AI development may be changing how applications are built, but many of the security problems underneath them are familiar. Applications need credentials to connect to models, cloud services, databases, development tools, and other systems. As teams experiment with new AI services and move applications toward production, those credentials can spread across repositories and development workflows just as other secrets do. An API key gets added to a configuration file to get a prototype running. A developer uses it during testing. The application evolves, the team moves on, but the credential remains.
Now the organization has two problems: How do you find credentials that have ended up where they shouldn't be, and once you find them, how do you keep them from ending up there again? That is where secret discovery and secrets management need to come together.
»AI adds another dimension to secrets sprawl
The problem starts well before a credential is compromised. A credential that lives outside an organization's secrets management practices can be difficult to inventory, monitor, and remediate. And simply searching today's code isn't necessarily enough. Credentials can persist in Git history even after they have been removed from the latest version of a file. AI applications often depend on model providers, cloud infrastructure, vector databases, and developer tooling, all of which introduce additional credentials that need to be managed securely.
HCP Vault Radar is designed to identify unmanaged secrets throughout development workflows. It scans connected data sources when they're added and as new commits and pull requests are created. Deep scanning can identify secrets in Git history, and supported secret types can include an activeness check to help teams understand which findings demand attention. That applies directly to AI development. Vault Radar currently recognizes multiple OpenAI credentials as built-in secret types, including admin, project, service account, user, and API keys. All five documented OpenAI secret types support activeness checks. The broader default detection library also covers credentials for services such as AWS and Microsoft Azure, which can be part of the infrastructure surrounding an AI application. So imagine that a development team accidentally commits an OpenAI API key to a repository. Discovering it is important but it isn't the end of the story.
»You found an exposed credential. Now what?
Finding a secret tells you where there is a problem but doesn't automatically establish where that credential should live going forward. A team can remove a credential from code, but without changing how the application receives and manages credentials, it's easy to recreate the same problem later. And source code may not be the only place that credential has surfaced. Collaboration platforms such as Confluence, Jira, Slack, and Microsoft Teams sit alongside development workflows, capturing conversations, troubleshooting, and handoffs throughout the build, test, and release lifecycle. Developers may share a credential to solve an immediate problem, only for it to remain in a message, ticket, or project page long after that work is complete. Vault Radar can scan these collaboration environments to help teams identify secrets that may otherwise remain outside traditional code-scanning efforts. Remediation should therefore address more than the individual finding. It should move the credential toward a more consistent management model.
HCP Vault Dedicated provides a hosted Vault Enterprise environment for securing, storing, and controlling access to sensitive credentials including tokens, passwords, certificates, and encryption keys. The goal is a simple shift: Instead of applications and developers becoming the places where credentials are managed, Vault becomes the system responsible for managing them. For our hypothetical exposed AI key, the team can first address the compromised credential with the provider and then store the replacement credential in Vault rather than putting it back into source code. From there, the application or AI agent can be updated to retrieve the credential from Vault when it needs it, rather than relying on a secret embedded in corde or configuration. This creates a more durable response to secret exposure. The team hasn't only cleaned up one finding. It has changed how that credential is managed.
»Keep credentials managed without disrupting application workflows
Centralizing a credential raises another practical question: How does an application get the secret it needs? Ideally, applications retrieve secrets through Vault. But that isn't practical for every environment.
Vault secrets sync supports environments where secrets need to be available through other secrets managers. Secrets sync keeps Vault as the system of record while synchronizing supported secrets to destinations including AWS Secrets Manager, Azure Key Vault, Google Cloud Secret Manager, GitHub Repository Actions, and Vercel Projects. That means remediation does not have to end with removing a key from GitHub. A team can replace the exposed credential, bring the new value under management in Vault for centralized visibility and management, and make a supported Vault-managed secret available to applications through the appropriate destination. The application can still get the credential where it needs it without putting the credential back into source code. Instead, the application code can reference the path or variable that points to where the secret is securely stored in Vault, allowing the application to retrieve it when needed rather than embedding the credential itself. Where supported, organizations can also reduce reliance on long-lived credentials by adopting short-lived or dynamically generated credentials through Vault.
»Turn discovery into a repeatable security practice
AI may increase the number and variety of credentials organizations need to manage, but it does not require an entirely new approach to secrets security. The better approach is to connect discovery with management and to find credentials that are exposed, address the immediate exposure, and bring the replacement credential under centralized management with secure, just in time access so the same problem is less likely to happen again.
With Vault Radar and Vault, organizations can turn secret discovery from a point-in-time cleanup exercise into part of an ongoing approach to managing credentials as AI development grows.
»Get started
Build a more resilient approach to credential risk. Learn how organizations can move beyond finding exposed secrets to reducing credential risk across the application lifecycle in the credential risk whitepaper.
Ready to get started? Explore Vault Radar to see how secret scanning can help uncover unmanaged credentials across development workflows, and HCP Vault Dedicated to learn how those credentials can be brought under centralized secrets management.
