Non-human identity security risks are rising in AI enterprises

At a glance

What this is: This analysis argues that non-human identity security risks are rising because AI-driven enterprises are creating, using, and forgetting machine identities faster than human-centric IAM can govern them.

Why it matters: IAM, NHI, and PAM teams need to treat machine identities as a core governance domain because overprivilege, orphaned accounts, and secret sprawl now sit on the same control plane as human access.

By the numbers:

• The silent majority of identities outnumber humans by a staggering 45 to 1 ratio.
• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.

👉 Read Token Security's analysis of rising non-human identity security risks in AI enterprises

Context

Non-human identity security risks are what happen when machine credentials, service accounts, tokens, and bots are treated as infrastructure details instead of governed identities. In AI enterprises, that mistake becomes expensive because identities are created continuously, used at machine speed, and often remain active long after the workload that created them is gone.

The core IAM problem is not just volume. It is that machine identities bypass the assumptions built into human IAM, including interactive authentication, predictable lifecycles, and manual review cycles. Once autonomous AI agents start creating their own credentials and sub-agents, the governance model shifts from access administration to identity control at runtime.

Key questions

Q: What breaks when non-human identities are not centrally governed?

A: Machine identities accumulate faster than teams can review them, so orphaned accounts, overprivileged roles, and exposed secrets become normal rather than exceptional. The result is a larger blast radius, weaker accountability, and more paths for attackers to abuse valid credentials without needing to break interactive authentication controls.

Q: Why do non-human identities increase cloud attack risk so quickly?

A: They increase risk because they are numerous, often poorly owned, and frequently granted broad access to keep systems running. Once a credential is exposed, attackers can use it immediately from anywhere, and machine accounts often lack the human-style friction that slows misuse or triggers response.

Q: What do security teams get wrong about service account governance?

A: They often treat service accounts as technical plumbing rather than governed identities. That leads to missing ownership, stale permissions, and secrets left in code or logs. A service account should be reviewed like any other privileged identity, with lifecycle, scope, and revocation tracked explicitly.

Q: How should teams reduce the blast radius of compromised machine credentials?

A: Reduce privilege to the minimum observed use, replace static secrets with ephemeral credentials where feasible, and monitor for anomalous machine behaviour in real time. The goal is to make any stolen credential short-lived, narrowly scoped, and easy to detect before it can spread across systems.

Technical breakdown

Why machine identities scale differently from human identities

A non-human identity is any credentialed machine entity that authenticates to a system without being tied to a person. That includes service accounts, API keys, workload identities, bots, and AI agents. The technical issue is multiplicative scale. Microservices, containers, serverless functions, and pipelines all need identity, and cloud-native systems create far more machine accounts than human accounts. Human IAM tools were designed around stable users, not identity populations that can appear, disappear, and replicate in seconds.

Practical implication: inventory machine identities as a live population, not a static directory list.

How AI agents change NHI governance

Agentic AI changes NHI governance because the identity is no longer just executing a script. A probabilistic agent can choose tools, initiate actions, and create downstream identities based on its own reasoning chain. That means access is not only granted once at provisioning time. It can be expanded, combined, and consumed dynamically during runtime, which breaks the assumption that privilege can be fully understood from the original request.

Practical implication: evaluate AI agent access as a runtime governance problem, not a provisioning-only one.

Secrets sprawl, overprivilege, and orphaned accounts

The most common NHI failure modes are simple but dangerous. Hardcoded secrets in repositories, overprivileged service accounts, and orphaned credentials after workloads are retired all extend attacker dwell time and widen blast radius. These problems persist because machine identities often lack clear ownership, and manual cleanup does not keep pace with DevOps and AI-driven creation. In practice, the attack path usually starts with a valid credential and ends with abuse of trust that no one is actively watching.

Practical implication: tie every machine credential to an owner, a use case, and a revocation path.

Threat narrative

Attacker objective: The attacker objective is to use valid machine trust to reach sensitive cloud data, privileged infrastructure, or production pipelines before defenders detect the abuse.

1. Entry occurs when attackers harvest exposed machine credentials from repositories, logs, CI/CD systems, or public code.
2. Escalation follows when those valid identities already carry broad permissions, allowing the attacker to move laterally or access sensitive cloud resources without triggering human-style MFA controls.
3. Impact is the exfiltration of data, modification of infrastructure, or supply chain compromise at machine speed before the identity is noticed or revoked.

Breaches seen in the wild

• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.
• MongoBleed breach — MongoBleed exposed secrets across 87K MongoDB servers.

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

NHI Mgmt Group analysis

Non-human identity governance has become the control plane for AI-ready enterprises. The article is right to frame NHI sprawl as a structural problem, not a niche hygiene issue. When service accounts, tokens, and AI agents outnumber humans by orders of magnitude, the identity estate stops being a directory and becomes a production attack surface. Practitioners should treat machine identity governance as core infrastructure risk, not an adjacent IAM task.

Ephemeral credential trust debt is the right concept for this phase of the market. The article shows how organisations keep creating machine access faster than they retire, scope, or observe it. That creates trust debt: access that remains valid longer than its operational purpose, even when the workload is gone or the agent has moved on. The implication is that lifecycle failure, not just secret exposure, is now the durable source of NHI risk.

Static IAM assumptions do not survive autonomous access decisions. Least privilege is designed for conditions where access needs can be reasoned about at provisioning time. That assumption fails when an agent can generate sub-agents, expand its own workflow, and consume tools based on runtime reasoning. The implication is that access governance must be rethought around machine-paced execution rather than human-paced approval cycles.

Machine identity failure is now a cross-domain problem spanning IAM, PAM, and supply chain security. The article ties CI/CD tokens, API keys, cloud roles, and AI agent behaviour into one risk pattern. That matters because the same credential can be both a build-system secret and a privileged path into production. Practitioners should stop managing these as separate queues and start managing them as one governed identity fabric.

Identity blast radius is the most useful operational lens for NHI risk. The practical question is no longer whether a machine identity exists, but how far it can move if stolen, misused, or overextended. That lens is stronger than raw inventory counts because it connects privilege scope, ownership gaps, and runtime observability. Teams should prioritise the identities whose compromise would unlock the widest downstream impact.

From our research:

• Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks, according to The 2024 ESG Report: Managing Non-Human Identities.
• From our research: Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, according to the same report.
• For a deeper read: Guide to the Secret Sprawl Challenge expands the operational side of hardcoded secrets, pipeline exposure, and remediation patterns.

What this signals

Ephemeral credential trust debt: the organisations that can afford to treat machine identity as an audit problem are disappearing, because cloud and AI operations now create credentials faster than quarterly reviews can govern them. With two-thirds of enterprises already reporting a successful cyberattack from compromised NHIs, the governance model needs to move from periodic cleanup to continuous control. Teams should expect identity inventory, ownership, and revocation to become board-level operating metrics.

A practical programme shift is to align machine identity control with runtime observability and Zero Trust policy enforcement, not just static provisioning. The NIST Cybersecurity Framework 2.0 is a useful anchor here because it forces teams to connect identify, protect, detect, respond, and recover around the same identity surface. That means service accounts, AI agents, and automation tokens should be treated as governed production actors, not background objects.

Where AI agents are in scope, the programme question changes again: can you prove what the agent was allowed to do at the moment it acted? If not, the organisation is still relying on assumptions designed for human-paced approval cycles. The transition to machine-speed access makes identity accountability, not raw access volume, the decisive control objective.

For practitioners

• Build a live inventory of machine identities Scan cloud IAM, code repositories, CI/CD systems, and logs to identify service accounts, tokens, workload identities, and bots that are active but poorly governed.
• Bind each NHI to an owner and lifecycle state Require a named business or technical owner, a clear use case, and an explicit revocation path for every machine credential so orphaned identities can be retired quickly.
• Right-size permissions using observed usage Compare actual permission use against granted scope and remove roles that are never exercised, especially administrator-level access granted for convenience.
• Automate rotation for exposed or long-lived secrets Replace static keys with ephemeral credentials where possible and enforce automated rotation for secrets that still must exist, especially those embedded in pipelines or logs.
• Monitor machine behaviour for impossible access patterns Alert on service accounts, APIs, or AI agents that suddenly access new systems, change timing, or move beyond their normal resource boundaries.

Key takeaways

• Non-human identities are no longer a side issue. They now represent a primary cloud and AI attack surface because they scale faster than human governance.
• The evidence is already visible in incident patterns: compromised machine credentials regularly lead to repeated attacks, broad access abuse, and delayed detection.
• The practical response is to inventory, own, scope, rotate, and monitor machine identities as living production actors, not as technical afterthoughts.

Key terms

• Non-Human Identity: A non-human identity is a credentialed digital entity that authenticates to systems without representing a person. It includes service accounts, API keys, workload identities, bots, and AI agents. The governance challenge is that these identities can scale quickly, operate continuously, and outlive the workloads that created them.
• Ephemeral Credential: An ephemeral credential is a short-lived identity secret issued for a narrow task or session. It reduces the window in which an attacker can reuse a stolen credential, but it only works if issuance, scope, and revocation are automated and tied to the actual workload or agent behaviour.
• Identity Blast Radius: Identity blast radius is the amount of access and downstream impact a single identity can unlock if it is compromised or misused. For non-human identities, it is a better risk measure than account count because it reflects privilege scope, system reach, and the speed of propagation across connected services.
• Orphaned Identity: An orphaned identity is a credential or account that remains active after the workload, pipeline, vendor relationship, or owner has changed. These identities are dangerous because they are often valid, unmonitored, and forgotten, making them attractive entry points for attackers seeking trusted access.

What's in the full article

Token Security's full blog covers the operational detail this post intentionally leaves for the source:

• A fuller breakdown of why AI agents create and consume identities differently from traditional automation
• Examples of how overprivileged service accounts and API keys expand the attack surface in cloud-native estates
• Practical guidance on inventorying, rotating, and revoking machine credentials across distributed environments
• The article's own framing of the relationship between NHI sprawl, supply chain exposure, and AI-driven access patterns

👉 Token Security's full post covers the NHI, AI agent, and cloud governance detail behind the risk

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.