What Is a Non-Human Identity in Cloud Security?

What is a non-human identity in cloud security?

Key takeaways

• Nearly 1 in 5 organizations have experienced a security incident related to non-human identity security.
• Most cloud breaches involve compromised credentials or excessive permissions, making non-human identity security a core cloud security discipline.
• AI agents introduce new identity challenges because every autonomous action depends on machine credentials, permissions, and access relationships.
• Effective security requires organizations to discover identities, understand permissions and risk, and enforce lifecycle controls continuously.

What role do non-human identities play in cloud security?

If your cloud environment relies on service accounts, API keys, Kubernetes workloads, or AI agents, it already depends on non-human identities (NHIs). These identities enable applications, workloads, services, and AI agents to authenticate, access systems, and interact with cloud resources without human intervention.

Non-human identity management is the practice of discovering, governing, and securing those identities as they evolve across cloud environments. Effective programs should:

• Discover service accounts, API keys, OAuth tokens, secrets, certificates, workload identities, and other NHIs.
• Understand the permissions, ownership, and potential blast radius associated with each identity.
• Enforce least privilege through continuous access reviews and credential management.
• Govern identities throughout their lifecycle as cloud infrastructure and AI workloads evolve.

As cloud automation and AI adoption accelerate, non-human identity management has become a foundational component of modern cloud and identity security.

Quick facts

Common non-human identity risks and how to address them

The growing footprint of a non-human identity

Imagine a company deploys an AI-powered cloud optimization agent to help reduce infrastructure costs. Initially, the agent receives read-only access to AWS billing data. As adoption grows, developers connect it to Kubernetes clusters, GitHub repositories, Terraform pipelines, monitoring platforms, and incident management systems.

Within months, the agent can analyze infrastructure, trigger workflows, and interact with production systems. Each integration adds credentials and permissions. The AI agent remains a single non-human identity, but its access footprint continues to grow.

What is NHI? In this case, the AI agent itself is a non-human identity whose permissions, credentials, and trust relationships enable it to operate across cloud environments.

When security leaders ask, "What can this agent access?", the answer is often difficult to determine. Understanding the identity behind the agent—and the permissions, credentials, and trust relationships that support it—is at the heart of non-human identity security. As organizations strengthen cloud security, governing these identities has become essential to reducing identity risk.

Why machine identities now dominate cloud environments

When security teams think about identity, they often think about employees logging into applications. The real question to ask is "What is NHI, and how is it creating risk?"

A typical cloud environment contains thousands of machine identities operating continuously behind the scenes, including:

• AWS IAM roles
• Azure Entra service principals
• Google Cloud service accounts
• Kubernetes workload identities
• OAuth and API tokens
• AI agent credentials
• Secrets and certificates

Secrets sprawl is a growing problem. In its State of Secrets Sprawl Report 2026, GitGuardian reported finding 28.65 million new hardcoded secrets in public GitHub commits, a 34% increase from the prior year.

Our cloud optimization agent illustrates how quickly identity complexity can grow. What began as a read-only workload now interacts with cloud infrastructure, source code repositories, deployment pipelines, and operational systems. Each new integration creates credentials, permissions, and trust relationships that security teams must govern.

What happens when a non-human identity is compromised?

A non-human identity is not inherently dangerous. Risk emerges when organizations lose visibility into its permissions, credentials, and access relationships. Reflecting that reality, the OWASP Non-Human Identity Top 10 identifies excessive privileges, secret exposure, and identity lifecycle failures among the most common NHI security risks.

Every non-human identity raises three critical questions: What is it? What can it access? What happens if it is compromised?

The third question often determines the true level of risk. If an attacker compromises a service account with administrative permissions, they inherit the same access as that identity. This potential impact is what security teams must continuously measure and reduce. Yet many organizations still struggle to do so. Around 60% of IT and security professionals lack confidence in their organization's ability to adequately secure non-human identities, highlighting the need for continuous discovery, permission analysis, and lifecycle governance.

A real-world example of identity risk

In late 2024, attackers compromised a BeyondTrust infrastructure API key used by its Remote Support SaaS service. The compromised key enabled unauthorized access to customer environments, including systems used by the U.S. Treasury Department. The incident also impacted at least 17 other SaaS customers and required immediate revocation of the compromised credentials.

The incident illustrates a core non-human identity security challenge: a single machine credential can provide access far beyond its original purpose. When organizations lack visibility into machine identities, permissions, and trust relationships, the blast radius of a compromised credential can be difficult to predict and contain.

A machine identity may begin with limited permissions but gradually accumulate access to source code repositories, cloud resources, and operational systems. Without continuous governance, organizations often don't realize how much access has accumulated until after an incident.

What happens when machine identities are left unmanaged?

Credential-based attacks remain one of the most common causes of breaches. According to Verizon's Data Breach Investigations Report 2026, credential abuse continues to be a leading attack vector across cloud and enterprise environments. But the impact extends beyond security incidents.

Operational impact

Unused service accounts accumulate over time as environments evolve. Cloud migrations, infrastructure changes, and application updates regularly create dormant identities that are rarely reviewed or removed. As identity inventories grow, so does the likelihood of overlooked credentials. Research by the Non-Human Identity Management Group shows that 91% of former employee tokens remain active, illustrating how lifecycle failures can leave unnecessary access in place long after it is needed.

Governance impact

Compliance frameworks increasingly require organizations to demonstrate effective access control and identity governance. Unknown machine identities make access reviews difficult and, in some cases, impossible.

AI governance impact

Agentic AI adds another layer of complexity because every AI agent operates through identities, permissions, tokens, and credentials that must be continuously governed. AI agents and automated services are creating new digital identities 10 times faster than traditional human user provisioning, making it increasingly difficult for security teams to maintain visibility. Agents that are initially approved for a limited use case often accumulate additional permissions through new integrations and automation workflows, expanding their effective access over time.

That challenge is already having an impact. In IBM's Cost of a Data Breach 2025 report, 13% of organizations reported breaches involving AI models or applications, while another 8% were unsure whether they had been compromised.

Best practices for reducing non-human identity sprawl

Leading security teams treat non-human identities as first-class security assets. Visibility is only the first step. As non-human identities multiply across cloud, SaaS, and AI environments, organizations must continuously discover identities, understand permissions and risk, and enforce governance. These best practices help put that approach into practice.

• Continuously discover non-human identities. Maintain a complete inventory of service accounts, API keys, OAuth tokens, secrets, workload identities, and AI agent credentials across cloud environments.
• Map permissions and trust relationships. Identify what each identity can access, how identities connect across systems, and the potential blast radius if a credential is compromised.
• Apply least privilege continuously. Remove unnecessary permissions as applications, integrations, and AI agents evolve to prevent privilege accumulation.
• Govern identities throughout their lifecycle. Rotate secrets, enforce credential expiration, remove unused identities, and assign ownership to every non-human identity.
• Monitor for unusual behavior. Establish behavioral baselines and investigate unexpected authentication patterns, access requests, or API activity.

These practices align with guidance from NIST's Digital Identity Guidelines and help organizations continuously discover identities, understand permissions and risk, and enforce governance as cloud and AI environments grow.

How Token Security approaches non-human identity security

Intent-based security continuously discovers non-human identities, understands permissions and risk, and enforces governance throughout the identity lifecycle.

The challenge

Organizations struggle to answer three basic questions about machine identities: What exists, what can it access, and what happens if it is compromised?

Traditional identity and access management (IAM) and privileged access management (PAM) technologies were designed primarily for human users. They often lack the visibility required to understand machine identity relationships across modern cloud environments.

In the cloud optimization scenario, security teams need to understand not only that the AI agent exists, but also every credential it uses, every permission it has accumulated, and every resource it can reach.

The approach

Token Security applies a machine-first security approach built on three essential pillars:

• Discovery to identify the AWS roles, GitHub tokens, and Kubernetes identities associated with the cloud optimization agent.
• Entitlement mapping to show which repositories, cloud resources, and operational systems those identities can reach.
• Blast-radius analysis to reveal what an attacker could access if one of those credentials were compromised.

This helps organizations discover identities, understand permissions and risk, and enforce security controls continuously.

The outcome

Organizations gain visibility into machine identities, reduce excessive permissions, shorten remediation timelines, and improve governance across service accounts, workloads, secrets, and AI agents.

Closing the gap between visibility and governance of non-human identities

Understanding non-human identity risk is only the beginning. Security teams need the context to prioritize remediation, reduce blast radius, and govern machine identities effectively. These experiences show how greater visibility can lead to stronger control and better security outcomes.

Gaining visibility into machine identity risk

As cloud environments grow, machine identities often accumulate permissions, credentials, and access relationships that are difficult to track manually. Without visibility into those relationships, security teams may struggle to identify which identities pose the greatest risk or where to begin remediation efforts.

"Token Security has enhanced our security by providing us accurate and relevant visibility into machine identity risks, something I have yet to see from other vendors. It's the first service I've seen that delivers on the widely made claim of finding machine identity risk. It provides us with the right amount of detail to mitigate issues without burdening us with tens of rows of useless alerts."
— Jonathan Jaffe, CISO at Lemonade

This experience highlights a core challenge in NHI security: visibility must be paired with context about permissions, access paths, and blast radius to drive effective remediation.

Governing AI agents before they become a blind spot

AI agents introduce a new category of non-human identity. As organizations deploy custom GPTs and autonomous workflows, security teams need visibility into how those agents operate, what they can access, and whether their permissions remain appropriate over time.

"Token Security gives us visibility we simply didn't have before. We can now automatically identify and control custom GPT agents running in our environment and ensure the required security level. Knowing that no AI agent is operating beyond our oversight means we can confidently accelerate our AI adoption."
— Tamir Ronen, Global CISO at HiBob

This example shows why AI security increasingly depends on identity security. Governing the identities behind AI agents helps maintain accountability and control as adoption grows.

Why visibility is no longer enough for AI governance

AI governance begins with visibility, but its goal is reducing identity risk over time. As AI agents and cloud automation proliferate, organizations must continuously discover non-human identities, evaluate their permissions, and govern them throughout their lifecycle. Those controls help prevent a common problem: 60% of non-human identities are reused across multiple applications, creating unnecessary concentration of privilege and increasing the impact of a single compromised identity.

Identity has become the control plane for modern cloud security. Organizations that continuously discover non-human identities, understand permissions and risk, and enforce governance will be better positioned to adopt AI without losing control of access.