Last updated: July 2026
For every human user in a modern enterprise, there are dozens, sometimes hundreds, of identities that belong to machines rather than people. They run silently in the background, authenticating thousands of times an hour, and most organizations cannot produce a complete list of them. This guide explains what machine identities are, the forms they take, why they are so hard to secure, and why they have become one of the defining security challenges of 2026.
What are machine identities?
A machine identity is any digital identity that belongs to a machine or workload rather than a person. It is what software uses, instead of a human, to authenticate and gain access to systems. Machine identities include service accounts, API keys, tokens, SSH keys, certificates, and the identities of applications, containers, devices, and increasingly AI agents.
Machine identities authenticate software to software: a backup tool connecting to a database at 2 a.m. does not type a password, it authenticates using a service account. The API key your ticketing system uses to pull CRM data, the certificate securing a server’s HTTPS connection, the OAuth token syncing employee records, these are all machine identities. They are frequently called non-human identities (NHIs), and the two terms are used interchangeably. Where a human identity belongs to a person who logs in, makes decisions, and logs out, a machine identity operates continuously in the background, part of the invisible workforce powering modern technology.
The main types of machine identities
Machine identities take several forms, each authenticating in a different way.
- Service accounts. Non-human accounts that run background processes, scheduled tasks, and application services, often using stored credentials.
- API keys and tokens. String-based credentials that authenticate applications when they call internal or external APIs. They range from simple static keys to OAuth tokens that expire and refresh, and JWTs that carry encoded permissions.
- Certificates. Cryptographic identities that authenticate servers and devices, encrypt communications (TLS/SSL), and establish trust between systems using public key infrastructure.
- SSH keys. Credentials used for automated, machine-to-machine access to servers and systems.
- Workload identities. Identities granted to applications, containers, and microservices, often via cloud-native IAM roles or, increasingly, open standards like SPIFFE that issue short-lived cryptographic identities.
- AI agent identities. The newest and fastest-growing type, the identity of an autonomous system that acts across other systems on behalf of users or other software.
Whatever the form, one thing unifies them: each authenticates using secrets, such as tokens, keys, or certificates, rather than a username, a password, and a human present to approve access.
How machine identities differ from human ones
The gap between human and machine identities is what makes the latter so hard to secure. Human identities follow predictable patterns: employees work specific hours, log in from consistent locations, and follow HR-driven lifecycle events, onboarding, role change, offboarding. None of these assumptions hold for machine identities.
A service account has no manager to certify its access. An API key does not complete MFA. A token has no normal working hours to baseline against, and no resignation date to trigger removal. Machine identities are created not by HR but by developers, business users, third-party vendors, and increasingly by other machines. They authenticate continuously, are often more privileged than any human account, and generate so little anomalous behavior that intrusion detection rarely fires on them. This mismatch is precisely where attackers operate.
Why there are so many machine identities
Machine identities now vastly outnumber human ones, and the ratio is climbing fast. Estimates vary by source and environment, but the direction is unmistakable: Rubrik Zero Labs puts the ratio around 45:1 in the typical enterprise, ManageEngine’s 2026 outlook found organizations reporting 100:1 (some as high as 500:1), and Palo Alto Networks’ figure grew from 82:1 to 109:1 in a single year. In cloud-native and DevOps environments, the ratio can reach 144:1.
The reason is structural. Modern systems are built from many small, automated parts: microservices, containers, serverless functions, CI/CD pipelines, and now AI agents, each of which needs its own identity to authenticate to other services. Every cloud resource, automation workflow, and AI integration creates new machine identities. As cloud, DevOps, and AI adoption accelerate, the population keeps multiplying faster than security teams can track it.
Why machine identities are a security risk
Machine identities have become one of the largest and least-governed attack surfaces in the enterprise. Industry reporting indicates that a majority of security incidents now involve machine identities, and a large share of organizations have suffered a breach tied to an unmanaged one. Several properties make them dangerous.
Most risk comes from long-lived static secrets. API keys and service-account passwords are frequently hard-coded into source files, copied between systems, and rarely rotated. Research indicates 71% of non-human identities are not rotated within recommended timeframes, and a significant share of secrets confirmed valid years ago remain exploitable today because they were never revoked. When a developer’s laptop is hit by an infostealer, these secrets are pulled alongside personal ones, exposing production credentials in one shot.
The lifecycle problem compounds it. When an employee leaves, access is revoked. When a machine identity is created, it often persists indefinitely, buried in a forgotten config file, a zombie identity that never retires, never gets audited, and becomes a stable, low-visibility backdoor for lateral movement. The OWASP Non-Human Identities Top 10 ranks improper offboarding as the number one NHI risk.
AI agents: the newest machine identity challenge
AI agents are the fastest-growing and most difficult category of machine identity, because they break the assumptions that governance was built on. A traditional machine identity like an API key is static: it has a defined scope you can inventory, audit, and revoke. An AI agent is dynamic: operating with delegated access, it can escalate its scope at runtime in ways no policy document anticipated, deciding which tools to call and which APIs to trigger on its own. The blast radius is higher, and the audit trail thinner, which is why securing AI agents has become an urgent priority in 2026.
How to start securing machine identities
Securing machine identities follows a clear sequence. Start with a complete, continuous inventory, because you cannot protect what you cannot see, and most teams underestimate their footprint by three to five times. Assign every identity a human owner, so accountability exists. Replace long-lived static secrets with strong, short-lived credentials like certificates and tokens, and rotate them automatically. Enforce least privilege so each identity can do only what it needs, and add monitoring so anomalous use is caught.
Doing this at the scale of hundreds of machine identities per human, across hybrid and cloud environments, is beyond manual processes. It requires the same governance discipline organizations finally applied to human users, extended to machines and AI agents. A dedicated approach to machine identity and AI agent security combined with expert integration is what turns an invisible, sprawling attack surface into a governed, auditable one.
Frequently asked questions
What is a machine identity?
A machine identity is a digital identity that belongs to a machine or workload rather than a person. It includes service accounts, API keys, tokens, SSH keys, certificates, and the identities of applications, devices, and AI agents, used to authenticate software to software.
What is the difference between a machine identity and a non-human identity?
They are the same thing. “Machine identity” and “non-human identity (NHI)” are used interchangeably to describe any identity belonging to software rather than a person.
What are examples of machine identities?
Common examples include a service account letting one application talk to another, an API key used by a script, a TLS certificate identifying a server, an SSH key for automated access, a cloud workload identity, and the identity of an AI agent.
Why are machine identities harder to secure than human ones?
Because they cannot perform MFA, have no manager to certify access, operate 24/7 with no normal behavior pattern to baseline, and often persist indefinitely with no owner or expiration. Traditional IAM was built for human lifecycle events that machines do not have.
How many machine identities does an organization have?
Far more than humans. Estimates range from about 45:1 to over 100:1 machine-to-human, and up to 144:1 in cloud and DevOps environments. Most organizations underestimate their footprint by three to five times.
Key takeaways
- A machine identity (or non-human identity) is any digital identity belonging to software rather than a person: service accounts, API keys, tokens, certificates, SSH keys, and AI agents.
- They now outnumber human identities by 45:1 to over 100:1 and are hard to secure because they bypass MFA, have no owner, and often persist indefinitely with unrotated secrets.
- AI agents are the fastest-growing and riskiest type, because they can dynamically escalate their access, making inventory, ownership, short-lived credentials, and least privilege essential.
