How do I enforce prompt and output policies before requests hit model providers?

To enforce prompt and output policies before requests reach model providers, put a policy enforcement layer in front of your LLM calls. Think of it as a gateway that inspects, rewrites, blocks, or routes requests/responses before anything is sent onward.

Recommended architecture

Client → Policy Gateway → Model Provider

• Input policy checks on the prompt/messages
• Prompt transformation if needed
• Model routing only after passing policy
• Output policy checks on the provider response
• Block/redact/repair before returning to the user

What to enforce

1) Prompt-side policies

Inspect the request before forwarding it:

• PII/PHI/secrets detection
• Prompt injection / jailbreak heuristics
• Disallowed content categories
• Tenant/data-boundary validation
• Rate limits and quota checks
• Allowed model / region / tool permissions

Actions:

• Allow
• Deny
• Redact
• Transform (e.g., strip secrets, normalize, add safety system prompt)
• Route to a safer model or sandbox

2) Output-side policies

Inspect model responses before returning them:

• Toxicity / harassment
• Disallowed instructions
• Sensitive data leakage
• Policy-specific formatting constraints
• Citation requirements / hallucination checks (if applicable)

Actions:

• Block
• Redact
• Regenerate
• Fallback to a safe response template
• Escalate to human review

Implementation pattern

A. Use a policy engine

Common choices:

• OPA (Open Policy Agent)
• Cedar
• Custom rules engine
• Regex + detectors for simpler setups

Policies should be versioned and auditable.

B. Add pre-request middleware

In your API service:

1. Receive request
2. Normalize input
3. Run policy checks
4. Decide action
5. Only then call provider

Pseudo-flow:

if violates_input_policy(request):
    return blocked_response

request = sanitize(request)
response = call_model_provider(request)

if violates_output_policy(response):
    return blocked_or_redacted_response

return response

C. Keep policy decisions separate from model logic

Do not rely on the model to police itself for critical controls. Enforce in your application layer.

Example: simple middleware

def handle_llm_request(req):
    decision = policy_engine.check_input(req)

    if decision.action == "block":
        return {"error": "Request blocked by policy"}

    if decision.action == "redact":
        req = redact_sensitive_fields(req)

    provider_resp = call_provider(req)

    out_decision = policy_engine.check_output(provider_resp)

    if out_decision.action == "block":
        return {"error": "Response blocked by policy"}

    if out_decision.action == "redact":
        provider_resp = redact(provider_resp)

    return provider_resp

Best practices

• Fail closed for sensitive environments
• Log decisions with reason codes
• Version policies and test them
• Use allowlists for models, tools, and destinations
• Separate tenants and contexts
• Redact before logging
• Add a human review path for borderline cases

If you use a gateway/proxy

You can enforce policies via:

• API gateway plugins
• LLM proxy services
• Service mesh filters
• Reverse proxy middleware

This is ideal if multiple apps/services call models.

Common pitfalls

• Checking only output and ignoring prompt leaks
• Letting prompts reach the provider before scanning
• Overblocking with fragile regexes
• Not handling streaming responses incrementally
• Not validating tool/function calls separately

Practical recommendation

If you’re starting today:

1. Put a middleware/gateway in front of model APIs
2. Use a policy engine for deterministic rules
3. Add PII and secret detection
4. Add prompt injection and content moderation checks
5. Validate output before returning it
6. Log every policy decision for audit

If you want, I can sketch a reference design for:

• Python/FastAPI
• Node/Express
• OpenAI-compatible proxy
• OPA/Cedar-based policy enforcement