Human identity management feels solved in most companies. You roll out Okta, enforce MFA, and move away from static passwords. This approach works well for employees, boosting security, cutting support tickets, and improving the user experience.
But that success also exposed a bigger challenge: non-human identities.
Service accounts, CI/CD pipelines, SaaS apps, and AI agents already far outnumber people. Each one comes with long-lived credentials, brittle workarounds, and endless manual rotations. DevOps teams are buried in overhead, security teams are chasing leaks, and developers lose time to audits instead of building.
Simply put, what works for humans fails for machines. Solving the non-human identity problem requires rethinking your IAM strategy from the ground up.
The Fundamental Mismatch
The core problem is that human IAM was never built for machine scale or behavior.
The Scale: Human IAM is designed for hundreds or thousands of people, not the hundreds of thousands of workloads found in a modern cloud environment. This scale mismatch makes manual credential management impossible.
The amount of non-human identities continues growing—10 to 1 will turn into 45 to 1, then 100 to 1, then 200 to 1. Nothing stops this growth.
The Nature of Identity: Unlike people, machines can’t use MFA or log in with a password. Instead, their “credentials” are static API keys or secrets embedded in code. This creates a hidden drag on DevOps velocity and leaves organizations exposed to security risks.
The result is that DevOps teams are burdened with provisioning and rotating secrets, while security teams are constantly chasing leaks. Your IAM strategy succeeds for humans but creates a persistent bottleneck for everything else.
There’s consistent friction between moving quickly, moving securely, and moving reliably. Trying to balance those three creates natural conflict.
Why Current Solutions Fall Short
When organizations try to address this problem, they often resort to tools that provide symptom relief, not a cure.
Secrets Managers
These tools don’t solve the problem; they just move it. While a vault can centralize credentials, it still doesn’t answer the core question: how does a machine prove who it is without another secret?
With Secrets Managers, the “secret zero” problem remains unsolved. If you’re on a physical machine and you don’t have AWS IAM or any native identity capabilities, how do you securely authenticate to the secrets manager to get your credentials? It’s the classic chicken and egg conundrum.
Instead of delivering secrets onto a machine and letting them sit on disk or writing them into code, you’re putting them into a vault that you then grab them from, just for them to end up on your disk anyway.
You’re still treating this as a secrets management problem when it’s actually an access management problem.
Cloud-Native IAM
These services provide excellent control within their specific environment, but they are fragmented.
They don’t provide a consistent way to manage access across multiple clouds, SaaS services, or on-premises systems. In cloud and data center environments, there are management planes, APIs, and services built into the infrastructure designed to help solve these problems at scale.
You’ve got AWS IAM, you’ve got Entra ID, you’ve got Active Directory. But in environments like retail with physical machines scattered everywhere, you really don’t have that, you just have hardware.
Governance Tools
These tools offer visibility into the problem but don’t prevent it. They can tell you where a credential has leaked but can’t prevent the leak in the first place. The real issue is not just knowing a secret has been exposed, but solving the underlying access problem that created the secret in the first place. This distinction is crucial: governance tools show you the symptoms, but a true solution requires addressing the root cause by establishing a machine identity.
The Identity-First Approach for Workloads
A true solution requires a shift from managing static secrets to managing dynamic identity. This new paradigm treats workloads like people, giving them the equivalent of a single sign-on (SSO) experience.
For humans, the process is simple and standardized: you authenticate once with an SSO platform to receive a SAML assertion or OIDC token, which all services accept. This eliminates the need for managing a jumble of different usernames, passwords, or API keys.
For machines, the current reality is fragmented. Workloads rely on a mix of static credentials like API keys, usernames, and passwords. This creates a security risk and an operational burden. The solution is to apply the same SSO principle to non-human identities.
An identity-first approach enables a machine to prove its identity (based on its code, environment, or purpose) and receive a short-lived, policy-based credential on demand. This is the equivalent of SSO for machines. It ensures there are no static, long-lived secrets to manage, rotate, or leak.
The result is a new way of working:
- Secretless Access: Workloads no longer need to store long-lived credentials, dramatically reducing the risk of a breach.
- DevOps Velocity: Teams are freed from the constant manual burden of credential management and can focus on building features.
- Zero Trust for Machines: Access is granted conditionally and verified in real time, enabling a Zero Trust framework that extends beyond human users.
The Snowflake Journey: A Case Study
Snowflake faced this exact challenge. After solving human IAM with Okta, they discovered that non-human identities were their biggest security and operational bottleneck. They evaluated existing solutions, including secrets managers and cloud-native IAM, and found them insufficient for their scale and needs.
By adopting an identity-first approach for their workloads, Snowflake:
- Freed engineering time by eliminating manual credential rotation
- Reduced audit and compliance effort
- Eliminated the risk of static secrets in their environment
Snowflake’s experience shows that the real opportunity comes when you stop treating non-human identity as an afterthought and start addressing it as a core security priority.
Rethink Security as an Accelerator
The human IAM strategy was never designed for machines. Trying to stretch it across workloads only adds risk and friction. The companies that are getting this right are no longer treating security as a brake but as an accelerator. They’re freeing DevOps teams, accelerating delivery, and proving that security can be a powerful business enabler.
A well-implemented access management solution is one of those rare things that can increase your security posture and improve your developer experience at the same time. The security improvements come from using ephemeral, short-lived credentials tied to a verifiable and auditable identity.
The developer experience improvements come from taking the responsibility and cognitive load of credential management and authentication off developers so they can focus on building features that deliver business outcomes.
The big win is that you get to a point where your developers don’t think about access at all. Who wants to create authentication for their latest AI agent or redesign the wheel on authentication every time they build a new service? They just want to get to their job and do what the business needs them to do.
The future intensifies these challenges. Agentic AI introduces new types of workloads with unique attributes and constraints. Sometimes it’s a workload and other times, it’s a workload operating on behalf of a human.
There’s also the potential return of the data center as companies get pulled back from expensive cloud costs and data residency requirements.
If you’re ready to explore how an identity-first approach for machines can work in your environment, Aembit offers a practical path forward.
