TL;DR: Akeyless says machine identities now make up more than 90% of identities in many enterprises, and Gartner’s 2026 Innovation Insights finds most organisations still lack centralized governance, automation and visibility for secrets and machine IAM. The control gap is structural: static credentials, fragmented tooling and inconsistent enforcement cannot keep pace with ephemeral machine access.
At a glance
What this is: Akeyless summarises a Gartner-backed machine IAM problem: identity sprawl has outpaced governance, leaving secrets, workload access and automation fragmented.
Why it matters: IAM and NHI teams need to treat machine identity as a first-class governance domain because static controls and manual processes cannot cover modern workload, microservice and AI-agent access at scale.
👉 Read Akeyless's analysis of machine IAM governance and identity sprawl
Context
Machine IAM is the governance and control layer for service accounts, workload identities, secrets, certificates and other non-human identities that authenticate to systems. The problem in this article is not just growth in machine identities, but the mismatch between that growth and the way most organisations still manage access, rotation and oversight.
Akeyless cites Gartner to argue that governance remains fragmented, visibility is weak and automation is missing across secrets and machine identity programs. In practice, teams are compensating with scripts, vault sprawl and manual certificate handling, which creates inconsistent enforcement and a wider attack surface.
The article also widens the scope to AI agents, which the vendor treats as workloads that still require identity, authorisation and secrets governance. That makes this a machine IAM story, but one that now intersects with autonomous execution and not just traditional service accounts.
Key questions
Q: What breaks when machine IAM is managed with ad hoc scripts and separate vaults?
A: Governance breaks because no single team can prove which machine identities exist, who owns them, or whether their access is still appropriate. Ad hoc scripts and separate vaults create inconsistent enforcement, duplicate exceptions and delayed provisioning. The result is a broader attack surface and weaker auditability, especially in hybrid environments where access changes quickly.
Q: Why do static credentials create so much risk for machine identities?
A: Static credentials are hard to track, easy to copy, and often survive long after the workload that used them has changed. That gives attackers or insiders a reusable access path with no natural expiry, which is why machine identity programmes need lifecycle controls as much as secret storage.
Q: How can IAM teams tell whether access governance is actually working?
A: Look for complete discovery coverage, low revocation latency, and certification results that match actual entitlement inventories. If reviews keep finding unknown apps, abandoned accounts, or recurring exceptions, the process is generating activity but not control.
Q: Should organisations treat agentic AI access differently from service account access?
A: Yes. Service accounts are usually persistent and can be managed through lifecycle controls, while agentic AI access is often ephemeral, runtime-selected, and initiated on demand. The right governance model is different because the identity behaviour is different. Treating both as the same class leads to control gaps and delayed policy decisions.
Technical breakdown
Why machine IAM becomes fragmented in hybrid environments
Machine IAM breaks down when teams treat secrets, certificates, workload identity and privileged service accounts as separate problems. Each team introduces local tooling, local policies and local exceptions, so the organisation ends up with patchwork governance instead of a single control model. In hybrid and multi-cloud environments, that fragmentation is amplified because identity issuance, rotation and audit evidence live in different planes. The result is not just operational inconsistency. It is an identity surface that no one can inventory cleanly or govern consistently.
Practical implication: map every machine-identity control plane and remove duplicated ownership before trying to standardise policy.
Why static credentials fail for modern workload access
Static credentials assume an identity can remain valid long enough to justify manual review, periodic rotation and human-visible control. That assumption no longer holds for ephemeral workloads, short-lived containers and automation that creates and destroys access in minutes. The security problem is not only exposure if a secret leaks, but also the persistence window created by long-lived credentials that outlast the task they were issued for. Machine IAM therefore has to move toward issuance-time control, time-bounded access and tightly scoped authentication rather than retrofitting human-era credential habits onto workloads.
Practical implication: replace long-lived machine secrets with short-lived, scope-limited credentials wherever the workload can support it.
How autonomous workloads change identity governance
AI agents and automated jobs change machine IAM because they can trigger access as part of runtime execution rather than through a predictable human workflow. That does not make them human identities, but it does mean governance must account for runtime identity creation, scoped authorisation and rapid revocation. The central issue is not tool complexity; it is the speed at which access appears, is used and should disappear. Once AI-driven workflows are in play, machine IAM is no longer just about storage of secrets. It becomes a control problem for execution-time trust.
Practical implication: classify AI-driven workloads under the same lifecycle and policy controls used for other high-risk machine identities.
Threat narrative
Attacker objective: The attacker aims to reuse machine credentials or overprivileged workload access to move through systems without relying on a human login.
- Entry occurs when machine access is provisioned through static secrets, hardcoded credentials or ad hoc service accounts that outlive the workload that uses them.
- Escalation follows when fragmented governance leaves over-permissioned identities, delayed provisioning and inconsistent enforcement across environments.
- Impact is a larger attack surface with harder-to-audit access paths, making credential misuse and secret exposure more damaging and more difficult to contain.
Breaches seen in the wild
- Hugging Face Spaces breach 2024: Unauthorised access to Hugging Face Spaces may have exposed secrets users stored for AI apps; tokens were revoked and org tokens removed.
- Microsoft Midnight Blizzard breach: Midnight Blizzard (APT29) exploited legacy test account without MFA to breach Microsoft.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Machine IAM sprawl is now a governance failure, not a tooling gap: The article shows that most organisations have more machine identities than human ones, but still govern them as a side problem. That combination produces inconsistent enforcement, delayed provisioning and blind spots that no amount of local scripting can fix. The practical conclusion is that machine identity must be managed as a distinct governance domain, not as an extension of user IAM.
Static credential governance was designed for stable actors: The assumption that access exists long enough to be reviewed, rotated and recertified was built for slower, more predictable identity lifecycles. That assumption fails when workloads are ephemeral and access is created and destroyed in seconds. The implication is not simply to add more automation, but to rethink whether your control model is still built around a credential lifetime that no longer exists.
Machine IAM has become the convergence point for secrets, workload identity and AI agent access: The article correctly treats these as one operational domain because the same governance failures repeat across all three. Secrets leakage, overprivilege and inconsistent policy enforcement are not separate issues once runtime access is automated. The practitioner takeaway is to govern machine identities by lifecycle and scope, not by the technology label attached to them.
Centralised policy matters more than isolated hardening: The most useful signal in the article is that patchwork solutions create patchwork assurance. Vaults, scripts and ad hoc certificate handling may solve local pain, but they do not produce consistent access evidence or comparable governance across environments. The field is moving toward centralised control because fragmented control cannot produce reliable accountability at machine scale.
Ephemeral credential trust debt: When credentials are issued for short-lived machine activity but the surrounding governance still assumes long-lived access, the organisation accumulates trust debt. That debt shows up as overscoped permissions, stale secrets and unverifiable access pathways. The implication for practitioners is to measure whether access is governed at issuance time, not just at review time.
From our research library:
- NHIs outnumber human identities by 25x to 50x in modern enterprises, according to the Ultimate Guide to NHIs.
- Enterprises manage far more machine secrets than human ones: 20 times as many according to Enterprise Strategy Group, and 45 times according to GitGuardian.
- Read next: NHI Lifecycle Management Guide
What this signals
Machine IAM governance has crossed the threshold from operational hygiene to access architecture. Once machine identities outnumber human ones by 25x to 50x, the old assumption that user IAM can absorb workload identity as a side concern stops holding. Security teams should separate machine identity governance from human access management and give it its own inventory, lifecycle and control plane.
Identity blast radius is the right lens for modern machine access. When workloads, certificates, service accounts and AI agents are all issuing access at runtime, the real question is how far a single credential can spread before it is detected or revoked. Teams should measure whether their current governance model reduces blast radius or simply records it after the fact.
For practitioners
- Inventory all machine identity classes Separate service accounts, workload identities, certificates, API keys and AI-agent credentials into one governed inventory so ownership and policy gaps are visible.
- Replace static secrets with short-lived credentials Prioritise workloads that can authenticate through identity providers and runtime issuance so the credential lifetime matches the task lifetime.
- Centralise machine identity policy enforcement Use a single control plane for issuance, rotation, audit logging and policy application so teams stop managing parallel exceptions across tools and clouds.
- Scope AI agent access as machine identity Apply the same identity, authorisation and lifecycle controls to AI-driven workflows that you already expect for high-risk service accounts.
- Monitor machine access continuously Send machine identity events into your SIEM and alert on orphaned secrets, overprivileged access and unexpected credential reuse.
Key takeaways
- Machine identity growth has outpaced the governance model most organisations still use for access, secrets and certificate handling.
- Fragmented ownership and manual enforcement create an attack surface that scales faster than review cycles can compensate.
- Practitioners need central policy, short-lived credentials and lifecycle control if they want machine IAM to keep pace with modern workloads.
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 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-02 — Secret Leakage | The article centres on poorly managed secrets across machine identities and workloads. |
| NHI-07 — Long-Lived Secrets | Static credentials and slow rotation are the core control weakness described here. | |
| NHI-05 — Overprivileged NHI | Fragmented governance leads to excessive access and inconsistent enforcement for machine identities. | |
| Recommendation — Scan for exposed machine secrets and revoke any credentials that leak into code, logs or scripts. Shorten credential lifetime and automate rotation where machine access still relies on long-lived secrets. Review machine identity permissions and remove any entitlement that exceeds the workload’s actual scope. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about governing entitlements for machine identities across environments. |
| Recommendation — Centralise entitlement control so machine identities receive only the access required for each task. | ||
| CIS Controls v8 | CIS-5 — Account Management | Machine IAM sprawl is an account and credential governance issue at scale. |
| Recommendation — Inventory machine accounts and remove stale or orphaned identities from every environment. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | The article’s risk model is credential misuse leading to broader movement through systems. |
| Recommendation — Map exposed machine credentials to credential-access and lateral-movement detections. | ||
Key terms
- Machine IAM: Machine IAM is the discipline of governing non-human identities such as APIs, service accounts, tokens, certificates, and automation workloads. It extends identity control to entities that authenticate programmatically and often operate continuously, which makes lifecycle management, revocation, and privilege scoping more important than login experience.
- Secrets Management: The discipline of securely storing, distributing, rotating, and auditing secrets across an organisation's systems and pipelines, typically implemented via a centralised secrets vault such as HashiCorp Vault, AWS Secrets Manager, or Akeyless.
- Workload Identity: The identity assigned to a software workload, such as a containerised application, serverless function, or microservice, enabling it to authenticate to other services without storing static credentials.
- Ephemeral Credentials: Ephemeral credentials are short-lived access artefacts issued for a limited task or session. They reduce the window for abuse, but they only improve security when paired with strong scope limits, telemetry, and automatic revocation at task completion.
What's in the full article
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- Gartner-linked capability gaps across secrets management and machine IAM governance
- Examples of runtime secrets injection and secretless authentication in dynamic environments
- How certificate lifecycle automation and centralised logging are positioned for multi-cloud operations
- The vendor's view of how AI agent workflows fit into the same machine identity model
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 August 17, 2026.
Updated on October 7, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org