Best practices for securing non-human identities and AI agents

At a glance

What this is: This is a practical guide to securing non-human identities, showing why service accounts and AI agents need dedicated governance instead of human IAM controls.

Why it matters: It matters because IAM, PAM, and IGA teams now have to govern identities that never leave the environment, often hold privileged access, and can outlive the workloads they support.

By the numbers:

• CyberArk's 2025 Identity Security Landscape study reported 82 machine identities per human identity across the surveyed organizations.
• The same study found that 42% of machine identities held privileged or sensitive access.
• CyberArk's 2025 State of Machine Identity Security research found that 50% of surveyed security leaders reported security breaches linked to compromised machine identities.

👉 Read Unosecur's guide to securing non-human identities and AI agents

Context

Non-human identity security is the discipline of governing accounts, keys, tokens, certificates, and agent identities that act without a person at the keyboard. The primary problem is not just credential storage. It is that ownership, privilege, and lifecycle often drift away from the workload that created the identity, which makes standard employee IAM processes too blunt for machine access.

This article focuses on why service account security and machine identity management need explicit discovery, ownership, credential, and runtime controls. That framing is directly relevant to NHI governance because the same account can persist long after a team changes, a workload is retired, or access requirements shift.

The article also extends the discussion to AI agents, which should be treated as identifiable non-human identities when they authenticate to tools or enterprise systems. That makes the guide a useful bridge between classic machine identity controls and the newer governance problems created by delegated agent access.

Key questions

Q: What breaks when organisations manage service accounts like human users?

A: Service accounts do not behave like people, so human IAM controls miss the real risks. They run continuously, use static or long-lived credentials, and often lack a clear owner or review cycle. When teams apply human models to machines, they tend to overgrant access, miss orphaned credentials and overlook scope drift until a breach or outage exposes it.

Q: Why do cloud environments increase non-human identity risk?

A: Cloud environments increase non-human identity risk because automation, APIs, and service accounts multiply faster than manual review can keep up. Those identities often carry persistent credentials and broad permissions, which makes them difficult to track and easy to over-extend. If they are not inventoried and rotated, they become durable access paths for attackers.

Q: How do security teams know if NHI governance is working?

A: Good NHI governance shows up in fewer shared credentials, shorter secret lifetimes, clear ownership for every machine identity, and offboarding that actually removes unused accounts. If teams still find tokens in chat, code, or ticketing systems, or cannot prove who owns a service account, the programme is not under control.

Q: What is the difference between managing secrets and governing NHIs?

A: Secrets management protects the credential itself, while NHI governance manages the identity behind it. That means defining ownership, allowed access, lifecycle, runtime behaviour, and retirement. A well-managed secret can still represent unacceptable risk if the account behind it is overprivileged or unowned.

Technical breakdown

Why human IAM controls break for machine identities

Human IAM assumes a person owns the account, can be questioned about purpose, and eventually leaves the organisation. Machine identities do not follow that model. They can be created by one team, used by another, and still authenticate long after the original business purpose has changed. That is why NHI governance must start with identity context, not just login control. The hard part is not proving that an account exists. It is proving why it exists, what workload depends on it, and who is accountable when the workload changes.

Practical implication: Build a governed inventory that links each NHI to a workload, purpose, owner, and environment.

Short-lived credentials and static secret exposure

Machine credentials become dangerous when they behave like permanent infrastructure. API keys, passwords, and certificates can leak into code, logs, images, and configuration files, then remain usable for months. The security issue is not only exposure, but duration. Short-lived credentials, federated access, and managed identities reduce the time an attacker can exploit a stolen secret. For NHIs, the question is whether compromise has an expiration date, not whether a secret store exists.

Practical implication: Replace long-lived secrets with expiring credentials wherever the platform supports it.

Behavioural monitoring for service accounts and AI agents

A machine identity should not only be authenticated, it should be observable in context. Normal behaviour for a payroll integration is not normal behaviour for a CI/CD identity, and neither pattern matches an AI agent that suddenly starts changing IAM policy. Monitoring source, destination, credential changes, privilege use, and resource access gives identity teams a way to spot compromise after authentication. That is especially important where the identity can act continuously and at machine speed.

Practical implication: Define behavioural baselines for each NHI type and alert on abnormal resource, privilege, or timing patterns.

Threat narrative

Attacker objective: The objective is to turn a machine identity into durable access to cloud resources, data, or administrative functions without triggering human review.

1. entry: Attackers gain access when exposed machine credentials, hardcoded keys, or stale service account secrets are found in code, logs, or cloud environments.
2. escalation: They abuse standing privilege, inherited permissions, or weak ownership to expand access beyond the original workload.
3. impact: They use the compromised identity to reach production systems, sensitive data, or cloud control planes at machine speed.

Breaches seen in the wild

• Meta AI Instagram Account Takeover — 20,225 Instagram accounts hijacked via compromised Meta AI support chatbot with overprivileged access.
• CoPhish OAuth Token Theft via Copilot Studio — CoPhish campaign exploits Microsoft Copilot Studio agents to steal OAuth tokens via AI-assisted phishing.

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

NHI Mgmt Group analysis

Identity programmes fail when they treat machine accounts like people. Human IAM assumes a natural owner, a joiner-mover-leaver lifecycle, and a person who can explain why access exists. That assumption breaks for service accounts, workload identities, and bots that outlive teams and applications. The implication is that NHI governance must be built around workload context, not employee process.

Static secret dependency creates identity trust debt. The longer an API key, password, or certificate remains valid, the more security debt accumulates between the time it was issued and the time it is still usable. This is not just credential sprawl. It is a governance model that quietly accepts stale authority as normal. Practitioners should treat long-lived machine credentials as a structural risk indicator, not a storage problem.

Privilege matters more than credential form. A perfectly stored certificate is still dangerous if the underlying service account can modify other identities, access production data, or cross trust boundaries. The article correctly centres effective access rather than assigned access. That is the right lens for NHI security, because excess privilege turns every credential compromise into a control-plane event.

Machine identity lifecycle drift: This guide points to a common failure mode where creation, use, and retirement are no longer tied to the workload lifecycle. When the account remains active after the application changes or disappears, accountability collapses. That is the governance gap practitioners need to eliminate, because orphaned access is often indistinguishable from legitimate access until it is abused.

AI agents extend the NHI problem into delegated autonomy. Once an agent can authenticate to tools, invoke APIs, and act on behalf of a human, the governance question is no longer just what secret it uses. It is what authority it inherited, what it can delegate, and how revocation follows the human it serves. Practitioners should re-evaluate NHI controls for delegation chains, not isolated credentials.

From our research:

• 69% of organisations now have more machine identities than human ones, according to The Critical Gaps in Machine Identity Management report.
• 57% of organisations lack a complete inventory of their machine identities, which means ownership and exposure are still being inferred rather than governed.
• For a deeper operational lens, see Guide to the Secret Sprawl Challenge for how credential sprawl turns into lifecycle risk.

What this signals

Machine identity programmes are moving from inventory work to governance work. With 69% of organisations already running more machine identities than human ones, the control problem is no longer scale alone. Security teams need ownership, lifecycle, and runtime context to keep NHI access from becoming invisible operational debt.

Secret sprawl is only one symptom of a broader identity design problem. The real issue is that machine access often persists after the workload changes, so decommissioning and review must be wired into platform operations rather than left to ticket-driven cleanup. The Guide to the Secret Sprawl Challenge is useful here because it frames the credential layer as part of a lifecycle system, not a vaulting exercise.

AI agents sharpen the need for governed delegation chains. As agents begin to act across tools and systems, teams will need to decide whether the identity is managed as a conventional NHI, a delegated actor, or a privileged extension of a human workflow. That decision will shape how ownership, revocation, and monitoring are implemented across the programme.

For practitioners

• Inventory every non-human identity with relationship context Map each service account, API key, certificate, bot, workload identity, and agent to its creator, workload, environment, and accountable owner. Record what it can reach and what system issued its credentials so orphaned access becomes visible before incident response does.
• Eliminate long-lived credentials where federation is available Replace persistent passwords and static API keys with short-lived credentials, workload identity, or federated access. Where static secrets remain necessary, automate rotation and verify that expiry, revocation, and logging are enforced consistently across environments.
• Review privilege as effective access, not assigned role Audit inherited permissions, wildcard entitlements, trust relationships, and cross-account access for every high-value NHI. Focus on what the identity can actually do in production, because machine accounts often retain more reach than the role name suggests.
• Baseline runtime behaviour for each identity class Define normal source, destination, frequency, and resource patterns for service accounts, CI/CD identities, and AI agents. Alert on policy changes, unusual database access, or activity that falls outside the workload's expected operating window.
• Tie decommissioning to workload retirement Require identity deletion, credential revocation, and ownership review when the workload, integration, or agent is retired. Do not rely on manual cleanup after a project ends, because dormant non-human identities are a standing access path.

Key takeaways

• Non-human identities need their own governance model because human IAM assumptions do not hold once access is workload-driven and machine-paced.
• Long-lived credentials, weak ownership, and standing privilege combine to create the most persistent NHI exposure paths.
• The right control model links discovery, ownership, lifecycle, and runtime behaviour so machine access can be governed before it becomes incident response material.

Key terms

• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
• 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.
• 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.
• Credential Lifecycle: Credential lifecycle is the process of issuing, rotating, expiring, and revoking secrets, certificates, and tokens across their usable life. For non-human identities, lifecycle discipline is the core control that separates temporary access from persistent exposure.

What's in the full article

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

• Step-by-step discovery coverage across cloud IAM, SaaS, CI/CD, Kubernetes, databases, and secrets stores
• Practical examples of ownership mapping for service accounts, workload identities, and AI agents
• Guidance on how to baseline normal machine behaviour and flag abnormal authentication or privilege use
• Examples of how to tie decommissioning and credential revocation to workload retirement

👉 Unosecur's full post covers the practical steps for discovery, ownership, privilege, and lifecycle control.

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 building or maturing identity security practice, it is worth exploring.