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Keep every API key, access token, password, private key, and service credential out of source code, client applications, prompts, and agent-readable files. Use a backend or workload to call AI services, store production secrets in a managed secret system or supported short-lived identity, scope each credential to one job and environment, scan before code is pushed, monitor use and spend, and revoke immediately when exposure is suspected. An environment variable is useful for local development, but it is not a complete production secret-management or AI-agent security strategy.
What you are protecting
API keys are application secrets
An API key authorizes software to call a service. Depending on its permissions, an exposed key can allow unauthorized requests, consume quota, create unexpected charges, or access project data. Treat access tokens, cloud credentials, database passwords, private keys, signing secrets, and service-account credentials the same way. The value does not become safe because it is short-lived, stored in a configuration file, or used only by an AI feature.
OpenAI’s API Reference states: “Remember that your API key is a secret! Do not share it with others or expose it in any client-side code (browsers, apps).” Its account-security guidance also calls committing a key to source code “a common vector for credential compromise.”
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A human password authenticates a person to an account. An application key authenticates a program, workload, project, or integration. They need different controls:
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- Use a password manager, multifactor authentication, recovery methods, and session review for human sign-in accounts.
- Use scoped permissions, separate environments, expiration or rotation, workload identity, audit logs, and automated deployment controls for application credentials.
- Never substitute a team member’s personal password for a service credential. Personal credentials make ownership unclear and make offboarding and incident investigation harder.
Why AI tools add another exposure path
A coding assistant or autonomous agent may read a workspace, inspect environment variables, run shell commands, view terminal output, use an IDE context window, call connected tools, or access Model Context Protocol (MCP) servers. A secret can therefore leak without ever being committed to Git. A local secret file excluded by .gitignore may still be readable by an agent that can inspect the directory. A key printed during a debugging command may become part of captured terminal history or an agent transcript.
Think of the complete exposure surface as the union of everything that can see the value: source files, build logs, shell history, process environments, IDE indexing, prompts, agent transcripts, CI jobs, deployment systems, connected tools, MCP servers, backups, and observability platforms.
Use a layered credential workflow
No single setting protects every stage. A practical workflow has six layers:
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- Inventory: identify where the AI tool, developer process, CI job, and deployment workload can read or send data.
- Placement: keep local development values outside committed source and keep production values in a managed secret store or supported workload-identity system.
- Isolation: use separate credentials for people, projects, products, integrations, and development, staging, and production.
- Authorization: grant only the API operations and resources required for the specific task, with an expiration or short lifetime where supported.
- Detection: scan locally, in CI, and at repository boundaries; configure push protection where the repository and plan support it.
- Response: revoke first, replace and deploy, inspect activity and spend, preserve incident details, and secure the associated human account if it may also be compromised.
These layers are complementary. Scanning is a safety net, not a substitute for safe storage, and an environment variable is a transport mechanism, not an authorization model.
Start with an access inventory
Before choosing a storage mechanism, document what each tool can access. Perform this review for every assistant, agent, CI workflow, and integration rather than assuming that “local” means isolated.
- List the credential: record its owner, service, project, environment, permissions, creation date, expiration, and replacement contact. Do not paste the value into the inventory.
- Map readers: note whether a developer process, deployment workload, CI runner, IDE, agent, shell, MCP server, or monitoring system can read it.
- Map copies: check source trees, configuration files, shell history, crash reports, build artifacts, logs, caches, backups, and ticket or chat systems.
- Map outbound paths: identify tools that can upload files, post messages, open pull requests, call external APIs, or send telemetry.
- Set an owner and expiry: someone must be responsible for rotation, usage review, and emergency revocation. A credential with no owner is an incident waiting to happen.
Ask specifically whether an agent can run env, inspect parent processes, search the home directory, read ignored files, access the Git metadata directory, or invoke a connected server. Disable capabilities that are not required for the task.
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Choose storage by environment
| Approach | Where the value lives | Who can read it | Best fit | Important limitation |
|---|---|---|---|---|
| Environment variable | Developer process environment, injected at launch | The process and anything able to inspect its environment or child-process output | Local development and tightly controlled one-off commands | It can still be exposed through an agent, shell history, diagnostics, or logs; it does not provide rotation, auditing, or fine-grained authorization by itself. |
| Operating-system credential store | Protected local keychain or credential service | Approved local applications under the user or machine policy | Developer workflows that support a credential-store integration | Agent and process permissions still matter; a tool allowed to retrieve the value can use it. |
| Managed secret store | Central service with encryption, access policy, audit trail, and versioning | Only the deployment identity or approved operator path | Production services, CI, and teams needing rotation and auditability | Requires access-policy design, availability planning, and a tested recovery process. |
| Workload identity or federation | No long-lived application key; a workload obtains a short-lived credential | An attested deployment or runtime identity | Supported production workloads where the provider offers federation | Provider and platform eligibility varies, and identity configuration must be secured. |
| Browser or mobile-embedded key | Downloaded into a user-controlled client | Every user, browser extension, debugger, or modified app that can inspect the client | Only narrowly constrained public-client designs, never as a general secret | It is not confidential. Use a backend broker instead. |
Local development
Keep a development credential outside committed application source. An approved environment-variable flow or operating-system credential store is preferable to a hand-edited configuration file. If a file is unavoidable, place it outside the agent’s workspace and enforce restrictive permissions; do not rely on .gitignore to stop an agent from reading it.
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Do not print the value to verify that it loaded. A command such as echo $API_KEY can place the secret in terminal capture, shell history, an IDE transcript, or an agent context. Test with a harmless authenticated request that reports only a success or failure status, and redact request and response logs.
Use a development-only key with a small quota and the minimum API permissions. Never reuse production credentials on a laptop or in an agent session.
Production and CI
For production, inject a secret from a managed secret store at runtime or use workload identity federation when the service and platform support it. Keep the value out of images, repositories, build artifacts, and long-lived CI variables whenever the platform offers a safer identity path.
Give each deployment workload its own identity or secret. Separate development, staging, and production so a test breach cannot directly authorize production calls. Restrict the deployment role to retrieving the specific secret version it needs; do not give every build job administrator access to the entire secret store.
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Keep secrets out of AI-agent context
Prepare the workspace
- Remove keys and passwords from source files, notebooks, sample configurations, issue text, prompts, and documentation.
- Store local secret files outside directories an agent is allowed to inspect. Exclude them from version control, but remember that exclusion affects Git, not filesystem access.
- Configure the agent with an allowlist of directories and commands. Deny broad home-directory searches, unrestricted network access, and access to deployment credentials unless the task requires them.
- Use redacted fixtures and fake values for tests. A realistic-looking placeholder should not be a copied production key.
- Review every connected MCP server and tool. Remove unused servers, limit scopes, and understand whether a server can upload files, retain prompts, or call external systems.
Control commands and output
Do not paste a credential into a prompt or terminal command. Avoid commands that dump all environment variables, process arguments, configuration directories, or shell history. Ask the agent to operate on a variable name or secret reference rather than the value. Mask sensitive headers and response bodies in logs, traces, screenshots, and pull-request comments.
If an agent must call an authenticated service, prefer a narrowly scoped, short-lived credential issued for that task. Run the agent in a sandbox or separate development account, and revoke the task credential when the work ends.
Review generated changes
Inspect diffs, new files, test snapshots, and generated documentation before committing. Agents can copy a value into a configuration example, error message, or fixture even when the original file was protected. Treat any unexplained secret-shaped string as a finding until its origin and intended lifetime are known.
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Design credentials with least privilege
OWASP’s Secrets Management guidance says: “New secrets must be securely generated and cryptographically robust enough for their purpose. New secrets must have the minimum privileges assigned to them to enable their required use/role.” Apply that rule to AI integrations as deliberately as to human applications.
Separate identities
- Use distinct credentials for each person, project, product, integration, and environment.
- Use project-based keys for team collaboration instead of sharing an individual’s key. Each teammate should authenticate through their own account and receive only the project access they need.
- Give CI, production, and support tools different identities so one compromise does not unlock every path.
Limit permission and lifetime
Allow only the required API operations, model or resource groups, network origins, and spending limits. Prefer expiration dates, automatic rotation, or short-lived tokens. Where supported, workload identity federation can remove the need for a long-lived API key altogether.
Restrictions such as IP allowlists can reduce misuse but do not make an overprivileged key safe. A credential that can perform destructive operations should not be handed to a coding agent merely because requests originate from an approved network.
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Attribute usage and cost
Separate keys make usage attributable. Monitor request volume, error rates, regions, models or products called, and spend alerts for each project and environment. A sudden increase, unfamiliar location, or activity outside deployment windows should trigger investigation and, when appropriate, immediate revocation.
Can an API key go in a browser or mobile app?
Not if it must remain secret. Browser JavaScript, mobile binaries, developer tools, rooted devices, extensions, proxies, and modified clients allow the user to inspect embedded values and requests. Obfuscation, minification, and a restricted referrer do not turn a client-distributed value into a confidential credential.
Use this request flow instead:
- The browser or mobile app authenticates the user to your backend using the application’s normal session or token mechanism.
- The backend validates the user, applies quotas and authorization, and constructs the AI-service request.
- The backend retrieves its server-side credential from a managed store or workload identity and calls the provider.
- The backend returns only the response data the client is allowed to receive, with sensitive provider headers and diagnostics removed.
If a provider offers a purpose-built, temporary client token, use only that documented flow and enforce its audience, lifetime, permissions, and origin restrictions. Do not expose a general project or account key as a substitute.
Can you share an API key with a teammate?
Do not send a personal or production key through chat, email, tickets, or a shared document. Shared values obscure who made a request, survive employee changes, and force a broad rotation when one person’s device is lost.
Create a project or team access structure with distinct keys or identities for the required environments. Record the owner, permissions, expiration, and recovery path. If a teammate needs temporary access, issue a time-limited credential or an approved role and remove it when the task ends. A password manager can protect human account passwords, but it does not replace application-level scoping, auditability, or a deployment secret store.
Detect accidental exposure before it spreads
Use several scanning points
- Editor and pre-commit scanning: catch high-entropy strings and known credential formats before they enter a commit.
- CI scanning: inspect changed files, build output, and generated artifacts under a service account with no production-secret read permission.
- Repository history scanning: search existing commits, branches, pull requests, and releases because deleting a line does not erase historical copies.
- Push protection: where the repository and plan support it, block a push that contains a recognized secret and require remediation or an explicitly documented exception.
GitHub’s remote MCP scanning guidance describes findings as ephemeral in supported client interactions. Treat those checks as pre-commit assistance, not as a durable repository alert or incident record. Verify which repositories, plans, clients, and secret patterns are eligible before relying on the control.
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Remediate findings correctly
Do not merely close the alert, delete the line, or rewrite the latest commit. First determine whether the value was valid, who could have read it, and where it may have been copied. Revoke or disable it, issue a replacement, deploy the replacement, then remove the old value from branches, artifacts, logs, and accessible history according to your retention policy. Preserve the finding and timeline in the incident record without preserving the secret itself.
What to do if an API key is leaked
Assume a discovered key is usable until the provider confirms revocation. Speed matters more than proving exactly how it escaped.
- Revoke or delete the exposed credential first. Do this at the provider or identity system. Do not wait for a code cleanup or a complete forensic explanation.
- Issue a replacement with tighter scope. Use a new project or environment credential where possible, set an expiration, and grant only required permissions.
- Deploy the replacement through the approved secret path. Confirm the workload is using the new version without printing it, then remove the old value from local environments, CI variables, images, and configuration.
- Review activity and spend. Check request logs, timestamps, source locations, model or resource usage, quota consumption, and billing for unexpected activity. Preserve relevant metadata and redact the secret.
- Search for additional copies. Inspect commits, branches, pull requests, agent transcripts, terminal captures, issue trackers, build artifacts, backups, and connected-tool logs.
- Contact the provider when needed. Ask about suspicious usage, billing protection, account recovery, and any provider-specific incident process.
- Secure the human account if applicable. Change the sign-in password, end unfamiliar sessions, review multifactor methods and recovery addresses, and follow the service’s account-recovery instructions if account credentials may also be exposed.
After containment, identify the control that failed: an unrestricted agent directory, a copied terminal command, an overbroad CI role, missing push protection, or an unowned credential. Change the workflow and test the replacement and revocation process while the incident is still understood.
Operate credentials as a lifecycle
Ownership and records
Maintain a non-secret record for every credential: purpose, owner, environment, permissions, creation date, expiry, last-use review, rotation procedure, and emergency contact. Review entries when a project, teammate, provider, or agent changes.
Rotation and revocation tests
Rotation is only real when the application can use the new value and the old value can be disabled without an outage. Practice the sequence in a non-production environment, measure how long replacement takes, and verify that old versions no longer authenticate. Remove unused keys rather than allowing them to age indefinitely.
Logging and monitoring
Log identity names, request metadata, policy decisions, and outcomes without logging secret values or full sensitive prompts. Alert on unusual volume, geography, permission errors, spending, or use outside an expected deployment window. Keep monitoring access at least as restricted as application access.
Team and agent policy
Define which agents may run with credentials, which MCP servers are approved, which directories are readable, and who can authorize exceptions. Train developers not to paste keys into prompts, tickets, screenshots, or public bug reports. Revoke access when a person, tool, repository, or deployment is retired.
A practical decision checklist
- Is this value a secret, and can the service issue a narrower or shorter-lived alternative?
- Does a browser, mobile app, agent, CI job, or MCP server need to see it? If yes, can the design move the call to a backend or workload?
- Is the value separated from source, logs, history, images, and generated files?
- Are development, staging, production, people, projects, and integrations using distinct identities?
- Can you name the exact permissions, owner, expiry, rotation method, and revocation command?
- Are local scanning, CI checks, repository history scanning, and eligible push protection enabled?
- Can you detect unexpected requests and spending, and have you tested replacing a credential under pressure?
If any answer is unknown, treat that gap as a security task before granting an AI tool broader access.
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