Machine identity security gaps are widening as AI agents scale

At a glance

What this is: This is an analysis of machine identity security and the growing exposure created by over-privileged service accounts and AI agents.

Why it matters: It matters because IAM, PAM, and security teams need to govern non-human identities with the same discipline used for humans, or risk lateral movement that blends into legitimate traffic.

By the numbers:

• 109:1.
• Organisations anticipate 85% growth in AI agent adoption over the next year.

👉 Read Zero Networks' analysis of machine identity security for service accounts and AI agents

Context

Machine identity security is about governing software identities such as service accounts, API keys, workload identities, and AI agents. The problem is that these identities often carry standing access, little review, and enough reach to move laterally without looking unusual to detection tools.

As AI agent adoption grows, the governance gap stops being theoretical and becomes a programme design issue for IAM, PAM, and network segmentation teams. The central question is no longer whether non-human identities exist, but whether they are being discovered, scoped, and constrained as identities rather than as background infrastructure.

Key questions

Q: What breaks when hybrid-cloud service accounts are over-privileged?

A: Over-privileged hybrid-cloud service accounts let attackers reuse trusted access paths instead of escalating from scratch. That turns a compromised server into a bridge into cloud resources, expands blast radius, and makes lateral movement easier to hide. The risk is highest when sync, admin, and workload permissions are blended into one identity.

Q: Why do non-human identities complicate zero trust architecture?

A: Because zero trust assumes access can be verified continuously, yet many machine identities are created for automation, reused widely, and left in place long after their original purpose ends. The result is standing trust hidden inside infrastructure. Teams need continuous verification plus lifecycle enforcement to make zero trust real for machines.

Q: What do security teams get wrong about AI access risk?

A: Many teams focus on the model while ignoring the identity path that reaches it. If a service account or token can invoke AI infrastructure, then that credential becomes the real control point. The mistake is treating AI risk as a model problem instead of an access governance problem.

Q: Who is accountable when a machine credential is abused?

A: Accountability should sit with the team that owns the workload, the identity lifecycle, and the connected business process, not with security alone. In regulated environments, that usually means engineering, platform, and IAM teams share responsibility for discovery, rotation, and offboarding while compliance verifies that the process is repeatable.

Technical breakdown

Why standing access makes machine identities hard to govern

Service accounts and other machine identities are usually provisioned for continuous operation, not human-style login events. That means they often retain credentials, tokens, or entitlements long after the original need has changed. When those identities are over-privileged, every authenticated action looks legitimate, which makes detection harder and blast radius larger. The security problem is not just credential exposure. It is the combination of long-lived access, weak review cycles, and broad entitlement sets that accumulate over time.

Practical implication: map where standing access exists and remove any entitlement that is not tied to an observed operational need.

How AI agent identity changes the trust model

AI agents are not just another workload. They can chain tools, follow dynamic execution paths, and expand their effective reach as tasks change. That creates a governance problem that is different from static service-account management because policy must account for runtime behaviour, not just provisioning-time permissions. If the agent can decide which tools to invoke and when to invoke them, the access model has to assume scope drift and tool misuse as part of normal operation rather than as edge cases.

Practical implication: govern AI agents as first-class identities with explicit tool boundaries and monitored execution scope.

Why network-level enforcement still matters for NHI

Identity controls alone do not stop lateral movement if the network allows an identity to reach too much once authenticated. Machine identity security works best when access is constrained by identity, logon type, and reachable assets rather than by IP location or static segment rules. That is why identity-driven segmentation is increasingly used to reduce the damage from compromised service accounts and AI agents. The goal is to make legitimate authentication less useful to an attacker by narrowing what the identity can actually reach.

Practical implication: pair identity governance with network-level allowlisting so compromise does not automatically become lateral movement.

Threat narrative

Attacker objective: The attacker’s objective was to convert a low-privilege foothold into control of higher-value internal systems by abusing over-privileged non-human access.

1. Entry occurred when attackers exploited a vulnerable NetScaler appliance and reached stored service account credentials in the compromised environment.
2. Escalation followed because a read-only LDAP bind account had accumulated more access than necessary, which allowed the attackers to pivot through RDP and alter a domain admin password.
3. Impact came when lateral movement turned a single foothold into broader domain control and business disruption.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Meta AI Instagram Account Takeover — 20,225 Instagram accounts hijacked via compromised Meta AI support chatbot with overprivileged access.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Standing privilege is the core machine-identity failure mode. Service accounts and AI agents are most dangerous when access outlives the business need that created it. That is not a visibility problem alone. It is a governance failure in how organisations treat non-human access as persistent infrastructure instead of scoped identity.

Machine identity security and agentic AI governance now overlap, but they are not the same discipline. Service accounts fail through accumulation, while AI agents fail through runtime scope drift and tool chaining. Teams that apply one control model to both will miss the difference between long-lived entitlement creep and autonomous execution behaviour.

Identity-first segmentation is now a control boundary, not an optimisation. When authenticated machine traffic blends into normal operations, defenders lose the ability to distinguish legitimate reach from abuse. The practical conclusion is that every non-human identity needs explicit reach constraints, not just a credential and a policy document.

Shadow AI creates the same governance problem that unmanaged service accounts created a decade ago. The difference is velocity. If security teams cannot discover what agents exist, what they can access, and who owns them, they will repeat the same lifecycle mistakes that made service account sprawl so hard to unwind.

Machine identity trust must be reviewed as a lifecycle issue, not a deployment issue. The article’s central lesson is that discovery, scoping, and policy enforcement only work when they are tied to continuous governance. Practitioners should treat every non-human identity as something that must be owned, reviewed, and retired on a defined schedule.

From our research:

• Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them, according to Ultimate Guide to NHIs.
• 79% of organisations have experienced secrets leaks, and 77% of those incidents resulted in tangible damage, according to Ultimate Guide to NHIs.
• For a broader breach pattern view, the 52 NHI Breaches Analysis shows how over-privilege and stale credentials repeatedly turn access into impact.

What this signals

Standing privilege debt: the longer a service account or AI agent keeps access beyond its original purpose, the more likely that access becomes indistinguishable from an attacker’s legitimate internal actions. That is why identity programmes need lifecycle ownership, not just inventory.

With 79% of organisations having experienced secrets leaks according to the Ultimate Guide to NHIs, the control conversation has to move beyond discovery into rotation, revocation, and offboarding discipline.

The next maturity step is to align NHI governance with network enforcement and breach learning. Teams that combine identity ownership, segmentation, and incident-informed policy design will have a better chance of containing machine-speed misuse before it spreads.

For practitioners

• Discover every service account and AI agent Build continuous discovery that maps identities, tools, and network connections so shadow AI and forgotten service accounts are not invisible to the IAM programme.
• Scope access to observed operational need Use a learning period to identify the actual assets and logon types each identity requires, then remove any privilege that is not supported by observed behaviour.
• Enforce identity-based segmentation Restrict service accounts and AI agents to pre-approved assets and logon types so a compromised credential cannot reach sensitive systems by default.
• Automate policy lifecycle management Replace static IP-centric rules with policy updates that adapt as the environment changes, using a deterministic engine and staged rollout before enforcement.

Key takeaways

• Machine identity security fails most often when standing access is treated as normal instead of temporary.
• The evidence points to a structural governance gap, not a lack of tools, because discovery, rotation, and offboarding remain incomplete in most organisations.
• Practitioners should pair lifecycle control with identity-based segmentation so compromised non-human access cannot easily become lateral movement.

Key terms

• Service Account: A special-purpose account used by applications, automated tools, or services rather than a human user to interact with systems, APIs, and infrastructure. Service accounts are a primary category of NHI and one of the most frequently exploited attack vectors.
• AI Agent Identity: The digital identity used by an autonomous AI agent to authenticate to external systems, APIs, and services. Managing AI agent identities is an emerging and rapidly evolving area of NHI security.
• Standing Privilege: Standing privilege is access that remains active even when no immediate task requires it. For NHI programmes, it is a common failure mode because long-lived credentials and persistent roles create unnecessary exposure. Reducing standing privilege usually means tighter expiry, on-demand access, and clearer review of who or what still needs access.
• Identity-Driven Segmentation: Identity-driven segmentation is a control model that allows or denies communication based on verified identity rather than only on subnet, firewall, or VLAN placement. It is especially useful in operational environments where topology changes frequently and network paths no longer describe trust accurately.

What's in the full article

Zero Networks' full article covers the operational detail this post intentionally leaves for the source:

• Step-by-step discovery and learning-period workflow for mapping every identity, asset, and connection in the environment.
• Operational examples of how a deterministic policy engine turns observed behaviour into least-privilege rules.
• Implementation detail on identity-driven microsegmentation for service accounts and AI agents across changing network conditions.
• The source article's framing of how Zero Networks positions staged rollout and simulation before enforcement.

👉 The full Zero Networks post covers discovery, policy automation, and identity-driven segmentation details.

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, PAM, or NHI programme, it is worth exploring.