Machine identities need governance beyond human IAM controls

At a glance

What this is: This is Delinea’s machine identity awareness post, and its key finding is that human-first identity controls leave NHIs and AI workloads under-governed.

Why it matters: It matters because IAM teams must govern service accounts, tokens, certificates, and AI agents with lifecycle and privilege controls that fit non-human behaviour.

By the numbers:

• 74% say machine identity management complexity has increased significantly in the past two years.
• 61% rely on spreadsheets or manual tracking for machine identity management.
• Only 38% have automated certificate lifecycle management in place.

👉 Read Delinea's post on why machine identities need stronger governance

Context

Machine identities are non-human identities such as service accounts, API keys, tokens, certificates, workloads, and AI agents. The governance problem is that many programmes still treat identity as a human login problem, which leaves machine-issued access under-inventoried, under-owned, and over-permissioned.

Delinea’s message is straightforward: once identities are created by code, copied across environments, and used by workloads or AI systems, the controls that were built around interactive user authentication stop covering the full attack surface. That is a machine identity governance problem first, and a tooling problem only second.

Key questions

Q: How should security teams govern machine identities at scale?

A: Treat machine identities as a separate governance population with their own discovery, ownership, rotation, and offboarding rules. Human IAM controls do not reliably cover service accounts, API keys, certificates, or AI workloads. The practical test is whether every non-human credential has an accountable owner, a documented purpose, and a revocation path that works without manual guesswork.

Q: Why do machine identities create more risk than human accounts in cloud environments?

A: Machine identities often outnumber human accounts, are cloned across environments, and can be hidden in code or configuration. That combination makes them harder to inventory, harder to review, and easier to abuse if a shared secret or standing privilege is exposed. Risk rises when the organisation cannot quickly answer who owns the identity and what it can access.

Q: What breaks when organisations manage machine identities like user accounts?

A: The programme loses visibility, ownership, and lifecycle control. Machine identities do not follow human onboarding, MFA, or password-reset patterns, so user-first processes miss the real control points. That leads to orphaned credentials, weak attribution, and a larger attack surface than the access review process is able to detect.

Q: How do teams know whether machine identity controls are actually working?

A: Look for complete inventory coverage, clear ownership, regular credential rotation, and the ability to revoke access quickly without breaking dependent services. If identities still rely on spreadsheets, shared secrets, or manual exception handling, the control environment is not working at enterprise scale. The signal to watch is whether access can be governed without emergency intervention.

Technical breakdown

Why machine identities evade human IAM controls

Machine identities do not behave like users. They do not log in interactively, they are often created programmatically, and they may never pass through MFA or standard user onboarding. That means the control points IAM teams rely on for people, such as authentication prompts, password resets, and user-centric access reviews, do not map cleanly to service accounts, API keys, and workload credentials. In practice, the identity exists in code, configuration, or cloud metadata rather than a human directory. Practical implication: security teams need separate discovery and governance for machine-issued credentials instead of assuming human IAM coverage is enough.

Practical implication: security teams need separate discovery and governance for machine-issued credentials instead of assuming human IAM coverage is enough.

Shared secrets, cloning, and hidden inventory create control blind spots

Machine identities are frequently duplicated across environments and embedded in code, which makes them easy to lose sight of and hard to attribute. Shared API keys and tokens also create a single credential path that can silently connect multiple workloads, developers, or environments. When ownership is unclear, the organisation cannot reliably answer who created the identity, who can revoke it, or what depends on it. That is why inventory quality and ownership are foundational, not optional. Practical implication: without authoritative discovery, lifecycle controls and rotation policies cannot be trusted to cover the full estate.

Practical implication: without authoritative discovery, lifecycle controls and rotation policies cannot be trusted to cover the full estate.

AI agents expand machine identity risk at runtime

AI agents and AI-enabled workloads inherit machine identity weaknesses, but they also increase scale and speed. When an AI system can act across APIs and data sources, broad access becomes a design choice that is easy to over-extend and difficult to review after the fact. The security issue is not that AI is special in itself. It is that autonomous or semi-autonomous execution multiplies the number of identities, sessions, and access paths that must be governed continuously. Practical implication: teams need to treat AI workloads as machine identities with active privilege boundaries, not as ordinary applications.

Practical implication: teams need to treat AI workloads as machine identities with active privilege boundaries, not as ordinary applications.

Threat narrative

Attacker objective: The attacker aims to turn a weakly governed machine identity into durable access that can be reused for data theft, workload abuse, or broader compromise.

1. Entry occurs when an exposed or cloned machine credential gives the attacker a working identity into cloud, API, or workload infrastructure.
2. Escalation follows when standing privilege, shared secrets, or weak ownership let the attacker move from one machine identity to adjacent systems.
3. Impact is reached when the attacker uses that identity foothold to access data, manipulate workloads, or sustain persistence without triggering human-style authentication controls.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Sisense breach — unauthorized GitLab access led to exfiltration of access tokens, API keys and certificates.

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 governance fails when organisations treat non-human access as a subset of user IAM. The article reinforces a common structural error: human authentication patterns are being used as the mental model for service accounts, API keys, certificates, and workloads. That assumption breaks because machine identities do not authenticate, onboard, or retire like people do. The implication is that governance has to be built around machine lifecycle and credential state, not around user-centric access rituals.

Hidden inventory is the real control gap, not just weak rotation. If an organisation cannot see where its machine identities live, it cannot prove ownership, enforce rotation, or determine blast radius. That is why discovery and attribution are upstream controls in OWASP-NHI and NIST-CSF terms. The practical conclusion is that a machine identity programme without authoritative inventory is performing security theatre, not governance.

AI agents turn machine identity sprawl into a runtime governance problem. Once AI systems use credentials to reach APIs and data, the organisation is no longer securing static objects alone. It is securing execution paths that can expand faster than review cycles can track. The practitioner conclusion is that AI workloads should be governed as machine identities with continuously bounded access, not as ordinary software deployments.

Ephemeral credential trust debt: Dynamic secrets reduce exposure time, but they do not remove the governance burden created when identities can be cloned, shared, or created outside controlled lifecycle processes. That debt accumulates when teams assume short-lived credentials automatically equal controlled identities. The implication is that lifecycle discipline and ownership remain necessary even when the credential itself is temporary.

Machine identity governance is now a cross-domain control plane problem. The same identity estate that powers cloud workloads, service integrations, and AI systems sits across IAM, PAM, and lifecycle governance. Organisations that keep these controls siloed will continue to miss shared privilege paths and orphaned identities. The practitioner conclusion is that machine identity management must be treated as an enterprise governance discipline, not a point solution.

From our research:

• 74% say machine identity management complexity has increased significantly in the past two years, according to The Critical Gaps in Machine Identity Management report.
• 59% of companies face greater difficulties auditing machine identities, primarily due to lack of clear ownership and limited visibility.
• The governance response is to move beyond awareness and use the 52 NHI Breaches Analysis to map real failure patterns before they repeat in your environment.

What this signals

Machine identity sprawl is now a governance signal, not just an infrastructure by-product. When non-human accounts are created faster than ownership and review can keep up, IAM teams inherit a programme problem that stretches across discovery, PAM, and lifecycle governance. The operational question is no longer whether machine identities exist, but whether they are still governed once they are cloned into production.

According to our research, 61% rely on spreadsheets or manual tracking for machine identity management. That is a strong indicator that the control plane is lagging the attack surface, because manual methods cannot sustain inventory, rotation, and offboarding across cloud estates. The reader should treat that as a trigger to prioritise authoritative discovery and process automation rather than another policy review.

Ephemeral credentials shrink exposure windows, but they do not erase identity debt. If credentials are temporary while ownership, approval, and revocation remain manual, the programme is still carrying hidden risk. Security leaders should prepare for machine identity management to converge with workload identity, secrets hygiene, and AI governance in a single operating model.

For practitioners

• Build an authoritative machine identity inventory Continuously discover service accounts, API keys, certificates, tokens, and AI workload identities across cloud and on-premises environments. Tie each identity to an owner, a system dependency, and a revocation path so that hidden credentials are not left outside governance.
• Replace shared secrets with scoped credentials Eliminate long-lived shared API keys and passwords where possible, and move to scoped, time-bound credentials that are easier to attribute and revoke. Apply the same discipline to machine and AI workloads that developers often reserve for human access.
• Enforce machine identity lifecycle ownership Define who can approve, rotate, and retire every non-human identity. If a machine identity can be created in code, it must also be governed through onboarding, offboarding, and periodic review that matches the system it supports.
• Apply least privilege to workload and AI access paths Audit which APIs, data stores, and internal services each machine identity can reach, then remove standing access that is not required for the current task. This is especially important where AI agents or automation can expand access faster than human review cycles.

Key takeaways

• Machine identities are governed poorly when organisations rely on human IAM assumptions for non-human access.
• The evidence points to a scale problem, with hidden inventory, manual tracking, and unclear ownership driving most of the risk.
• Practitioners need authoritative discovery, lifecycle ownership, and least-privilege controls that match machine behaviour, not user behaviour.

Key terms

• Machine Identity: A machine identity is a non-human credentialed subject such as a service account, API key, token, certificate, or workload identity. It represents software, infrastructure, or an AI system that needs authenticated access, and it must be governed with ownership, lifecycle, and revocation controls distinct from human accounts.
• Lifecycle Management: Lifecycle management is the process of governing an identity from creation through use, review, rotation, and retirement. For machine identities, it must account for code-driven creation, hidden dependencies, and offboarding that can happen faster than human review cycles can observe.
• Standing Privilege: Standing privilege is access that remains continuously available rather than being issued only when needed. For machine identities, it creates a persistent attack path because shared secrets or long-lived credentials can be reused, cloned, or abused without a fresh approval step.
• Workload Identity: Workload identity is the identity assigned to an application, service, container, or automated process so it can authenticate to other systems. It matters because workload access often spans APIs and data stores, and weak ownership or poor rotation quickly turns it into an enterprise-wide control gap.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Delinea: Cybersecurity Awareness Month PSA on machine identities. Read the original.