Secrets Management for AI Deployments

AI deployments accumulate secrets quickly: HuggingFace tokens, hosted-API keys (Claude, OpenAI), inter-service auth tokens, encryption keys for log storage. .env works for solo dev; production needs proper secrets management.

What needs managing

• HuggingFace tokens: for gated model downloads (Llama, etc.)
• Hosted-API keys: Claude / OpenAI / Together / Fireworks for fallback routing
• Inter-service tokens: app → vLLM API key, app → vector store credentials
• Per-tenant API keys: customer-issued tokens for your AI API
• Database credentials: Postgres / Qdrant connection strings
• Encryption keys: at-rest encryption for logs / models
• Webhook signing secrets: for AI-event callbacks

Tools

• HashiCorp Vault: self-hosted; battle-tested; learning curve
• AWS Secrets Manager / Azure Key Vault / GCP Secret Manager: cloud-native
• Doppler: developer-friendly UX; good for SaaS
• 1Password Secrets Automation: simple; pairs with team 1Password
• Kubernetes External Secrets Operator: bridge between any of the above and k8s

Patterns

• Inject at runtime: vault read on container start; never commit secrets to images
• Per-environment: separate secrets for dev / staging / prod
• Rotation: HF tokens every 90 days, hosted-API keys every 30, signing secrets every 30
• Audit access: log every secret read; alert on unexpected access patterns
• Break-glass: documented emergency rotation runbook

Verdict

Secrets management for AI is standard secrets management with a longer list of items. Adopt a tool early; rotate regularly; never commit secrets. The cost of getting this wrong (HF token in repo → rate-limit ban → production outage) is high relative to the cost of doing it right.