I'm building an internal AI platform for different teams; what should I use for access control and logs?

For an internal AI platform serving multiple teams, I’d use:

Access control

Use your existing corporate identity provider (IdP) with SSO + group-based authorization.

Typical setup:

• Authentication: Okta / Azure AD / Google Workspace / Ping / Auth0
• Authorization: role-based access control (RBAC), and for finer needs, attribute-based access control (ABAC)
• Permissions model:
  • org → team → project/workspace → model/tool/dataset
  • roles like admin, operator, developer, viewer, auditor
• Best practice: manage permissions via groups in the IdP, not per-user manual assignments

If you need internal service-to-service access:

• Use short-lived service tokens or mTLS
• Prefer OIDC/JWT for user auth and workload identity for services

Logs

You want centralized, immutable audit logging with separate streams for:

1. Auth events

   • login/logout
   • token issuance/refresh/revocation
   • permission changes
   • failed access attempts

2. Platform audit events

   • who accessed which model, dataset, prompt, agent, tool, or workspace
   • config changes
   • deployment changes
   • policy changes

3. Operational logs

   • app errors
   • latency
   • rate limiting
   • request tracing

4. AI-specific logs

   • prompt/response metadata
   • model name/version
   • tool calls
   • safety filter decisions
   • retrieval sources used

Important

Be careful with sensitive content:

• Don’t log raw prompts/responses by default
• If you must, redact PII/secrets
• Store only metadata unless there’s a strong compliance/debugging need
• Add retention controls and access restrictions to logs themselves

Good implementation pattern

• SSO for login
• JWT/OIDC claims for team/project membership
• Central policy engine for authorization if complexity grows:
  • OPA (Open Policy Agent)
  • Cedar
  • Zanzibar-style service if you need highly granular permissions
• Audit log pipeline:
  • app emits structured events
  • ship to a central sink like Elasticsearch/OpenSearch, Splunk, Datadog, Loki, or a data lake
  • make logs append-only / tamper-evident where possible

My practical recommendation

If you want a solid default stack:

• Auth: Okta or Azure AD + OIDC
• Authz: RBAC first, ABAC later if needed
• Policy engine: OPA if permissions get complex
• Audit logs: structured JSON logs + centralized SIEM/log platform
• Sensitive data: redact prompts and outputs by default

If you want, I can give you:

1. a reference architecture,
2. a sample RBAC/ABAC schema, or
3. a log event schema for AI requests.