governance problem, not just an operational nuisance. When applications, services, and devices authenticate and act without clear ownership or oversight, access can persist outside normal review and certification cycles.
The article frames the issue around visibility, manual process burden, and overly permissive access. That combination matters because machine accounts often sit in the background until they become the easiest route to critical resources.
For identity programmes, the core question is no longer whether machine identities exist, but whether they are discoverable, classifiable, and governed with the same discipline applied to human access.
Key questions
Q: What breaks when machine identities are tracked manually?
A: Manual tracking breaks when credential volume outpaces human oversight. Teams lose visibility into where certificates, keys, and secrets live, who owns them, and when they expire. That creates outages, audit gaps, and stale access paths that remain valid longer than intended. Central inventory and automated lifecycle control are the practical response.
Q: Why do machine identities create more risk than human identities in some environments?
A: Machine identities are often numerous, long-lived, and embedded in code or infrastructure. They are harder to review manually, easier to overlook during offboarding, and more likely to carry excessive privilege. That combination increases blast radius when a secret or token is exposed.
Q: How do security teams know if machine identity governance is working?
A: Look for fewer standing accounts, faster onboarding of automation workflows, auditable role approvals, and visible retention of access records after logout. If teams still depend on manual tracking to explain machine access, governance is only partially effective. Working machine identity governance should reduce both operational overhead and review friction.
Q: How should teams reduce machine identity visibility gaps in enterprise IAM?
A: Teams should start by building a complete inventory, then attach ownership, review, and entitlement checks to every machine account. The goal is to make hidden identities visible enough to govern and stale permissions easy to remove. Without that sequence, automation only scales the blind spots instead of reducing them.
Technical breakdown
Why machine accounts evade normal IAM oversight
Machine identities are not people, but they still hold credentials, permissions, and access paths that can be abused. The problem is that they often run continuously, integrate across systems, and lack a clear human actor watching them. That makes them easy to overlook when IAM programmes are built around joiner-mover-leaver processes for employees. Once a machine account is active, it can keep operating long after the business owner forgets it exists. The governance failure is usually not absence of authentication, but absence of ownership, review, and lifecycle control.
Practical implication: inventory machine accounts separately from human users and require named ownership for each one.
How excess privilege turns visibility gaps into risk
Machine identities frequently need broad access to work across systems, but broad access becomes dangerous when it is granted once and never revisited. Over time, service accounts accumulate permissions that exceed their current function, especially in environments where access is copied, reused, or left untouched after application changes. That creates an identity layer with hidden blast radius. In NHI terms, the issue is not just whether the account authenticates correctly, but whether its standing access still matches the task it performs. Excess privilege plus low visibility is what makes machine identity risk operationally stubborn.
Practical implication: review machine account entitlements against actual service function and remove permissions that are no longer required.
Why unified policy and workflow matter for machine identity governance
A fragmented tool stack often leaves machine identities governed by different workflows, different data models, and inconsistent policy checks. That is why teams end up with manual handling, blind spots, and uneven oversight across platforms. A unified approach matters because machine identity control is not just about one vault, one scanner, or one workflow. It is about making discovery, ownership, access review, and enforcement operate from the same identity model. Without that consistency, governance becomes partial and the weakest integration becomes the effective control boundary.
Practical implication: align machine identity governance to a single policy model so discovery, review, and enforcement use the same source of truth.
Threat narrative
Attacker objective: The objective is to exploit a machine identity whose access outlives oversight, creating a durable and low-friction path into enterprise resources.
- Entry occurs when a machine account is created or inherited without clear visibility into who owns it or why it exists.
- Escalation follows when that account retains excessive permissions across critical resources long after its original purpose has changed.
- Impact emerges when the hidden account becomes a durable path to sensitive systems, and teams discover they cannot confidently explain or limit its access.
Breaches seen in the wild
- Code Formatting Tools Credential Leaks: Widely used code formatting tools cause massive credential and secrets leaks in enterprise environments.
Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Machine identity visibility is now a governance baseline, not an advanced capability. The article shows that organisations can no longer rely on human ownership assumptions to cover machine accounts. When 62% of surveyed companies say they have active machine identities without visibility, the governance gap is already embedded in the estate. Practitioners should treat discoverability as a prerequisite for control, not a reporting enhancement.
Access review models built for human accounts do not translate cleanly to machine identities. Human-driven certification cycles assume an identifiable person can attest to use, but service accounts often run unattended and continuously. That means the real control question is whether the account should exist, who owns it, and whether its permissions still match the service it supports. The implication is that machine identity governance needs lifecycle logic, not only periodic review.
Ephemeral ownership is the wrong mental model for machine identities that persist indefinitely. The article highlights a structural mismatch between background accounts and enterprise oversight. If a machine identity keeps authenticating after the application changes, ownership becomes nominal and accountability fragments. Practitioners need to recognise that machine identity risk is often persistence without stewardship, which is why access drift becomes the real problem.
Visibility debt is the right name for the hidden machine identity problem. Once identities exist without reliable discovery, organisations accumulate access they cannot inventory, review, or confidently revoke. That debt compounds across platforms because each tool sees only part of the estate. The practical conclusion is that machine identity governance has to start with a complete inventory and a consistent policy model, or the rest of the programme will remain partial.
Machine identity governance belongs inside enterprise identity architecture, not beside it. The article points to a multi-vendor environment where inconsistent workflows and APIs produce blind spots. That is a sign that machine accounts cannot be managed as an afterthought attached to infrastructure teams. They need to sit inside the same identity governance model that already governs human and privileged access, otherwise the programme remains structurally incomplete.
What this signals
Visibility debt: machine identity programmes fail when organisations accept active accounts they cannot explain, assign, or retire. That debt compounds across hybrid estates because one blind spot becomes many, and manual handling is too slow to keep pace with service sprawl.
The governance priority is to treat machine identities as first-class identities with ownership, reviewability, and lifecycle state. When those attributes are missing, access decisions become inferential instead of evidential, and the programme loses the ability to enforce policy consistently.
For practitioners
- Discover and classify machine accounts Build a current inventory of service accounts, application identities, and device credentials, then classify each by business function and risk.
- Assign a human owner to every machine identity Require a named accountable owner for each machine account so review, exception handling, and retirement decisions have a clear decision-maker.
- Review entitlements against actual service function Compare the permissions on each machine identity with the workload it currently supports and remove access that no longer maps to runtime need.
- Unify governance workflows across platforms Use one policy model for discovery, access review, and enforcement so machine identity controls do not diverge by tool or business unit.
Key takeaways
- Machine identities create governance gaps when they are active but not visible, because unmanaged service accounts can preserve access long after their original purpose changes.
- The article’s numbers show that this is already a mainstream IAM problem, with survey respondents reporting difficulty, manual overhead, and invisible active accounts.
- The practical fix is to inventory machine identities, assign ownership, and align entitlements to current service function before hidden access becomes durable risk.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-53 Rev 5 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | Machine accounts that remain active without oversight reflect lifecycle failures in NHI governance. |
| NHI-05 — Overprivileged NHI | The article highlights excessively permissive access rights across critical resources. | |
| NHI-07 — Long-Lived Secrets | Machine identities often persist with unchanged credentials and weak lifecycle oversight. | |
| Recommendation — Map dormant and unmanaged machine accounts to NHI-01 and revoke identities that no longer have a business owner. Apply NHI-05 to reduce machine account entitlements to the smallest set required for the workload. Use NHI-07 to find machine credentials that outlive the service and force rotation or retirement. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about controlling and reviewing machine identity access. |
| Recommendation — Use PR.AA-05 to govern machine identity permissions through reviewable, policy-based authorization. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Machine accounts depend on managed credentials and lifecycle control over authenticators. |
| Recommendation — Apply IA-5 to control machine credential issuance, rotation, and revocation on a defined schedule. | ||
| CIS Controls v8 | CIS-5 — Account Management | The article focuses on discovering, owning, and reviewing non-human accounts at scale. |
| Recommendation — Use CIS-5 to maintain an accurate machine account inventory and remove stale or orphaned identities. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | Hidden machine identities with broad access create paths attackers can exploit for credential use and movement. |
| Recommendation — Map machine identity blind spots to TA0006 and TA0008 and prioritise accounts with broad system reach. | ||
Key terms
- Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
- Visibility gap: A visibility gap is the point where a security team can no longer reliably see who has access to what, or why that access exists. In identity and data governance, it is a control failure because remediation depends on accurate ownership and current entitlement state.
- Identity Ownership: Identity ownership is the assignment of a responsible human for each identity's purpose, access, review, and retirement. For non-human identities, ownership must be explicit because the creator is not always the right person to approve ongoing access. Without ownership, review and revocation become inconsistent and slow.
- Answer Drift: Answer drift is the gradual change in a model’s responses over time, often showing up as reduced consistency or increasing error rates. It can signal degraded grounding, shifting data quality, or prompt and retrieval issues. Monitoring drift helps teams catch reliability problems before they become widespread user-facing failures.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on June 25, 2026.
Updated on October 6, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org