Machine Identity Management: How to Secure Service Accounts, API Keys, and Tokens

What is machine identity management?

Machine identity management is the discipline of discovering, governing, and securing every non-human identity that authenticates and acts in your environment: service accounts, API keys, OAuth tokens, workload certificates, CI/CD pipeline credentials, and AI agents. Each of these is a way for software to prove who it is and act without a human signing in, and each one is an access path into your systems that an attacker can take.

The core problem is not creating machine identities. It is governing them. Human identity programs have matured, most organizations now run SSO, MFA, conditional access, and risky sign-in detection, while software identities have multiplied quietly in the background, largely unowned, rarely rotated, and almost never reviewed.

Why machine identities are the identity problem of 2026

The numbers make the case on their own. Machine identities in the average enterprise grew from roughly 50,000 in 2021 to around 250,000 in 2025, according to  CyberArk 2025 State of Machine Identity Security Report. Palo Alto Networks put the ratio of non-human to human identities at 109 to 1 in its 2026 research, up from 82 to 1 the year before. KPMG's 2026 figure runs even higher in some environments. Whichever source you take, the direction is the same and the slope is steep.

The risk shows up in breach data, not just inventory counts. Sophos, in its State of Identity Security 2026 research, found that non-human identities were the root cause of 41 percent of successful identity breaches, and that only 34 percent of organizations regularly audit or rotate their service accounts and machine identities. Palo Alto Networks' 2026 landscape research found that 90 percent of organizations had at least one identity-related breach in the past year, and 83 percent had two or more. In 2026, many breaches do not need a stolen human password at all, because an API key, service account, or workload token already holds the access the attacker wants.

Machine identities carry a particular kind of risk because of how they behave. They cannot use MFA. They do not log out. They are seldom retired. A single stolen machine credential can therefore give an attacker broad, persistent access that no one notices, because unlike a human identity, a machine identity does not complain when something is wrong. It just keeps working, for whoever holds the credential.

The failure modes that turn machine identities into breaches

Secrets sprawl. Credentials leak out of the places they are supposed to live and end up scattered across repositories, configuration files, CI/CD pipelines, container images, and developer laptops. GitGuardian documented 28.65 million new hardcoded secrets in public GitHub commits in a single year. Once a secret is committed, automated scanners find it within minutes, and the credential often stays valid for years.

Stale and ownerless credentials. A service account gets created for a project and never decommissioned. The person who made it leaves. The credential sits unchanged, still valid, still privileged, with no one able to say what depends on it. This is how most organizations discover their machine identities exist: something breaks, and a 2 AM investigation reveals a web of dependencies nobody had documented.

Over-privileged workloads. Teams routinely assign broad roles to service accounts and workloads without a second thought. A CI/CD pipeline token with write access to production, or a Kubernetes service account granted admin-level permissions by default, is a critical exposure waiting for an attacker to find it. When the token is stolen, the attacker inherits every permission it carries.

OAuth grants that never expire. Third-party OAuth applications can reach CRM data, customer records, and financial systems through tokens that rarely expire and are seldom audited. Each grant is a standing access path that operates outside the visibility of traditional IAM, which is why OAuth token compromise features so heavily in recent SaaS supply-chain incidents.

How to manage machine identities properly

Effective machine identity management is a continuous loop, not a one-time cleanup. Five practices form the core.

1. Discover across every source

Build a complete inventory by collecting identities from cloud IAM, SaaS applications, CI/CD systems, secret managers, Kubernetes, source control, OAuth applications, and API gateways. Partial discovery is the same as no discovery, because attackers look for exactly the identities your inventory missed. An inventory that captures most of your machine identities still leaves the riskiest ones invisible.

2. Map each identity to owner, purpose, scope, and last use

Discovery produces a list. Governance requires context. Every machine identity needs a named owner, a documented purpose, a defined scope, and a record of when it was last used. Last-use data alone surfaces a huge amount of dead risk, because an identity that has not authenticated in months is usually one nobody will miss when you remove it.

3. Eliminate the unused, then tighten the rest

Start by removing ownerless and unused credentials, which is the fastest way to shrink the attack surface. Then work on what remains: rotate long-lived secrets, scope permissions down to actual need, and where possible replace static keys with short-lived, workload-federated credentials that expire on their own rather than sitting valid indefinitely.

4. Score and prioritize by real risk

Not every machine identity deserves equal urgency. A key to a low-value internal service is not the key to production. Score each identity against configuration, privilege, and behavior, and tie its risk to what it can actually reach, so your team works the identities that matter most first instead of fixing in alphabetical order.

5. Connect posture to detection

Storing machine identities in a nicer box is not governance. The inventory and its risk scores need to inform your detection layer, so that when a machine credential behaves abnormally, the alert already carries the identity's ownership, scope, and risk context. If your secrets manager and your identity governance do not talk to each other, you are only organizing your risk, not reducing it.

Where 8layers fits

Octagon, the posture module of the 8Layers platform, was built to treat machine identities as first-class citizens rather than an afterthought. It unifies inventory across every identity provider and cloud, scores every service account, API key, OAuth token, workload credential, and AI agent against security baselines, maps federation trust chains to calculate the compound risk an identity carries across systems, and prioritizes remediation with formal risk acceptance where a finding cannot be fixed immediately.

Because Octagon shares an identity data layer with Thor for detection and Compass for compliance, a risky machine identity is not just catalogued, it is watched, and it is tied to the compliance controls it affects. Posture, detection, and proof run on the same data, which is what turns a machine identity inventory from a spreadsheet into an actual control.

If you want to see how many unowned or over-privileged machine identities are live in your environment, book a demo.

Frequently asked questions

What is a machine identity?

A machine identity is any non-human identity that software uses to authenticate and act: service accounts, API keys, OAuth tokens, workload certificates, CI/CD pipeline credentials, and AI agents. It lets an application, script, or agent prove who it is and take action without a human signing in for each step.

How many machine identities does a typical enterprise have?

Machine identities commonly outnumber human ones by more than 100 to 1 in modern enterprises, with Palo Alto Networks reporting a 109 to 1 ratio in 2026. In absolute terms, the average enterprise went from roughly 50,000 machine identities in 2021 to around 250,000 in 2025, and the ratio is still climbing year over year.

Why are machine identities harder to secure than human ones?

Because the controls that protect human identities do not apply to them. Machine identities cannot use MFA, they never log out, and they are rarely retired. They are often created without an owner, granted more privilege than they use, and left with long-lived credentials that never rotate, which makes each one a durable and largely invisible access path.

What is the first step in machine identity management?

Discovery. You cannot secure what you cannot see, so the first step is building a complete inventory across cloud IAM, SaaS, CI/CD, secret managers, source control, and OAuth applications, then mapping each identity to an owner, purpose, scope, and last-use date. Eliminating unused and ownerless credentials from that inventory is usually the fastest early risk reduction.

How do machine identities cause breaches?

Most commonly through leaked or stale credentials. A hardcoded API key in a repository, an unrotated service account, or a long-lived OAuth token gives an attacker the access that credential carries, with no password to steal and often no alert to trip. Recent incidents show attackers using compromised machine credentials to move directly into production systems and downstream customer environments.