How to Manage Secrets in DevOps Workflows

In today's interconnected digital landscape, secrets—API keys, database passwords, encryption certificates, and authentication tokens—represent the keys to your kingdom. A single exposed credential can cascade into catastrophic breaches, data loss, and regulatory penalties that devastate organizations both financially and reputationally. Yet despite these stakes, many development teams continue treating secrets management as an afterthought, hardcoding credentials into repositories or sharing them through insecure channels. The consequences of this negligence have never been more severe, with automated scanners constantly crawling public repositories and exploiting exposed secrets within minutes of publication.

Secrets management encompasses the policies, tools, and practices organizations implement to control access to sensitive credentials throughout the software development lifecycle. It's not merely about storing passwords securely—it's about establishing comprehensive governance over who can access what, when, and under what circumstances. This multidimensional challenge requires balancing security requirements with developer productivity, compliance mandates with operational efficiency, and centralized control with distributed architectures. Different perspectives exist on the optimal approach: security teams prioritize zero-trust architectures and minimal access privileges, developers advocate for frictionless workflows that don't impede velocity, and operations teams focus on auditability and incident response capabilities.

This comprehensive guide delivers actionable strategies for implementing robust secrets management within your DevOps pipelines. You'll discover practical frameworks for selecting appropriate tools, establishing governance policies that teams actually follow, and integrating secrets management seamlessly into continuous integration and deployment workflows. Whether you're securing a startup's first production environment or modernizing enterprise infrastructure, you'll gain the knowledge to protect your organization's most sensitive credentials while maintaining the agility that DevOps promises.

Understanding the Secrets Management Challenge

The fundamental problem with secrets management stems from an inherent tension: secrets must be both highly secure and readily accessible. Applications need credentials to function, developers require access to build and test, and automated systems must authenticate without human intervention. Traditional approaches like embedding credentials in configuration files or environment variables create numerous vulnerabilities. These methods lack audit trails, make rotation nearly impossible, and frequently result in secrets proliferating across multiple systems where tracking becomes unmanageable.

Modern cloud-native architectures amplify these challenges exponentially. Microservices communicate across network boundaries, containers spin up and down dynamically, and infrastructure scales automatically in response to demand. Each component potentially requires unique credentials, and the ephemeral nature of cloud resources means static credential distribution becomes impractical. Additionally, compliance frameworks like SOC 2, PCI-DSS, and GDPR impose strict requirements around secrets access, rotation, and auditability that manual processes simply cannot satisfy at scale.

"The biggest security vulnerability in most organizations isn't sophisticated zero-day exploits—it's the database password sitting in a GitHub repository that's been there for three years."

The attack surface expands with every secret created. Developers' laptops contain credentials for testing environments, CI/CD systems store production access tokens, configuration management tools maintain infrastructure passwords, and backup systems hold encryption keys. Each location represents a potential breach point, and the interconnected nature of modern systems means compromising one secret often provides pathways to others. Attackers understand this topology intimately, which is why credential theft remains among the most common attack vectors.

Common Secrets Management Anti-Patterns

Recognizing dysfunctional patterns is the first step toward improvement. Organizations frequently fall into predictable traps that seem convenient initially but create significant technical debt and security exposure over time. Hardcoding credentials directly into application source code remains surprisingly prevalent, particularly in legacy systems or during rapid prototyping that becomes permanent. This practice makes secrets visible to anyone with repository access, persists them in version control history indefinitely, and requires code changes for routine credential rotation.

• 💾 Configuration file sprawl: Storing secrets in unencrypted configuration files scattered across servers, with no centralized inventory or access control
• 📧 Email and chat transmission: Sharing credentials through Slack messages, email threads, or documentation wikis where they remain searchable and accessible
• 📝 Shared credential accounts: Multiple team members using the same administrative password, eliminating accountability and making revocation impossible
• 🔄 Infrequent rotation: Leaving credentials unchanged for months or years, maximizing the window of opportunity if compromise occurs
• 🔓 Overly permissive access: Granting broad permissions to credentials when narrow, scoped access would suffice

Another pervasive anti-pattern involves treating different environments inconsistently. Organizations might implement robust secrets management for production while allowing development and staging environments to operate with lax controls. This approach ignores that non-production environments often contain realistic data, provide stepping stones to production access, and frequently lack the monitoring that might detect compromise. Attackers specifically target these "softer" environments as entry points to more valuable systems.

Establishing Secrets Management Principles

Effective secrets management rests on foundational principles that guide tool selection, policy development, and operational practices. The principle of least privilege dictates that every application, service, and user should receive only the minimum credentials necessary to perform their specific function. This containment strategy limits blast radius when credentials are compromised and simplifies reasoning about access patterns. Implementing least privilege requires granular permission systems and the discipline to resist granting excessive access for convenience.

Secrets should never be long-lived when short-lived alternatives exist. Dynamic secrets that expire automatically after defined periods dramatically reduce risk by ensuring that stolen credentials have limited utility. This approach requires infrastructure that can request and receive new credentials programmatically, but the security benefits justify the investment. Similarly, just-in-time access patterns where credentials are generated on-demand for specific operations and immediately revoked eliminate the need for standing permissions that might be exploited.

"Security and convenience are not opposites—they're complementary when you build the right systems. Developers shouldn't even know where production credentials are stored, and that's exactly how it should work."

Auditability represents another non-negotiable principle. Every access to every secret must generate immutable logs that capture who accessed what, when, from where, and whether the access was authorized. These audit trails serve multiple purposes: detecting anomalous access patterns that might indicate compromise, satisfying compliance requirements, and investigating incidents after they occur. Without comprehensive logging, secrets management becomes security theater rather than actual protection.

Separation of Duties and Access Control

Implementing separation of duties prevents any single individual from having complete control over secrets lifecycle. The person who creates a secret should differ from who approves its use, and those who access secrets in production should be distinct from those who can modify access policies. This separation creates checks and balances that make insider threats and accidental misconfigurations less likely. In practice, this might mean security teams define policies, operations teams implement them, and automated systems enforce them, with no single group able to circumvent controls unilaterally.

Role-based access control provides the mechanism for implementing these separations effectively. Rather than granting permissions to individuals directly, organizations define roles that encapsulate specific responsibilities and assign users to roles based on their job functions. This abstraction simplifies permission management, makes access reviews more tractable, and ensures that personnel changes don't require reconfiguring individual secrets. Roles should be defined narrowly enough to enforce least privilege but broadly enough to avoid administrative overhead that encourages workarounds.

Selecting Secrets Management Tools

The secrets management tooling landscape offers numerous options, from cloud provider native solutions to specialized third-party platforms and open-source alternatives. Selection criteria should prioritize integration capabilities with your existing infrastructure, support for your specific use cases, operational complexity, and total cost of ownership. Cloud-native solutions like AWS Secrets Manager, Azure Key Vault, and Google Secret Manager provide tight integration with their respective platforms and simplified operations, making them excellent choices for organizations heavily invested in a single cloud ecosystem.

HashiCorp Vault has emerged as the de facto standard for platform-agnostic secrets management, offering exceptional flexibility and powerful features like dynamic secrets generation, encryption as a service, and sophisticated access policies. Vault's learning curve is steeper than managed alternatives, and operating it reliably requires significant expertise, but the investment pays dividends for organizations with complex, multi-cloud environments or stringent security requirements. The open-source core provides tremendous value, while enterprise features add high availability, disaster recovery, and advanced governance capabilities.

"The best secrets management tool is the one your team will actually use consistently. A perfect solution that creates too much friction will be circumvented, while a good solution that fits naturally into workflows will be adopted."

Kubernetes-native options like Sealed Secrets and External Secrets Operator address container orchestration environments specifically. These tools integrate directly with Kubernetes resource models, making secrets management feel native to developers already comfortable with pods, deployments, and services. For organizations running primarily containerized workloads, these specialized tools often provide better developer experience than general-purpose solutions, though they typically require complementary tools for non-Kubernetes secrets.

Evaluating Tool Capabilities

Dynamic secrets generation represents one of the most valuable capabilities to seek in secrets management platforms. Rather than storing static database passwords or API keys, systems with dynamic secrets can generate credentials on-demand with specific permissions and automatic expiration. When an application requests database access, the secrets manager creates a unique username and password valid for a defined period, grants it precisely the required permissions, and automatically revokes access when the lease expires. This approach eliminates credential sharing, simplifies rotation, and dramatically limits exposure windows.

Encryption capabilities extend beyond merely encrypting stored secrets. Advanced platforms offer encryption as a service, allowing applications to encrypt and decrypt data using centrally managed keys without the keys ever leaving the secrets management system. This pattern keeps cryptographic operations under centralized control while enabling applications to protect sensitive data. Similarly, support for various secret types—key-value pairs, certificates, SSH keys, database credentials—with type-specific handling improves usability and security.

Implementing Secrets in CI/CD Pipelines

Continuous integration and deployment pipelines present unique secrets management challenges. Build processes require credentials to access source repositories, artifact registries, testing environments, and deployment targets. These secrets must be available to automated systems while remaining protected from exposure in logs, artifacts, or to unauthorized users. The ephemeral nature of CI/CD runners—spinning up to execute jobs and terminating afterward—makes traditional credential distribution impractical and creates opportunities for dynamic secrets approaches.

Modern CI/CD platforms like GitHub Actions, GitLab CI, and Jenkins offer native secrets management features that provide baseline protection. These systems encrypt secrets at rest, inject them into build environments as environment variables, and mask them in log output. However, these built-in capabilities represent starting points rather than complete solutions. Secrets still exist as long-lived values that require manual rotation, and access control often lacks the granularity needed for least privilege implementation. Integrating dedicated secrets management platforms elevates security significantly.

"Your CI/CD pipeline is only as secure as the secrets it uses. If your deployment process can access production databases with static credentials, you've created an attack vector that bypasses all your runtime security controls."

The integration pattern typically involves authenticating the CI/CD runner to the secrets management platform using short-lived tokens based on the job context, then retrieving only the specific secrets required for that particular build or deployment. For example, a GitHub Actions workflow might authenticate to Vault using GitHub's OIDC identity, receive a time-limited token scoped to the specific repository and branch, and use that token to retrieve database credentials valid only for the test environment. This approach chains multiple security mechanisms—OIDC authentication, time-limited tokens, scoped permissions—creating defense in depth.

Securing Deployment Credentials

Deployment processes require elevated privileges to modify production infrastructure, making deployment credentials particularly high-value targets. Traditional approaches that store cloud provider access keys or Kubernetes service account tokens in CI/CD secrets stores create persistent attack surfaces. Instead, leverage platform-native identity mechanisms where possible. Cloud providers offer workload identity features that allow CI/CD runners to assume roles without long-lived credentials, using the platform's trust relationship with the CI/CD provider.

For AWS deployments, GitHub Actions can assume IAM roles using OpenID Connect without storing AWS access keys. The trust relationship is established once, defining which repositories and branches can assume which roles, and individual workflow runs receive temporary credentials automatically. This pattern eliminates static credentials entirely from the CI/CD system while providing granular control over what each workflow can deploy. Similar capabilities exist across Azure, Google Cloud, and Kubernetes, though implementation details vary.

• 🔐 Authenticate using workload identity: Leverage OIDC or similar mechanisms to authenticate CI/CD runners without static credentials
• ⏱️ Request minimum duration tokens: Set token lifetimes to the minimum required for job completion, typically minutes rather than hours
• 🎯 Scope permissions precisely: Grant deployment credentials only the specific permissions needed for the deployment task
• 📊 Monitor credential usage: Alert on unusual patterns like credentials used from unexpected locations or outside normal deployment windows
• 🔄 Rotate regularly: Even for dynamic credentials, rotate the underlying trust relationships and authentication mechanisms periodically

Managing Secrets in Application Runtime

Applications running in production require access to secrets throughout their lifecycle, not just at startup. Database connections, API integrations, and encryption operations all depend on credentials that must be available when needed. The challenge lies in providing this access without embedding secrets in container images, configuration files, or environment variables where they persist longer than necessary and become difficult to rotate. Runtime secrets management requires tight integration between applications and secrets management platforms.

The sidecar pattern has emerged as a popular architecture for injecting secrets into containerized applications. A sidecar container runs alongside the application container within the same pod, handling authentication to the secrets management system, retrieving secrets, and making them available to the application through shared volumes or local APIs. HashiCorp Vault's Agent Injector exemplifies this approach, automatically injecting a Vault agent sidecar that manages token renewal and secret retrieval without application code changes. The application simply reads secrets from a file path, while the sidecar handles all secrets management complexity.

"Applications should consume secrets, not manage them. The moment you start implementing credential rotation logic in application code, you've created technical debt that will haunt you across every service."

For applications that can integrate directly with secrets management APIs, the SDK approach provides more control and flexibility. Libraries exist for most programming languages that handle authentication, token renewal, and secret caching transparently. Direct integration enables applications to request secrets lazily—only retrieving credentials when actually needed rather than at startup—and to refresh secrets proactively before expiration. This approach requires more development effort but eliminates the operational overhead of sidecar containers and reduces resource consumption.

Handling Secret Rotation

Credential rotation—regularly changing secrets to limit exposure windows—represents a critical security practice that many organizations struggle to implement effectively. The difficulty stems from the coordination required: new credentials must be created, distributed to all consumers, verified as working, and only then can old credentials be revoked. This sequence becomes exponentially more complex as the number of secret consumers increases. Automated rotation mechanisms that handle this orchestration become essential at scale.

Dynamic secrets naturally support rotation since they're generated with defined lifetimes and automatically expire. Applications designed to work with dynamic secrets request new credentials as needed rather than caching them indefinitely. For static secrets that cannot be made dynamic, implementing rotation requires careful planning. Database passwords, for example, can be rotated using a versioning approach: create a new password, add it as an additional valid credential, update all consumers to use the new password, verify functionality, then remove the old password. This sequence ensures zero-downtime rotation.

Rotation schedules should reflect risk levels and compliance requirements. High-privilege credentials like database administrative passwords warrant monthly or even weekly rotation, while lower-risk secrets might rotate quarterly. However, automated rotation enables much more aggressive schedules without operational burden. Some organizations rotate all production secrets daily, dramatically limiting the value of stolen credentials. The key is ensuring rotation is fully automated and tested regularly—manual rotation processes inevitably get deprioritized and skipped.

Secrets Management for Infrastructure as Code

Infrastructure as code introduces the challenge of managing secrets that define infrastructure itself: cloud provider credentials, encryption keys for Terraform state, and sensitive configuration values embedded in infrastructure definitions. These secrets must be available to infrastructure provisioning tools while remaining protected in version control systems. Simply committing secrets to Git repositories—even private ones—violates security principles and creates compliance violations, yet infrastructure code without the associated secrets cannot be executed.

Encrypted secrets in Git represents one approach to this challenge. Tools like Mozilla SOPS and git-crypt encrypt secret values before committing them to version control, allowing infrastructure code and secrets to coexist in the same repository. SOPS integrates with cloud provider key management services, encrypting files using KMS keys and decrypting them during infrastructure operations. This approach maintains the benefits of version control—history, collaboration, code review—while protecting sensitive values. However, it requires careful key management and doesn't provide the dynamic secrets capabilities of dedicated platforms.

The external secrets pattern separates secrets from infrastructure code entirely. Infrastructure definitions reference secrets by identifier rather than including actual values, and provisioning tools retrieve the current secret values from a secrets management platform at runtime. Terraform, for example, can read secrets from Vault, AWS Secrets Manager, or Azure Key Vault during plan and apply operations. This separation enables secrets rotation without infrastructure code changes and ensures that infrastructure repositories contain no sensitive data whatsoever.

Terraform and Secrets Management Integration

Terraform's state files present a particular challenge since they often contain sensitive values from resources that include secrets. Even when using external secrets, Terraform state might capture database passwords, API keys, or other credentials. Storing state in encrypted backends becomes mandatory rather than optional. All major cloud providers offer state storage solutions with encryption at rest and in transit: AWS S3 with encryption, Azure Storage with encryption, and Google Cloud Storage with encryption. Additionally, state locking prevents concurrent modifications that might expose secrets during operations.

Terraform providers for secrets management platforms enable infrastructure code to both read existing secrets and create new ones. When provisioning a database, Terraform can generate a random password, store it in Vault or a cloud secrets manager, and configure the database to use that password—all in a single operation. This pattern ensures secrets are generated securely, stored properly, and never exposed in logs or state files in plaintext. Provider-specific encryption can further protect state files, though this adds complexity to disaster recovery scenarios.

• 🔒 Encrypt state files: Always store Terraform state in encrypted backends with access controls
• 🎲 Generate secrets in infrastructure code: Use Terraform's random provider to create passwords rather than defining them manually
• 📦 Store generated secrets immediately: Write generated secrets to secrets management platforms as part of the same Terraform operation
• 🔍 Review state for sensitive data: Regularly audit state files for secrets that shouldn't be there and refactor to eliminate them
• ⚙️ Use remote operations: Execute Terraform in controlled environments rather than on developer workstations to limit state exposure

Monitoring and Auditing Secrets Access

Comprehensive monitoring and auditing transform secrets management from a preventive control into a detective one as well. Every interaction with secrets—creation, access, modification, deletion—should generate audit events that flow into centralized logging systems. These logs enable security teams to detect anomalous patterns that might indicate compromise, satisfy compliance requirements for access tracking, and investigate incidents when they occur. Without robust auditing, secrets management provides a false sense of security since breaches may go undetected indefinitely.

Effective audit logs capture sufficient context to enable meaningful analysis. Beyond simply recording that a secret was accessed, logs should include the identity of the accessor, the mechanism used for authentication, the source IP address or workload identity, the specific secret accessed, and whether the access was authorized. Timestamp precision matters for correlation with other security events, and immutability ensures logs cannot be tampered with after the fact. Streaming audit logs to write-once storage or SIEM systems provides this immutability.

"Audit logs are worthless if nobody reviews them. The goal isn't generating logs—it's detecting anomalies quickly enough to respond before damage occurs."

Alerting rules should flag high-risk patterns for immediate investigation. Access to production secrets from development environments, credential access outside normal business hours, failed authentication attempts that might indicate brute force attacks, or sudden spikes in access volume all warrant alerts. Machine learning-based anomaly detection can identify subtle patterns that rule-based systems miss, though simpler approaches often provide substantial value. The key is ensuring alerts route to teams capable of responding and that alert fatigue doesn't cause important signals to be ignored.

Compliance and Reporting Requirements

Regulatory frameworks impose specific requirements around secrets management that audit logs must support. PCI-DSS requires tracking access to cardholder data environments, SOC 2 mandates logging of administrative access to systems, HIPAA demands audit trails for protected health information access, and GDPR requires demonstrating appropriate security measures for personal data. Meeting these requirements necessitates not only generating appropriate logs but also retaining them for specified periods and producing reports that demonstrate compliance.

Regular access reviews represent another compliance requirement that proper auditing enables. Quarterly or annual reviews should examine who has access to which secrets, whether that access remains appropriate given current roles, and whether any anomalous access patterns exist in historical logs. Automated reporting that shows secret access by user, by secret, and by time period simplifies these reviews dramatically. The reports should highlight dormant accounts with access, overly broad permissions, and secrets that haven't been rotated within policy timeframes.

Evidence collection for audits becomes straightforward with comprehensive logging. Rather than scrambling to demonstrate secrets management practices during audit season, organizations with mature monitoring can produce reports showing access patterns, rotation history, policy changes, and incident responses. This documentation not only satisfies auditors but also provides valuable input for improving security posture. Trends in the data might reveal that certain types of secrets are accessed far more frequently than necessary, indicating opportunities for architecture improvements.

Incident Response for Secrets Compromise

Despite best efforts, secrets compromise will eventually occur. Detecting and responding to these incidents quickly minimizes damage. Incident response plans specific to secrets compromise should define clear procedures: how to determine the scope of compromise, which secrets to rotate immediately versus which can wait, how to identify and remediate any damage caused by unauthorized access, and how to prevent similar incidents in the future. These plans require regular testing through tabletop exercises and simulated incidents.

The first step in responding to suspected compromise is determining what was accessed. Audit logs become critical here, showing exactly which secrets the compromised credential accessed and when. This information bounds the incident scope and informs remediation efforts. If logs show a compromised API key only accessed development environment secrets, response can focus there rather than assuming production compromise. Conversely, if production database credentials were accessed, immediate rotation and potential data breach investigation become necessary.

Rotation procedures must be executable quickly under pressure. Documented runbooks that walk responders through rotating each type of secret, updating all consumers, and verifying functionality enable rapid response even during stressful incidents. Automation accelerates this process dramatically—scripts that rotate credentials across multiple systems simultaneously, update configuration management, and restart affected services can complete in minutes what might take hours manually. Regular testing ensures these automation systems work when needed.

Post-Incident Analysis and Improvement

Every secrets compromise incident provides opportunities to strengthen defenses. Post-incident reviews should examine not only how the compromise occurred but also why existing controls failed to prevent it. Was it a process failure where someone circumvented proper procedures? A technical gap where a particular secret type lacked adequate protection? Or a detection failure where compromise occurred but wasn't noticed promptly? Understanding root causes enables targeted improvements rather than generic security enhancements.

Common findings from post-incident reviews include overly broad secret permissions that allowed lateral movement after initial compromise, insufficient monitoring that delayed detection, or missing automation that would have enabled faster response. Each finding should translate into concrete action items with owners and deadlines. The goal is continuous improvement of the secrets management program, using incidents as learning opportunities rather than merely problems to solve and forget.

Building a Secrets Management Culture

Technology and processes alone cannot secure secrets—organizational culture must reinforce security practices. Developers need to understand not just how to use secrets management tools but why proper secrets handling matters. Security training should include concrete examples of breaches caused by mismanaged secrets, demonstrating real consequences rather than abstract risks. When team members understand that exposed credentials can lead to data breaches affecting real people, compliance becomes more than checking boxes.

Making secure practices convenient encourages adoption. If retrieving secrets from the secrets management platform is more difficult than hardcoding them, developers will find workarounds. Investing in developer experience—clear documentation, simple APIs, IDE integrations, and helpful error messages—pays dividends in compliance. Security teams should view themselves as enablers rather than gatekeepers, providing tools and support that make doing the right thing easier than doing the wrong thing.

Recognition and accountability reinforce desired behaviors. Celebrating teams that implement exemplary secrets management, highlighting security improvements in company communications, and including security practices in performance evaluations signal that the organization values these efforts. Conversely, accountability for security violations—not punitive measures that discourage reporting but clear expectations and consequences—ensures standards are maintained. The balance between recognition and accountability shapes culture more than any policy document.

Frequently Asked Questions

What is the difference between secrets management and password management?

Password management focuses on human-used credentials for accessing systems and applications, typically storing passwords in encrypted vaults for individual users. Secrets management encompasses a broader scope including application credentials, API keys, database passwords, encryption certificates, and other non-human credentials used by systems to authenticate to each other. While password managers help individuals organize their credentials, secrets management platforms provide programmatic access, dynamic secret generation, fine-grained access policies, and audit capabilities required for automated systems and DevOps workflows.

How often should secrets be rotated?

Rotation frequency depends on secret sensitivity, compliance requirements, and operational capabilities. High-privilege credentials like production database administrative passwords should rotate monthly or more frequently, while lower-risk secrets might rotate quarterly. Dynamic secrets that expire automatically within hours or days provide superior security to any static secret rotation schedule. Compliance frameworks often mandate minimum rotation frequencies: PCI-DSS requires quarterly rotation for certain credentials, while other frameworks specify rotation after personnel changes. The key is automating rotation completely so frequency can be increased without operational burden.

Can secrets management tools work across multiple cloud providers?

Platform-agnostic tools like HashiCorp Vault excel in multi-cloud environments, providing consistent secrets management across AWS, Azure, Google Cloud, and on-premises infrastructure. These tools abstract away provider-specific differences, allowing organizations to implement unified policies and workflows regardless of where workloads run. Cloud-native solutions like AWS Secrets Manager or Azure Key Vault work best within their respective ecosystems but can be used alongside other providers with additional integration work. Many organizations adopt hybrid approaches, using cloud-native solutions for provider-specific secrets while managing cross-cloud credentials in platform-agnostic tools.

What happens if the secrets management system becomes unavailable?

Secrets management platforms represent critical infrastructure requiring high availability design. Most production deployments implement redundancy across availability zones or regions, automated failover, and regular backup and recovery testing. For applications, implementing local secret caching with reasonable time-to-live values provides resilience during brief outages, though cache duration must balance availability against security. Disaster recovery procedures should include documented steps for accessing encrypted backups of secrets if the primary system fails catastrophically. Organizations should regularly test these procedures to ensure recovery capabilities match availability requirements.

How do you migrate from hardcoded secrets to a secrets management platform?

Migration requires systematic inventory of all secrets across codebases, configuration files, and infrastructure definitions. Start by cataloging where secrets currently exist, then prioritize based on sensitivity and exposure risk. Begin migration with new applications or services to establish patterns before tackling legacy systems. For each application, implement secrets management integration, store secrets in the platform, update code to retrieve secrets programmatically, and verify functionality in non-production environments before production cutover. Remove hardcoded secrets from repositories after migration, though note they remain in Git history unless repositories are rewritten. The process typically spans months for large organizations, with incremental progress across teams rather than attempting wholesale migration simultaneously.

Are there open-source secrets management solutions suitable for production use?

Several open-source solutions provide production-grade secrets management capabilities. HashiCorp Vault's open-source core offers robust features including dynamic secrets, encryption as a service, and multiple authentication backends, though enterprise features like disaster recovery and advanced replication require commercial licensing. Kubernetes-native options like Sealed Secrets and External Secrets Operator work well for container-focused organizations. Mozilla SOPS provides Git-integrated encryption suitable for infrastructure as code workflows. The viability of open-source solutions depends on your team's operational capabilities—these tools require expertise to deploy and maintain reliably, but organizations with strong platform engineering teams successfully run them at scale.