What Is Identity Security in the Non-Human Identity Era?

What Is Identity Security? A Modern Definition for the Non-Human Identity Era

Key takeaways

• Identity security now extends beyond employees to include service accounts, API keys, secrets, workloads, and AI agents.
• Effective programs follow a simple sequence: discover identities, understand access, and enforce appropriate controls.
• Only 16% of organizations report high confidence in their ability to detect AI agent-specific threats.
• AI security increasingly depends on identity security because every AI agent operates through credentials, permissions, and machine identities.

Quick answer

What is identity security?

Identity security is the discipline of making sure every user, application, service account, workload, and AI agent has the right access to the right resources at the right time, and nothing more. Identity security helps organizations:

• Verify who or what is requesting access
• Limit permissions to only what is necessary
• Protect and manage credentials throughout their lifecycle
• Detect and respond to identity-related threats before they spread

Identity security risks at a glance

How identity sprawl creates security gaps

Identity security sounds simple: know what identities exist, understand what they can access, and ensure that access remains appropriate over time.

In practice, that becomes harder as organizations adopt cloud services, automation, and AI-driven workflows. Every application, service account, API token, workload, and AI agent adds another identity to govern. As these identities multiply, permissions often accumulate faster than oversight; 99% of cloud identities have excessive permissions, creating a massive, vulnerable attack surface.

Consider a cloud engineering team using GitHub Actions to automate deployments. Initially, a service account has limited permissions to deploy code to a development environment. Over time, new workflows are added, and the account gains access to production infrastructure, cloud storage, Kubernetes clusters, and CI/CD pipelines.

No one intentionally created a security issue. Access simply expanded faster than governance controls.

This scenario reflects a broader trend. According to the OWASP Non-Human Identity Top 10, unmanaged machine identities and excessive privileges are among the most common sources of identity-related exposure. As non-human identities proliferate, understanding what they can access becomes increasingly difficult.

The challenge of securing machine identities

Effective identity security follows a simple sequence: discover identities, understand their permissions and risk, and enforce appropriate controls. In practice, each step becomes more difficult as machine identities multiply across cloud and SaaS environments.

Discovering identities

In our GitHub Actions example, the engineering team knows the service account exists. What they do not fully understand is everything that now depends on it. As workflows are added, credentials, tokens, and connected systems create access relationships that are difficult to track manually.

Organizations cannot secure identities they cannot see. Human identities are typically managed through identity and access management (IAM) platforms such as Azure Entra ID, AWS IAM, and Google Cloud IAM. Non-human identities are often scattered across cloud services, Kubernetes workloads, service accounts, OAuth applications, API tokens, secrets managers, and CI/CD pipelines.

Understanding permissions and risk

Over time, the GitHub Actions service account gains access to production cloud resources, deployment pipelines, and Kubernetes workloads. Security teams must determine whether that access is still necessary, which permissions are actively used, and what systems could be affected if the account were compromised.

Visibility alone does not answer critical questions:

• Which identities have production access?
• Which credentials are unused?
• Which AI agents can access sensitive data?
• What is the blast radius if a credential is compromised?

As machine identities accumulate permissions and integrations, these relationships become increasingly difficult to understand manually.

Enforcing controls

After identifying exposure, security teams must reduce it. In the GitHub Actions scenario, that means reviewing permissions, rotating credentials, removing unnecessary access, and limiting the service account to only the resources required for deployments.

More broadly, organizations must continuously enforce least-privilege access, revoke dormant accounts, and apply lifecycle governance policies. Without ongoing enforcement, machine identities can accumulate permissions over time, expanding both risk and blast radius.

Why does identity security matter?

Identity is the control plane for modern infrastructure. NIST identifies identity as a foundational component of Zero Trust Architecture, where every access request is continuously verified.

This matters because attackers increasingly target credentials instead of endpoints. According to IBM's 2026 Cost of a Data Breach Report, stolen or compromised credentials are the second most common initial attack vector.

In our GitHub Actions scenario, a routine automation account has become a high-value target. A single compromised credential could provide access to production infrastructure, deployment pipelines, cloud resources, and Kubernetes workloads.

The real risk is not the credential. It is the access attached to it.

How identity security creates risk when it fails

Identity failures create security, operational, governance, and compliance challenges. One well-known example is the 2019 Capital One breach, where excessive cloud permissions contributed to an attack path that exposed more than 100 million customer records. The incident demonstrated how identity decisions can directly influence breach impact.

Common sources of identity-related exposure include:

• Excessive cloud permissions
• Stale service accounts
• Unused OAuth grants
• Long-lived API tokens
• Unmanaged AI agent permissions
• Forgotten secrets in code repositories

Returning to our GitHub Actions scenario, imagine an API token associated with the service account is exposed in a public repository. Because permissions were never reviewed, that single credential now provides access to production infrastructure, cloud storage, and Kubernetes clusters.

What began as a leaked token has become a pathway into multiple critical systems. Agentic AI introduces the same challenge on a greater scale. As AI agents connect to more APIs, SaaS applications, and data sources, their access footprint can expand quickly.

The challenge is not simply identifying AI agents. It is understanding what they can access. This is where blast radius becomes critical. The question is not whether a credential, service account, or AI agent can be compromised. It is understanding everything it can reach if it is.

Identity security requires continuous governance

Leading security teams treat identity security as a continuous governance process, not a periodic audit. Most mature programs focus on:

• Maintaining an inventory of human and non-human identities
• Mapping permissions and access relationships
• Enforcing least-privilege access
• Managing credential lifecycles
• Monitoring anomalies

In our GitHub Actions scenario, the service account is treated as a first-class identity. Its permissions are continuously reviewed, monitored, and adjusted to prevent unnecessary access from accumulating over time.

Traditional IGA programs remain important, but modern identity security must extend beyond employees to machine identities, workloads, service accounts, API keys, secrets, and AI agents.

What are the best practices for modern identity security?

Effective identity security requires more than periodic access reviews. As organizations adopt more cloud services, automation, and AI-driven workflows, identity governance must become a continuous process.

Strong identity security programs typically include:

• Continuous discovery of identities across cloud, SaaS, and Kubernetes environments
• Automated credential rotation for secrets and API tokens
• Regular entitlement reviews for privileged accounts
• AI agent identity governance and access reviews
• Blast-radius analysis for critical credentials
• Lifecycle governance from creation through decommissioning

In the GitHub Actions scenario, these practices prevent the service account from quietly accumulating access over time and help keep permissions aligned with its intended purpose.

Cloud identity management must account for both human and machine access patterns. Without ongoing governance, permissions tend to accumulate, increasing complexity and expanding potential blast radius.

How Token Security approaches identity security

The challenge

Traditional IAM and IGA programs were designed to govern people. Token Security is designed to govern the machine identities and AI agents that now outnumber them. As organizations adopt cloud services, automation, and AI-driven workflows, security teams must also govern service accounts, API tokens, secrets, workloads, and AI agents.

In the GitHub Actions scenario, the challenge is not simply finding the service account. It is understanding what it can access, how its permissions have evolved over time, and what systems could be affected if it were compromised. Traditional approaches often lack the visibility and context needed to answer those questions at scale.

The approach

Token Security is built specifically for machine-first security. It helps security teams:

• Discover non-human identities across cloud and SaaS environments
• Map entitlements and access relationships
• Analyze blast radius to understand potential exposure
• Establish behavioral baselines and identify unusual activity
• Automate remediation of identity risks
• Govern machine identities and AI agents throughout their lifecycle

The outcome

Security teams gain visibility into previously unmanaged machine identities, understand the permissions and access relationships behind them, and reduce excessive privileges before they become security issues. The result is stronger governance, improved accountability, and greater confidence in managing machine identities and AI agents at scale.

Identity security in action

Lemonade: Turning visibility into action

As Lemonade expanded its cloud footprint, understanding machine identity exposure became increasingly important.

"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 reflects a common challenge in identity security. Visibility matters, but actionable remediation matters more.

HiBob: Governing AI agents with confidence

As AI adoption accelerated, HiBob needed better visibility into custom GPT agents operating across its environment.

"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 highlights how AI security increasingly depends on identity governance and control of machine identities.

Why AI agent security starts with identity

Identity security has evolved far beyond managing employee access. Today, it governs cloud workloads, service accounts, OAuth applications, API tokens, Kubernetes workloads, and AI agents.

Our GitHub Actions example illustrates a challenge that will only grow as agentic AI adoption accelerates. The service account and an AI agent share the same governance problem: both operate through credentials, permissions, and machine identities that can accumulate access over time.

As a result, AI governance ultimately depends on identity governance. Yet only 18% of security leaders are highly confident that their current Identity and Access Management (IAM) systems can effectively handle agent identities.

Organizations that can discover machine identities, understand their permissions, access relationships, and blast radius, and enforce lifecycle controls will be better positioned to scale AI securely. Those who cannot may find themselves managing thousands of autonomous identities without fully understanding what they can access or how far a compromise could spread.

Identity security is no longer just about users. It is the foundation for governing the machines acting on their behalf.