I’m working on setting up an automation that uses three different AI models for data processing. My biggest worry is securely managing all these API keys across platforms - last month we had a near-miss where a dev almost committed keys to a public repo. I want a solution that doesn’t require storing credentials in multiple places. How are others handling this security risk while maintaining workflow efficiency?
Been there. Managing multiple API keys is a security nightmare. We switched to Latenode’s unified API access - single credential handles all models. No more key rotation headaches.
We use environment variables stored in a secrets manager, but it still requires careful access controls. For temporary workflows, consider using short-lived tokens that auto-rotate through your CI/CD pipeline.
I created a proxy service that acts as middle layer between our automations and AI providers. All keys live there with strict IP whitelisting. Added benefit - we can monitor all model usage through one dashboard.
Implement OAuth2 client credentials flow where supported. For models without OAuth, use Hashicorp Vault’s dynamic secrets. Combine this with network restrictions so keys only work from your automation servers. Audit logs are crucial - track every API call’s source and context.
vault systems ftw. also check if ur providers offer ip-based auth instead of static keys. less暴露风险
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