AI Content Moderation with Explainable Policy Enforcement

The problem

AI classification outputs are validated by Corules before moderation actions execute. High-confidence violations are actioned immediately. Low-confidence or edge cases route to human reviewers. Creator-facing explanations reference the specific policy category without leaking internal model details. Every decision is logged with the classifier version and policy version.

Without deterministic enforcement, AI agents either block every edge case (adding manual overhead) or silently approve decisions that violate policy — with no audit trail to show auditors or regulators.

How Corules solves it

Corules sits between your AI agent and the action it wants to take. When the agent proposes a decision, Corules evaluates the full context against your compiled policy set in a single deterministic pass — no LLM, no ambiguity.

The result is a structured outcome: ESCALATE — Violation confidence 0.65 below auto_action_threshold 0.85. Routing to human moderator.

Policy example

Corules policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated deterministically at request time — no LLM, no variability.

// Content moderation policy (CEL)
context.violation_confidence < params.auto_action_threshold
  ? "ESCALATE"
  : context.violation_category in params.zero_tolerance_categories
    ? "BLOCK"
    : "ALLOW"

This expression is evaluated against the structured context your agent sends in the /v1/validate request.

Integration options

Corules integrates with the tools your teams already use. All integrations call the same REST API or MCP server — your policy logic stays in one place.

Frequently Asked Questions