Secrets management best practices for ephemeral environments

Secrets vaulting was designed for persistent systems, such as databases, long-running servers, and applications that remain active for months. Ephemeral environments don’t work that way. Kubernetes jobs, serverless functions, and CI/CD pipelines spin up, execute, and shut down in minutes. If your secrets last longer than the workload, you’ve already created a risk.

This mismatch breaks traditional strategies because long-lived credentials embedded in images or injected at build time can leak through logs. Others remain cached in containers or stay valid long after the job is complete. Instead, you need secrets that expire as quickly as the workload itself.

Comparison of secrets lifecycle in persistent vs. ephemeral environments. Traditional systems rely on long-lived credentials that must be rotated, while ephemeral workloads require dynamic secret access that expires within a defined time.

Take a CI/CD job that fails and dumps logs. If you injected a permanent database key, that secret lingers and can be misused long after the run. If you instead inject an OIDC token or other short-lived credential, the exposure window closes as soon as the job ends.

In this article, I explain why vault-based strategies can fail in ephemeral systems, the risks of treating them like persistent ones, and the principles that you can apply in practice.

TL;DR

• Use short-lived credentials that expire with the workload.
• Inject secrets at runtime, not build time.
• Automate rotation and revocation through your cloud provider or a secrets manager.
• Scrub logs and disable debug endpoints to avoid leaks.
• Centralize orchestration with a platform like Doppler.

The risk of treating ephemeral systems like persistent ones

Imagine you have a user-profile microservice running in an Amazon EKS cluster that connects to an Amazon Relational Database Service(RDS) to manage customer data. Instead of using temporary credentials, your team uses a permanent AWS access key with full read/write permissions to the database, which is then mounted directly as environment variables in the application’s container.

Here’s what that might look like in a Kubernetes manifest:

By doing this, you’re not only hardcoding secrets in YAML but also using overly privileged keys, essentially giving attackers a window to gain access to your workflow. Suppose someone gains even read-only access to the Kubernetes API; they can retrieve this secret manifest, decode the Base64 values, and misuse the valid AWS keys, without ever touching your running application.

How attackers can gain access through poor practices such as mounting secrets directly into containers. A static AWS key in a Kubernetes manifest can be retrieved, decoded, and misused without touching the running application.

This is just one of the many ways things can go wrong when you treat ephemeral environments like persistent ones. Even with serverless computing, if a developer relies on persistent secrets and forgets to pass them as environment variables before deploying a Lambda function, the function fails on cold start.

The snippet below illustrates how a Lambda function wired to persistent environment variables can fail immediately if those values are missing or outdated:

Another common scenario is when the pipeline itself exposes long-lived secrets. When persistent credentials are injected broadly into a CI/CD job, they can leak into logs during verbose runs or failures:

To avoid scenarios like this, the principle here is simple: ephemeral environments need dynamic secret injection. Allow your Kubernetes pods, Lambda functions, and other short-lived workloads to request time-sensitive secrets automatically.

Best practices for managing sensitive data in short-lived systems

Adhering to the correct principles for handling secrets in ephemeral environments helps avoid breaches, failed runs, credential misuse, and even secrets sprawl. From broadly handling ephemeral workloads, a few key secrets management best practices for short-lived workloads become clear:

• Automate token requests: Systems should automatically request credentials with a defined time for expiration rather than relying on static keys.
• Inject secrets at runtime, not build time: Avoid baking secrets into images or code.
• Harden your runtime environment: Scrub logs, remove debug endpoints, and prevent secrets from persisting.
• Centralize orchestration with a secrets manager: As your workflows grow and ephemeral systems multiply, use a single platform that can control access by issuing and revoking dynamic credentials at scale.

Take our earlier scenario with AWS access keys in the user-profile microservice. Instead of mounting permanent keys as environment variables, you can use IAM Roles for Service Accounts (IRSA).

Think of IRSA as giving your pods a verifiable ID badge. Whenever a pod needs database access, it presents this badge to AWS and receives temporary, short-lived credentials. The Kubernetes service account is trusted by AWS, so new pods get fresh access automatically without anyone passing keys around. This trust relationship removes the risk of credentials being exposed in logs or leaked from manifests.

To make this work in our user-profile example, the steps look like this:

Step 1: Define AWS trust policy

This allows AWS’s security token service(STS) to issue temporary credentials only to default:user-profile-sa, restricting which service account can assume the role.

Step 2: Annotate the Kubernetes service account

This binds the pod identity to the IAM role, which grants only the required permissions.

Step 3: Use short-lived credentials at runtime

This allows the SDK in the container to discover credentials from the IRSA token. STS would issue them with a default TTL of about one hour and refresh them automatically.

With this setup, no long-lived keys are stored in manifests, credentials are refreshed automatically, and the risk of exposure in logs is reduced. Adopting these patterns across ephemeral systems leads to reduced leakage, stronger compliance, improved information security, and greater operational consistency.

Among the best practices, centralizing orchestration is a force multiplier. A platform like Doppler lets you dynamically issue, refresh, and revoke large volumes of short-lived credentials. With that foundation in place, other practices become easier to apply consistently and at scale.

How Doppler works in ephemeral environments

With Doppler as a secrets management solution for ephemeral environments, you centralize how dynamic secrets are requested. Instead of building separate secret-fetching logic for Kubernetes, Lambda, and CI/CD, you give developers a single, unified workflow.

Doppler acts as a consistent layer on top of platform-native identity mechanisms, such as IRSA and Lambda IAM Roles. Workloads authenticate with their native identities, but secrets are retrieved from one centralized location.

Here’s what it would look like in some ephemeral environments:

Kubernetes

The Doppler Kubernetes Operator syncs secrets into the cluster from a Doppler project. In our user-profile example, when a pod needs database access, the Operator doesn’t hand over a static password. Instead, it keeps a Kubernetes Secret updated with fresh secret data from Doppler. Behind the scenes, Doppler connects to RDS to generate a temporary user with a short TTL, typically 30 minutes. If the pod lives longer than that TTL, the Operator refreshes the Secret with a new value. Once the pod is destroyed, the lease expires, and Doppler instructs RDS to revoke the user completely.

In practice, you’d first register your Doppler Service Token in Kubernetes:

Then your manifest file would be set up like this:

It’s important to note that Doppler handles TTL and rotation itself. The Operator’s role is only to sync those updated values into Kubernetes using the manifest above.

Lambda

Doppler adapts the same workflow to serverless functions. Secrets are delivered dynamically instead of hardcoding secrets into application code or storing long-lived credentials. In most setups, Doppler is used in CI/CD to sync short-lived secrets into the Lambda environment at deployment time. Alternatively, Doppler can keep AWS Secrets Manager or Parameter Store updated with fresh values, and the function retrieves them at runtime with the AWS SDK. In both cases, secrets are centrally managed and can be rotated or revoked without code changes. Serverless security becomes critical in this context, as secrets must expire alongside the function itself.

Here’s what that looks like in practice (CI/CD sync example):

And your function just reads the values at runtime:

In this alternative setup, Doppler continuously syncs fresh secrets into AWS Secrets Manager, and your Lambda function simply fetches them at runtime with the AWS SDK:

Across ephemeral environments like Kubernetes, Lambda, and CI/CD, Doppler replaces static secrets with dynamic workflows. The table below compares the key differences between using traditional static secrets management and Doppler’s approach.

With the core patterns in place for Kubernetes and Lambda, it’s worth examining how ephemeral secrets are managed as demand expands with the growth of AI adoption.

Ephemeral secrets management in the age of AI

The definition of ephemeral workloads first widened with the rise of cloud computing, and AI has stretched it even further. Many AI-driven systems now run on serverless architectures, while requiring adherence to strict security policies and compliance requirements. Bots, AI agents, and models frequently operate as short-lived jobs that demand secrets only for as long as the task itself exists.

Examples include:

• Fetching Kubernetes metrics and adjusting cluster resources automatically
• Running retrieval-augmented generation(RAG) pipelines
• Cross-checking cloud workloads against policies and suggesting next steps

Consider a short-lived AI agent that triages GitHub issues. It pulls open issues from multiple repositories, summarizes them with an LLM, applies labels, posts updates, and then shuts down. The agent only needs GitHub API access for the duration of the job. Using Doppler as a single source of truth, you can provide a scoped, time-boxed token at runtime, centralize audits, and automatically revoke the token once the job ends.

The same principles you applied earlier to CI/CD pipelines, containers, and serverless functions also apply here: automate dynamic token requests, inject them at runtime, and revoke them once the workload is complete.