CISO's guide to nonhuman identity security

What are nonhuman identities?

At first glance, the term "nonhuman identity" would appear to include anything that isn't a person, such as servers, devices, workloads, service accounts and so on. But the industry's understanding of identity has evolved. In legacy environments, machine identities generally refer to certificates, SSH keys, device accounts or service accounts tied to OSes or hardware. These were relatively static, predictable and closely aligned with infrastructure stacks. In a cloud-native, API-driven environment, however, that definition is no longer sufficient. NHIs encompass a much broader and more dynamic set of identities, including the following:

• Workload identities. These represent cloud workloads -- VMs, containers, serverless functions -- that are permitted to authenticate to cloud resources. Examples include AWS identity and access management (IAM) roles for EC2 or Lambda, Azure managed identities and Google Cloud service accounts. These identities often live for microseconds to hours and frequently generate temporary credentials.
• Service accounts. These include OS or application accounts used by internal services, applications, databases or backup systems. They often run background processes or scheduled tasks. Despite being one of the oldest forms of NHIs, they remain one of the least governed and most overprivileged.
• Application identities. These are software components, such as APIs, microservices and web apps, that authenticate to databases, message brokers or third-party APIs. These identities might use API tokens, OAuth secrets or embedded keys.

• Secrets and API keys. These include credentials used directly by software, scripts, automation pipelines or infrastructure-as-code templates. They often represent API keys -- SaaS, cloud, payment gateways; database connection strings; OAuth client secrets; GitHub and GitLab tokens; and container registry tokens.
• Composite AI and machine learning identities. With the rise of AI agents, large-language model-driven workflows and autonomous pipelines, model-driven processes create and use identities to call APIs, retrieve data or take automated action.
• OT and IoT identities. Sensors, industrial control systems, cameras, medical devices and other embedded systems authenticate to management consoles or data collectors. They often use weak or factory-default credentials unless explicitly governed.

While machine identities and NHIs overlap, NHIs introduce the following three fundamental differences:

• Scale. Traditional machine identities -- certificates, device accounts -- are relatively small in number and long-lived. NHIs scale into the tens of thousands or millions and are created dynamically by continuous integration/continuous delivery (CI/CD) pipelines, auto-scaling workloads, AI and self-healing infrastructure, and event-driven automation. Most legacy IAM and privileged access management (PAM) tools were never designed to handle that level of volume and churn.
• Diversity of authentication methods. Machine identities have historically used certificates or Kerberos to authenticate. NHIs authenticate using a much broader array of methods, including JSON Web Tokens, cloud IAM roles, OAuth2/Open ID Connect secrets, long-lived API keys and more. Each requires unique governance, rotation, lifecycle management and telemetry handling.
• More autonomy. NHIs are often more autonomous than traditional machine identities and perform actions independently in many cases. They initiate API calls, move data, spin up resources, run scripts and interact with critical systems. This autonomy means that NHIs can cause large-scale damage extremely quickly if compromised, and traditional security controls might fail to see NHI behavior as abnormal.

Challenges of protecting NHIs

NHIs represent a new class of rapidly changing, high-impact identity risk that can't be easily addressed with existing tools or mental models used for human identities.

NHIs represent a new class of rapidly changing, high-impact identity risks that can't be easily addressed with existing tools or mental models used for human identities. This challenge becomes even greater as organizations accelerate automation and cloud adoption. NHI sprawl also increases faster than governance maturity.

The following issues make NHIs uniquely difficult to protect:

• Lack of ownership and accountability. NHIs are often created automatically by infrastructure teams, DevOps pipelines, application teams and SaaS integrations. In many cases, there isn't a clear sense of who owns the identity, who controls and approves permissions, or who should rotate keys, etc. This ownership vacuum leads to identities that persist far longer than intended.

• Excessive privileges. NHIs frequently receive broad, over-provisioned permissions, among them wildcard IAM roles in cloud, service accounts with full domain admin rights and API keys with full read/write scopes. Because NHIs automate business processes, teams fear breaking them and avoid reducing privileges. As a result, a range of identities can access massive amounts of sensitive data or infrastructure.

• Long-lived and hardcoded credentials. Many NHIs rely on never-rotated API keys, secrets hardcoded in code repositories, credentials stored in config files or scripts, and shared secrets reused across applications. This creates a high likelihood of leaked credentials, often resulting from developer mistakes, misconfigurations or CI/CD logs exposing secrets.

• Lack of behavioral baselines. Human user behavior is relatively predictable. Logins follow work hours, user accounts rarely call thousands of APIs per minute and access patterns generally align with job roles. NHIs are more challenging to profile, with high-frequency API usage, automated bursts of activity, irregular patterns driven by workflows or triggers, and potential interaction with many systems. This makes anomaly detection more complex and harder to tune.

• Limited telemetry and monitoring. Security tools were designed around human identity patterns. SIEM, user and entity behavior analytics and PAM products often don't analyze NHI authentication logs or model NHI risk scoring, and might lack visibility into service-to-service communication. Even in the cloud, where copious IAM logs exist, these files can be noisy, verbose and spread across services.

• Credential propagation in multi-cloud and SaaS integrations. Since many organizations use NHIs to link cloud environments, CI/CD tools, SaaS platforms and traditional on-premises infrastructure, secrets are often duplicated or reused across multiple systems, making remediation and rotation difficult if a single identity is compromised.

How to protect NHIs

Zero trust, a security technique favored by many organizations, is difficult to apply to NHI scenarios. Zero trust is built on concepts and controls such as continuous authentication, explicit verification and context-driven access. For NHIs, these controls are harder to implement because NHIs often do not have a session in many cases. In addition, device posture is irrelevant; context signals, such as location and behavior, are harder to define and model; and newer controls, such as adaptive MFA, usually don't apply. This leaves organizations with far fewer mechanisms to gate access.

To manage NHI security effectively, organizations need to shift their strategies, using a framework that manages the entire NHI lifecycle, from creation to monitoring to retirement.

Establish NHI classification and ownership

Create an enterprise-wide NHI taxonomy with categories including service accounts, workload identities, API keys, and app and service tokens. Each identity should have a clear owner responsible for permission approvals, rotation policies, usage reviews, and deletion or retirement.

Implement least privilege principles for NHIs. Adopt cloud-native best practices, such as using scoped tokens with minimal permissions, avoiding wildcard permissions or administrative roles where possible, using cloud IAM roles instead of static credentials, and applying microsegmentation to limit blast radius wherever feasible. For service accounts, switch from domain-wide privileges to task-specific permissions.

Centralize secrets and credential management

Replace hardcoded or static credentials with secret managers, such as AWS Secrets Manager, HashiCorp Vault or Azure Key Vault; credential brokers; identity federation with short-lived tokens; and automated rotation workflows. Never store secrets in places such as Git repositories, CI/CD logs, Terraform or Ansible playbooks, or container images. Static credentials should be used as a last resort.

If possible, deploy continuous monitoring and behavioral analytics for NHIs that understand service-to-service authentication patterns. Track NHI access frequency, API calls and error spikes, and create behavioral baselines for workloads and service accounts. Cloud platforms provide logs, such as AWS CloudTrail or Microsoft Entra ID sign-in logs, but teams must aggregate and interpret them with organizational context.

Automate, automate, automate

Manual identity governance doesn't scale. Use automation to perform common actions, such as auto-approving least-privilege permissions sets, auto-revoking unused NHIs, auto-rotating secrets on a schedule and decommissioning identities when workloads retire. CI/CD pipelines should generate ephemeral credentials that disappear with the workload.

Work toward zero trust by implementing the following:

• Mutual TLS between services, service mesh or workload identity frameworks such as SPIFFE/SPIRE.
• Continuous identity verification on every API call.
• Policy enforcement based on identity context.

These controls help ensure that service-to-service communication is authenticated, authorized and auditable.

Test NHI-related resilience and incident response

Conduct regular exercises such as simulated token theft, API key replay tests and workload compromise drills. During these exercises, validate logging visibility, determine the blast radius, test revocation and rotation speed, and confirm whether downstream systems detect anomalies.

NHIs now and in the future

As organizations accelerate automation, machine-to-machine communication, cloud adoption and AI integration, NHI security will grow in importance. With this growth comes sprawling credentials, unclear ownership, overprivileged service accounts, difficult-to-monitor authentication flows and other risks.

Security teams must evolve their identity governance strategies to encompass this new reality. The future of identity security lies in automated lifecycle management, least-privilege enforcement, behavioral analytics and strong credential management tailored to the nature of NHIs, not humans. Organizations that embrace this shift will strengthen their resilience, reduce their attack surface and be far better prepared for a world where work is increasingly done not by people, but by autonomous digital actors.

Dave Shackleford is founder and principal consultant at Voodoo Security, as well as a SANS analyst, instructor and course author, and GIAC technical director.