# Community guardrail > Source: > Published: 2026-11-11 17:15:00+00:00 A *community guardrail* is a configurable, policy-driven safety layer that enforces shared behavioral norms across AI agents and models in a multi-tenant marketplace—preventing harmful, off-topic, or policy-violating outputs before deployment or inference. It operates at the orchestration level, independent of individual model weights. A *community guardrail* applies consistent safety logic across multiple models and tenants within a shared infrastructure—like an AI marketplace—whereas model-specific guardrails are baked into individual model artifacts (e.g., fine-tuned refusal heads or safetied checkpoints). Community guardrails decouple policy from model architecture, enabling rapid updates without retraining or redeployment. They rely on lightweight, pluggable classifiers (e.g., Granite Safety Classifier) and metadata-aware routing, making them ideal for federated, multi-stakeholder environments. Marketplace operators embed community guardrails as middleware between API gateways and model endpoints. Requests pass through a policy engine that evaluates: (1) tenant identity and scope, (2) input/output modality (text, code, JSON), (3) declared use case tags (e.g., `financial-advice`, `healthcare-chat`), and (4) real-time risk signals (e.g., PII density, sentiment polarity). IBM Cloud Pak® for Data and IBM Watsonx™ Marketplace both deploy this pattern using open-policy-agent (OPA) + Granite Safety SDK integrations. Policies are versioned, tested in shadow mode, and enforced with configurable actions: block, redact, log, or route to human review. In regulated sectors—such as Brazilian fintech or health tech—consistent, auditable, and tenant-aware safety enforcement is non-negotiable. A community guardrail ensures that all models serving a given regulated vertical (e.g., BCB-authorized credit scoring tools) adhere to identical fairness, explainability, and data minimization thresholds—even if sourced from different vendors. This satisfies principle-based oversight requirements (e.g., BCB Circular 4.123/2023 on AI governance) without requiring each model provider to implement identical safeguards independently. **A:** Yes—policy rules support tenant-scoped overrides (e.g., stricter PII masking for healthcare tenants) while maintaining baseline compliance across the marketplace. **Q:** Do they require model retraining? **A:** No—they operate post-tokenization and pre-response, requiring no changes to model weights or training pipelines. **Q:** How are violations logged and audited? **A:** All guardrail decisions are recorded with trace IDs, policy version, timestamp, and anonymized input hashes—exportable to SIEM or BCB-mandated audit logs. **Q:** Are they compatible with open-source models? **A:** Yes—community guardrails are model-agnostic and work with Llama, Mistral, Granite, and custom fine-tunes via standard REST/gRPC interfaces. Fontes Saiba mais em [https://g.cloud](https://g.cloud) *Originally published at [g.cloud](https://g.cloud/blog/en/guardrail-comunidade/) — the guardrail every AI answer passes through before reaching a human.*