How do I set up an inference gateway for agents that need RBAC, network egress controls, and audit logs?

A good pattern is to put an inference gateway in front of your model providers and make it the only path agents can use for LLM calls. The gateway becomes the enforcement point for:

• RBAC: who/what can call which models or tools
• Network egress controls: where requests are allowed to go
• Audit logs: every prompt, response, tool call, and policy decision

Recommended architecture

Agents → Inference Gateway → Approved model providers / internal models

Add supporting services around the gateway:

• Identity provider: OIDC/SAML, service accounts, workload identity
• Policy engine: OPA / Cedar / custom policy service
• Secrets manager: Vault, cloud secret manager
• Logging pipeline: SIEM / log warehouse
• Network controls: egress proxy, firewall, private endpoints, DNS allowlists

What the gateway should enforce

1) RBAC

Use identity-based access for each agent or workload.

Typical controls:

• agent.readonly can use only approved smaller models
• agent.pii can use redaction-safe models only
• agent.admin can access premium models or higher token limits
• Separate permissions for:
  • model selection
  • max context length
  • tool/function calling
  • file uploads
  • streaming responses
  • batch jobs

Implementation tips:

• Authenticate via mTLS + workload identity or JWT
• Map service identity to roles in a policy engine
• Enforce per-role limits:
  • model allowlist
  • prompt size
  • response size
  • rate limit
  • time of day / environment restrictions

2) Network egress controls

Do not let agents reach the internet directly.

Instead:

• Force all outbound traffic through a proxy or NAT gateway
• Allow only:
  • approved model endpoints
  • approved internal APIs
  • required package repositories if needed
• Block:
  • arbitrary web access
  • unknown DNS resolution
  • direct TCP egress
• Prefer private connectivity:
  • PrivateLink / Private Service Connect / VNet peering / VPC endpoints
  • internal model endpoints for on-prem or private cloud

Useful additions:

• DNS allowlisting
• IP/domain allowlists
• TLS inspection only if appropriate and lawful
• Per-agent network segments for stronger isolation

3) Audit logs

Capture complete, structured traces for every request.

Log at minimum:

• timestamp
• caller identity
• role/policy decision
• model requested and model used
• token counts
• request metadata
• tool calls
• upstream endpoint
• latency
• success/failure
• moderation/redaction actions
• correlation/request ID

Be careful with sensitive data:

• Redact secrets, credentials, PII, and regulated content
• Store raw prompts only if policy allows
• Encrypt logs at rest and in transit
• Set retention by data class

A practical request flow

1. Agent authenticates to gateway with service identity.
2. Gateway checks policy:
   • Is this identity allowed to use this model?
   • Is the destination endpoint approved?
   • Is the request size within limits?
3. Gateway applies transformations:
   • redact secrets/PII if required
   • add trace IDs
   • attach metadata
4. Gateway forwards the request only to approved model endpoints.
5. Gateway logs the request/response and policy decision.
6. Gateway returns response to the agent.

Policy examples

You can express controls like:

• marketing-bot may use gpt-4.1-mini but not gpt-4.1
• finance-agent may only call internal hosted models
• prod agents may not call external endpoints directly
• dev agents may only operate in sandbox environments
• requests containing secrets are blocked or redacted

Key security features to include

• mTLS between agents and gateway
• JWT/OIDC for identity propagation
• OPA/Cedar for policy evaluation
• Egress proxy + firewall for network enforcement
• Central audit logging
• Prompt/response redaction
• Rate limiting and quotas
• Model allowlists
• Tenant isolation
• Secrets detection
• Replay protection and request signing

Suggested deployment options

You can implement the gateway as:

• an API gateway with custom plugins
• a dedicated inference proxy service
• a service mesh egress gateway
• a sidecar plus centralized policy service

For stronger control, combine:

• service mesh for identity and egress
• policy engine for RBAC
• gateway for model routing and logging

Operational checklist

• All agent traffic to LLMs goes through the gateway
• No direct internet access from agent workloads
• Model endpoints are allowlisted
• Policies are versioned and tested
• Audit logs are centralized and immutable
• Sensitive content is redacted or blocked
• Access is tied to workload identity, not shared keys
• Alerts exist for policy violations and unusual usage

Example minimal policy matrix

Role	Allowed models	Egress	Logging
dev-agent	sandbox/internal only	no public internet	full audit
prod-agent	approved external + internal	allowlisted endpoints only	full audit + retention
finance-agent	internal only	internal networks only	full audit, restricted raw prompts

If you want, I can also give you:

1. a reference architecture diagram,
2. a sample OPA policy, or
3. a Kubernetes-based implementation blueprint.