How platform teams can fight secrets management fatigue

More often than not, the fatigue that comes from handling secrets is a byproduct of how a team approaches the lifecycle of its secrets. Storing credentials across multiple tools and manually rotating expired ones is the perfect recipe for secrets management fatigue.

Beyond the obvious security risks, such as secrets being exposed in logs, stale credentials being exploited, or secrets sprawl, this approach also slows down your operations. Imagine having to update API keys scattered across several vaults before a production push or onboarding a new developer and manually sharing dozens of credentials before they can start work. These are real scenarios where simply passing secrets becomes a time-consuming and error-prone task. Your platform team should work to eliminate these scenarios.

In this article, I’ll break down the concept of secrets management fatigue, its common causes, and practical remedies, so your platform team is equipped with the modern practices and tools needed to overcome it.

Why NHI is breaking traditional access models

Fatigue doesn’t just come from juggling tools or repeating the same rotations. A growing source of the problem is the explosion of Non-Human Identities (NHIs). How do you track service accounts that are auto-generated by cloud providers or CI/CD pipelines, created silently in the background? How do you secure these NHIs when human-style authentication methods like Multi-Factor Authentication (MFA) or username and password don’t apply? It’s challenging, and with the sheer number of identities created every day in active systems, traditional access models start to look ineffective.

You can’t rely on manual onboarding, offboarding, or managing identities created in dynamic environments. For example, ephemeral CI/CD jobs or serverless functions can spin up thousands of short-lived service accounts, and tracking these identities manually quickly becomes unmanageable. Your platform team needs systems and tools that can detect and track NHIs, automatically rotate their credentials, and grant them only the access they genuinely need. Without solutions like these in place, the governance gap will continue to widen, making it easier for malicious actors to gain access to sensitive systems across your cloud environments and fueling the fatigue of teams already stretched thin by secrets sprawl.

NHI growth overwhelming traditional IAM

In a bid to address these needs, some teams begin using many secrets management solutions at once. But instead of solving the problem, this leads to inconsistent policies and a lack of visibility into how secrets are stored and shared.

When more secrets management tools make things worse for data security

You might think using multiple secrets management tools is foolproof. Your team could see it as balanced: “Since we deploy on AWS, let’s use AWS Secrets Manager for AWS secrets, while we handle rotations and dynamic secrets through another platform.” Or even worse, you end up allowing each team to use isolated tools in their own way. For example, one team uses HashiCorp Vault, while another relies on Azure Key Vault. It sounds smart at first glance, like a “separation of concerns.”

In reality, your team has just made secrets management more complicated. Now you’re rotating and monitoring secret usage on multiple fronts, applying policies in different places, and trying to keep rotation schedules in sync. Each additional tool multiplies the access controls, audit formats, and integration quirks your team must track, multiplying fatigue. Without a single source of truth or a centralized secrets management approach, gaps in security coverage emerge unnoticed, leaving risks hidden until an incident forces them into view.

Multiple fragmented secrets managers create silos and inconsistent policies, while a centralized orchestration approach keeps credentials unified, rotated, and fully auditable across environments.

This is one situation where less really is better. Stop juggling multiple tools. Consolidate secrets management in a central location, and automate the process to reduce complexity, close policy gaps, and avoid operational burnout.

How modern teams automate secrets and replace manual processes

Top platform teams focus on evolving their secrets management strategies to avoid breaches, security gaps, and operational fatigue. Here’s how your platform team can do the same:

1. Centralize your secrets management

Just because your team handles multiple types of secrets, such as database credentials, API keys, cloud access keys, or service accounts, doesn’t mean you need to store them in numerous tools. They might span across multiple environments like development, staging, and production, but it’s still better to choose a single, central platform where you can rotate secrets, audit usage, enforce access controls, and integrate with other tools.

2. Automate ephemeral workflows with dynamic secrets

Use dynamic secrets to reduce exposure for workflows that exist only to complete short-lived tasks. For example, a GitHub Actions workflow that needs to authenticate into an AWS account and pull a Docker image from an S3 bucket should not use static AWS access keys. Instead, have the pipeline request a temporary token valid from your secrets manager for 5–10 minutes.

3. Strengthen observability and policy enforcement

To avoid human and machine identity sprawl, implement transparent auditability across environments, covering both routine credentials and sensitive information. Track each secret from creation, through usage and access, to its expiration.

Use Infrastructure as Code (IaC) tools like Terraform or Pulumi to provision cloud-native infrastructure, including networking, Kubernetes clusters, and encryption layers, while avoiding hardcoding secrets in configuration files. For microservices and zero-trust architectures, integrate service mesh systems to identify every service (e.g., Kubernetes pods) and manage how they exchange secrets.

Finally, NHI-aware access policy engines that grant or deny access based on request details should be adopted. Implement logic in your workflows that checks who or what is making the request, what resources are being accessed, the time, IP location, and past access patterns. This allows you to block irregular or risky NHI activity automatically.

From the outside, these practices might appear to require storing secrets in separate systems to meet different needs. But they’re far easier to manage through an orchestrated approach rather than isolated tooling. Doppler acts as a central orchestration layer, letting you store and manage secrets, track their lifecycles, use dynamic and rotated secrets, and apply the access controls needed to implement NHI-aware policies and service mesh integrations.

Central orchestration acts as the hub for dynamic injection, policy enforcement, IaC integration, service mesh coordination, and full auditability, helping teams automate secrets without burnout.

It’s crucial for your team to lock down that single tool and adopt healthy management practices, especially now that secrets are increasingly being generated automatically for AI and LLM agentic systems.

Agentic systems and dynamic identity management for sensitive data

Agentic systems can already perform multi-step tasks on our behalf, such as provisioning cloud infrastructure, debugging code across multiple repos, or spinning up ephemeral test environments. As their autonomy grows, so does the need to scrutinize how these systems access and rotate the credentials that enable those actions.

AI agents create new identity challenges. Each interaction with a database, API, or cloud resource must be issued, rotated, and revoked securely.

How are these credentials issued, rotated, and revoked automatically? How do you track what these agents do and who they effectively “become” when carrying out tasks? Without clear governance, you risk privilege creep and unauthorized access slipping into your workflows.

If your team already struggles with manual rotations, adding AI agents that generate and consume credentials autonomously will amplify fatigue. That makes it critical to identify the gaps in your current approach and recognize the signs if fatigue has already set in.

Signs you’re suffering from secrets fatigue

At this point in the discussion, it should not be hard to tell whether your platform team is already suffering from secrets fatigue or heading toward it. To help you assess quickly, here’s a checklist you can scan.

If your team is:

• Still rotating secrets manually
• Not using audit trails to track secret usage
• Managing secrets across multiple tools and environments
• Manually sharing secrets and constantly mixing up their location or state
• Allowing AI agents to use long-lived tokens

Then you’re already deep in management fatigue. The fix? Start with a single platform first to unify secrets storage, then automate rotation, track usage, and issue credentials dynamically. Doppler provides the centralized orchestration layer to bring all of this together, reducing burnout and restoring operational focus.