Implementing secrets governance across multi-cloud, on-prem, and edge environments

Most organizations span workflows across multiple cloud providers, on-premises systems, and even edge locations. These workflows interact with one another, yet the secrets vaults behind them are treated as isolated silos. Over time, inconsistent visibility, fragmented policies, and scattered logs turn into constant burdens. Time and workforce that should be spent on building real features shift toward untangling a growing web of secrets.

This happens because each secret management system has its own configuration, API style, and protocol. What teams need is a secrets governance model that unifies visibility, access policies, and automation across these isolated environments without discarding their existing tools.

TLDR

This article provides a step-by-step run-through of that model. By the end, you'll have a clear blueprint for centralizing secrets control across your entire infrastructure.

Here's a high-level overview of the stages involved in implementing a secrets governance framework:

High-level overview of the stages involved in implementing a secrets governance framework

Step 1: Map your current secret landscape

The first step involved in building a centralized secrets governance is discovering what secrets you have and where they live. You can’t govern or protect secrets that you don’t know exist. Such ignorance is often why teams face issues like leaked secrets, overused non-human identities, and long-lived credentials.

Perform a secrets inventory check to obtain a complete list of sensitive assets across all of your workflows, including:

• Cloud vaults
• Private cloud, on-prem systems, or datacenters
• Kubernetes clusters and CI/CD platforms
• Edge or IoT devices
• Existing secrets vaults already in use

With this visibility, identify duplicates across environments, clarify ownership of each secret, and determine which credentials should be federated, rotated, migrated, or expired.

Step 2: Design a governance plane, not another vault

It’s important to understand that a governance layer differs from a secrets manager. You’re not building another repository to just store secrets. Instead, a governance plane defines the rules, policies, and processes that determine how, when, and by whom every secret, including those in your existing vaults, is managed.

Design a plane that provides traceability and enforces compliance through visibility, forming the backbone of a centralized secrets management solution across your existing systems. Make sure it has the following core capabilities:

Metadata aggregation

It collects metadata such as secret names, locations, owners, encryption key IDs, and rotation dates from all environments into a single inventory. This helps you discover secrets across your workflows and maintain continuous visibility as new ones are created.

Policy engine

It defines global rules for each secret behavior. With it, set security policies for the secrets your workflow uses. For example, you can require that the credentials used by your Kubernetes pods expire within 30 minutes and be fully revoked on pod termination.

An effective governance plane also manages role bindings by mapping identities from your provider (e.g., AWS IAM, Google Cloud IAM) to specific permissions in your vault. For instance, members of a DevOps team can automatically inherit relevant policies through the governance plane.

Audit collection

It acts as a centralized audit hub, collecting activity across all environments, including isolated logs from existing vaults. This makes it possible to query who accessed what, when, and where, for example, identifying which application retrieved a MongoDB credential across environments.

Access orchestration

It establishes consistent access patterns across environments by assigning trusted identities such as AWS IAM roles or Google service accounts. For example, instead of embedding static credentials in a workflow, use a CI/CD runner's OIDC token to request temporary secret access from the governance plane, while the static secret remains secure in the vault.

These are the critical capabilities your governance plane must have to meet the modern security and operational standards. Tools such as Doppler already provide these functions, allowing you to integrate with your existing environments via APIs or connectors.

Step 3: Standardize access management and policy definitions

This step focuses on establishing uniform rules and access patterns across all environments. The goal is to ensure that no matter where secrets live, you control access to them using a consistent approach. You can achieve this by having these three key areas under control.

Role structure

Define a base set of roles, such as developer or tester, and map them to the various identity providers (IDPs), such as IAM roles or service accounts your workloads rely on for authentication. This ensures that the same role names are recognized when accessing sensitive data and that consistent privileges are applied across your workloads.

Access scope

Create namespaces or environment definitions to separate different stages of development. Your production and testing stages may rely on the same systems, but the secrets they use should be named in a consistent way. For example, you might use names like aws-access-staging or gcp-database-staging for staging, and aws-access-prod or gcp-database-prod for production.

Approval workflows

As part of your privileged access management, automate reviews for highly sensitive secrets, especially those connected to production workloads. This adds oversight and prevents sensitive actions, such as extending access scopes or rotating secrets for your primary database cluster, from occurring without review.

Below is a practical example of how you could structure a standardized policy:

This policy defines consistent roles, environment scopes, and approval rules.

Step 4: Automate secrets management lifecycle

Manual management of your secrets lifecycle often leads to issues such as security breaches, longer attack windows, and uncontrolled secrets sprawl. One of the main goals of secrets governance is to minimize human involvement at every stage of that lifecycle. Below are the key stages and how to automate each:

Creation

Use dynamic secrets where possible. Since these credentials are generated on demand and have a short lifetime, they significantly reduce the risk of breaches by shrinking your attack surface. With IaC, you can configure dynamic secrets integration to connect to a provisioned AWS RDS database and define secret configurations that automatically generate short-lived credentials with a defined TTL.

Rotation

Establish clear timelines for automatic rotation of static credentials. Depending on sensitivity, secret rotation might be carried out monthly, weekly, or quarterly.

You can automate this using serverless cloud functions or your cloud provider's rotation features, but these approaches can cause downtime if not managed carefully. A more resilient approach is using Doppler's dual-secret rotation. Here, two instances of your workload secret get created: one active and the other remains on standby. The active credential remains in use until its TTL expires or is revoked. At that point, the governance plane promotes the standby version and generates a new one in the background.

This eliminates downtime and can be mirrored in other systems by managing versioned secrets or alternating aliases.

Distribution

There are multiple ways to distribute secrets based on the workload type. Combine workload identity and cloud IAM to deliver temporary tokens into serverless compute or virtual machine environments.

For containerized workloads or managed Kubernetes services like EKS and GKE, or secret orchestrators like Doppler to inject secrets at runtime. Even for inter-team collaboration, use parameter stores or other secrets managers to share secrets and grant access only to authorized users securely.

Revocation and retirement

Implement event-driven revocation using webhooks or automation pipelines. For instance, when an employee is deactivated, a webhook can automatically revoke the keys associated with that user and generate replacements for active workers.

An identity provider or monitoring system, such as Okta, usually detects incident alerts or user deactivation events. It routes these signals through an API Gateway, which publishes them as events such as revoke:db-prod-key to an event bus. The bus then invokes Doppler or another system to revoke or rotate the affected credential.

Step 5: Extend governance to hybrid environments

Once the governance model is established across your multi-cloud secrets management environments, the next step is extending it to edge locations, on-premises systems, and hybrid cloud environments. Two main challenges appear here:

1. Unreliable connectivity: Cloud connections are fast and stable, but edge or on-prem locations often experience downtime or slow links. This can delay secret synchronization, leaving dynamic or recently rotated secrets out of date.
2. Data-residency and compliance: Industries such as healthcare, finance, and government require managing secrets within the same geographic region as the data they protect. For instance, an EU on-prem secret can't legally reside in a U.S.-hosted governance layer.

Let's discuss how you can overcome these issues.

Solve connectivity with a cache-and-sync model

On each of your edge or on-premise systems, deploy a lightweight software agent that periodically fetches required secrets from the central governance plane and caches them in an encrypted local vault.

If the connection breaks down, your local workloads continue using the cached secrets. When restored, the agent compares timestamps, purges outdated credentials, and resynchronizes with the governance plane.

Structural representation of a cache-and-sync central governance model

The design mirrors Starbucks' implementation, in which each store runs a local cluster that can rotate and serve secrets independently when disconnected from the cloud. This proves that you can replicate this model on a large scale.

Also, instead of incurring operational overhead from deploying and maintaining your own custom agent, you can use the hybrid connectors of secrets management platforms, such as Doppler, to securely sync and cache your secrets.

Address compliance with location-aware policies.

For on-prem environments that must meet geographic or regulatory requirements, use hybrid connectors that pull policies and logs from the cloud but prevent secrets from being pushed upward.

The central governance plane still aggregates audit data across all regions, but the secrets themselves remain within their jurisdiction.

You can also define location-based policies, such as:

These policies ensure that region-tagged secrets are stored only in the vault for their corresponding location.

Step 6: Build a unified audit and compliance layer

Once your governance plane oversees secret policies and lifecycle automation across multiple clouds, on-premises, and edge systems, you need a single point of visibility into how those secrets are used.

Set up a Security Information and Event Management (SIEM) platform to act as the audit and continuous monitoring component of your governance layer. Tools such as Splunk, Microsoft Sentinel, or Elastic SIEM can centralize activity data from all your environments.

Follow these steps to unify and normalize your audit trail:

• Aggregate logs
  Ingest secret-related events from all sources, such as AWS CloudTrail, Azure Activity Log, Doppler Audit Logs, Kubernetes API Server, and CI/CD systems, into your chosen SIEM.
• Normalize formats
  Different systems describe similar events in different ways (status: 200 vs. event: secret_read_success).
  
  A consistent schema allows your security team to query and correlate events across environments.
  
  Use your governance layer or a log-processing pipeline to map them into a consistent structure:

• Tag with metadata
  Add key attributes such as owner, application, environment, and region to every event. Tags like team:devops, env:production, or location:eu make audits more meaningful and simplify compliance reporting.

With these steps in place, a single search query or API call can answer questions such as "who accessed what secret, when, and from which environment or region?"

Also, stale or over-privileged secrets can be detected early, before they grow into unmanaged sprawl. This level of visibility is only possible through a functioning secrets governance framework, a framework architected from multiple tools working together.

Reference architecture for secrets governance

At its core, secrets governance unifies the tools you already use into a structured system made up of several layers. Each layer manages a specific aspect of the lifecycle, including storage, access, automation, and auditing, all working together under one governance framework.

Let’s look at each layer’s purpose and standard toolset:

As the table shows, each layer plays a role in delivering complete secret governance. The model in no way replaces your existing systems. Instead, it orchestrates them to strengthen security posture, maintain consistency, and provide room for future growth and new technologies.