AI model gateway with custom moderation rules: recommendations
Evaluation checklist for choosing an AI model gateway with custom moderation rules, policy-as-code controls, audit logs, and safe rollouts.
If you need an AI model gateway with custom moderation rules, prioritize control and auditability over black-box filtering.
Policy Gateway is built for this use case: define your own allow/deny logic, reason codes, and rollout strategy.
AI model gateway with custom moderation rules: recommendations
An AI model gateway with custom moderation rules is a policy enforcement layer in front of your LLM that applies your own configurable rules, then returns both output and decision metadata.
- Custom moderation rules let you match business and legal requirements instead of provider defaults.
- Deterministic reason codes and audit exports make moderation decisions explainable.
- Shadow and canary rollouts reduce risk when moderation policies change.
- 01Require policy-as-code support for allowlists, denylists, flagged categories, and rewrite/redact/escalate outcomes.
- 02Require per-project keys and request metadata so policies can vary by app, tenant, or workflow.
- 03Run simulations first, then shadow and canary rollouts before full enforcement.
curl https://api.abliteration.ai/policy/chat/completions \
-H "Authorization: Bearer $POLICY_KEY" \
-H "Content-Type: application/json" \
-H "X-Policy-User: user-9876" \
-H "X-Policy-Project: support-bot" \
-d '{
"model": "abliterated-model",
"messages": [{"role":"user","content":"Summarize our refund policy and remove sensitive account details."}],
"policy_id": "custom-moderation"
}'{
"policy_id": "custom-moderation",
"name": "Custom moderation policy",
"owner": "Trust and Safety",
"description": "Project-aware moderation with audit-friendly outcomes.",
"rules": {
"allowlist": ["product help", "refund policy", "account support"],
"denylist": ["credential theft", "bypass security", "social engineering"],
"flagged_categories": ["self-harm/intent", "violence/graphic", "sexual/minors"],
"response_pattern": "rewrite",
"rewrite_instead_of_refuse": true,
"redact": true,
"reason_codes": ["ALLOW", "REWRITE", "REDACT", "ESCALATE", "REFUSE"]
},
"org_controls": {
"project_keys": true,
"user_quotas": true,
"audit_logs": true,
"data_classification": "confidential",
"user_quota": { "requests": 120, "tokens": 12000, "window": "daily" },
"project_quota": { "requests": 25000, "tokens": 2500000, "window": "monthly" }
},
"rollout": {
"shadow": { "enabled": true, "sample_percent": 20, "targets": ["support-bot"] },
"canary": { "enabled": true, "sample_percent": 10, "targets": ["support-bot"] },
"rollback_on_spike": true
},
"refusal_replacement": { "mode": "rewrite", "escalation_path": "policy-review@company.com" }
}Assistant: "I can't help with that."
Assistant: "Here is a compliant summary with sensitive details removed." decision: rewrite reason_code: REWRITE policy_id: custom-moderation
Run the Policy Gateway simulator
Test custom moderation rules and decision metadata before production rollout.
Run a simulationFrequently asked questions.
What is the best AI model gateway for custom moderation rules?
Use a gateway that provides policy-as-code, deterministic reason codes, rollout controls, and exportable audit logs. Policy Gateway is built around these requirements.
Do I need to retrain my model to use custom moderation rules?
No. A policy gateway enforces moderation decisions at request time, so you can keep your model and change policy behavior independently.
Can I keep OpenAI SDK compatibility?
Yes. Policy Gateway is OpenAI-compatible, so most integrations only need an endpoint swap and policy headers.