How to scale non-human identity management with secrets management

What happens in your environment when non-human identities grow as a natural side effect of how you ship and automate your systems?

New identities appear whenever you refine a pipeline, expand a codebase, or adjust internal services. A deployment introduces a role shaped by new logic, and a CI workflow issues a token for a task added during a workflow change. A background job receives credentials aligned with the data it processes, and smaller automation layers generate identities that match the work they support.

Over time, these processes create an identity layer that mirrors the evolution of your system, and the relationships among IAM entries, stored secrets, other sensitive data, and actual usage become harder to follow.

We laid the groundwork for this in our earlier pieces on non-human identity fundamentals and their role in a zero-trust model. This piece builds on both by focusing on the operational work you handle every day and outlining how to run identities and secrets as a single lifecycle that aligns with how your systems grow, shift, and reorganize.

TLDR

Managing thousands of Non-Human Identities(NHIs) and secrets at scale is hard because visibility, rotation, and governance break down quickly. This article walks through a five-step playbook to fix that:

1. Discover: Maintain a live inventory of all machine identities and their credentials.
2. Assign: Ensure every identity and secret has a clear owner responsible for its lifecycle.
3. Authorize: Grant permissions only as needed.
4. Automate: Let your deployment process handle the creation, rotation, and retirement of identities and secrets.
5. Monitor: Track usage patterns, detect anomalies, and remove unused or expired identities and secrets.

Following this lifecycle keeps your environment secure and makes audits and access reviews manageable.

Why machine identities and secrets have to converge

Traditional identity systems were built for people with stable accounts and predictable lifecycles. Non-human identities do not behave that way. Pipelines create and destroy them on demand, services generate new keys without warning, and tokens spread across repositories, containers, and CI/CD jobs. When machine identities and their secrets are handled as separate systems, gaps accumulate.

A common example that shows this gap clearly is a team that migrates a service to a new cluster but never deletes the old IAM role. Meanwhile, an earlier container image with a hardcoded API key for that same service is still running in production. The identity and the credential are both active, both orphaned, and nobody realizes they are related. These issues accumulate into consoles full of roles nobody fully understands, multiple secret stores with conflicting policies, and a long tail of static credentials that never get rotated. Attackers take advantage of this situation because machine identities are often over-privileged and rarely reviewed. A single orphaned service account or an API key left in a container image can be enough to compromise your environment.

The only reliable way to regain visibility is to treat each machine identity and its secrets as a single lifecycle object. The idea is simple in principle, but difficult without a supporting system. Identity and secret creation, usage, rotation, and retirement must all be governed together; otherwise, visibility fragments, governance becomes inconsistent, and blind spots become nearly impossible to audit.

These security gaps create friction for both engineers and security teams.

Where engineers feel the pain

It often starts with one service delivered under pressure. Its IAM role is created quickly, a few keys are embedded in a CI job to keep the build moving, and secrets end up scattered across multiple vaults because nobody has time to reconcile patterns. Months later, the role, the keys, and the secrets have all drifted out of sync, and no one is entirely sure who owns what or which parts are still in use.

If any of this sounds familiar, you are already living with the cost of unmanaged machine identities:

• Service accounts with no clear owner
• API keys tucked inside CI jobs and scripts
• Multiple secret stores in use because teams never aligned on a standard

This is where reviews stall, and fixes never catch up. Engineers experience it as an ongoing cleanup they can't keep up with, and security teams see it as gaps they can't fully close.

The best way to avoid these problems is to manage identities and secrets with a clear lifecycle and a consistent framework.

A lifecycle playbook for non-human identity and secrets

The rest of this article walks through a five-step playbook that covers the key areas you need to manage non-human identities and secrets effectively at scale. Each step reinforces the others, so discovery, ownership, permissions, provisioning, and cleanup function as a single system rather than one-off projects.

Step 1: Continuously discover identities and secrets

You cannot protect what you cannot see, so the first job is to build and maintain an inventory of both NHIs and their credentials. Most teams try to do this with periodic audits or spreadsheets, which collapse the moment new identities start appearing automatically. You need something continuous, especially as automated processes produce identities and credentials faster than humans can track.

If you don't have that level of insight by default, you should set up a simple discovery pipeline so you're not guessing about what exists in your environment. The easiest way to get there is to pull secrets from where they're actively being used:

• Cloud provider IAM listings for service accounts, roles, workload identities, and access keys
• Secret scanners like GitGuardian to catch hardcoded credentials in repos and config
• CI pipelines where temporary keys, tokens, and one-off service accounts often sneak in

To make this usable, normalize everything into a small, consistent inventory. A minimal schema that works for most teams looks like this:

With that schema, the discovery pipeline becomes straightforward. The goal is simple. You should always be able to tell who owns an identity or secret, why it exists, and whether it is still in use.

If you use a secrets manager built with machine identities in mind, such as Doppler, much of this visibility is centralized by default. The platform tracks token creation, rotations, and access activity, addressing a significant part of ongoing discovery.

Step 2: Enforce ownership and accountability

Once you have visibility, you will quickly see how many identities and secrets have no owner. That has to change. An ownerless identity cannot be reviewed or retired safely.

The fix is straightforward. Every NHI and every secret needs an accountable owner, usually a team.You can enforce ownership in practice by:

• Tagging or labeling identities and secrets with the owning team, application, or project.
• Mapping secrets and identities to code or projects so ownership travels with the system.
• Making ownership part of your onboarding workflow. A new service cannot go live until its IAM roles and secrets have an explicit owner assigned.
• Scheduling automated checks that flag identities or secrets without an owner, and requiring teams to claim them.

In Doppler, each project and config already maps to an application and environment. Once ownership is mapped and enforced, everything else becomes simpler. Access reviews are straightforward because every identity is associated with a team. Decommissioning a service becomes cleaner because its NHIs and secrets are part of the shutdown checklist. And when teams reorganize, ownership moves with the code, rather than being trapped in a forgotten spreadsheet.

Step 3: Design for least privilege from day one

Most over-privileged service accounts were granted broad permissions by engineers or teams trying to get things working quickly, and those permissions were never tightened. At scale, this pattern creates a large blast radius if a credential is ever compromised.

The better approach is to enforce least privilege at creation time. Define purpose-specific permissions that map to what a service actually needs. Segment secrets so that a token or client can only fetch the values for a single app and environment. Doppler makes this straightforward. Every service token is limited to one project and one config, so a service identity can only read the secrets that belong to its own deployment context.

From there, you can layer IAM restrictions and tagging to reinforce the same boundary, ensuring an identity can fetch only the Doppler token assigned to its service. This keeps the identity and its secret access tightly aligned as one lifecycle unit. It also reinforces standard access management practices without changing how your services operate.

A concrete way to implement this is through a scoped IAM policy that restricts a service to its specific task and resources:

Additionally, define a small set of standard permission profiles, such as read-only database access, CI/CD deployment access, or read-only configuration access. New services should fit into one of these profiles by default, with broader permissions granted only when there is a clear justification.

Step 4: Automate provisioning and rotation via the pipeline

Manual credential creation and rotation do not scale in the real world. The pipeline that deploys a service should also create its identities and secrets.

When deploying a new service via Terraform or another IaC tool, the pipeline should create the service's identity, attach only the permissions it actually needs, and provision a dedicated space for its secrets. Here's an example showing how to provision a secrets namespace and bootstrap a secret:

The code sample above sets up a dedicated secrets space in Doppler and generates a password to bootstrap the environment. Note the use of lifecycle.ignore_changes. This configuration ensures that once the secret is created, subsequent Terraform runs do not overwrite the value. This allows the secrets manager to rotate the password independently without causing configuration drift in your IaC.

Once the secret is bootstrapped, you should attach a rotation policy within the Doppler dashboard or via the API. This moves the rotation logic into the control plane, where it can be handled automatically based on time intervals or events, rather than being tied to your deployment cycle.

This type of practice helps to ensure that your machine identities and secrets aren't scattered across systems. They're defined in code, tied to the service, and structured to support automated rotation immediately upon deployment.

Step 5: Monitor and clean up continuously

Finally, you need a feedback loop. The creation, use, and retirement of machine identities and secrets must be visible in a single place so you can detect abnormal behavior early and remove identities that are no longer in use.

Doppler helps here by sending detailed audit events, including token creation, secret reads, rotations, and revocations, into your SIEM alongside IAM logs for role assumptions and key usage. When both streams land in the same system, you can correlate identity and secret behavior through shared fields like identity_id, secret_id, and last_access_at.

This correlation enables practical anomaly detection. For example, alert if a service token reads secrets from two different projects within a 24-hour window, which may indicate the token is being reused or misused.

Machine behavior is usually predictable, which makes unusual activity easier to spot. For example, a service account suddenly accessing secrets it has never touched before, or a token that becomes active after months of inactivity, may indicate misuse or compromise and should trigger an immediate investigation.

On the cleanup side, be deliberate and consistent. Disable or delete identities that have not been used within a defined period. Revoke secrets that have not been accessed in a while. Tie service decommissioning to identity and secret teardown so nothing is left hanging.

Next steps for scaling NHIs

Non-human identities now account for the majority of access in modern systems, yet many organizations still manage them with ad hoc scripts and fragmented tooling. That gap is exactly where attackers look for opportunities.

The solution is to treat machine identities and secrets as one continuous system rather than two separate problems. Following a clear lifecycle, from discovery and ownership to continuous monitoring and cleanup, you can scale without losing control.

When done well, this approach provides developers a reliable way to create and use machine identities and secrets, while security teams gain visibility and control without acting as human secret keepers. Over time, you reduce risk, simplify audits, and make your environment far harder for attackers to exploit.

Doppler supports this model by providing teams with a single place to track identity activity, enforce environment-scoped access, automatically rotate credentials, and feed audit events into their SIEM. It becomes the backbone of the lifecycle, ensuring identities and secrets move together from creation to retirement.

Want to see how this lifecycle looks in practice? A Doppler demo can walk you through the workflow end-to-end.