AI Moderation
Moderate comments and user-generated content with Workers AI and first-party EmDash plugin hooks.
Plugin
AI Moderation
Moderate comments and user-generated content with Workers AI and first-party EmDash plugin hooks.
Product Details
- Category
- AI
- Status
- available
- Version
- Included in beta
- Price
- Included in beta
- License
- MIT
- Release Date
- TBD
- Compatibility
- EmDash CMS beta preview
- Maintainer
- EmDash CMS Team
Links
AI Moderation demonstrates EmDash using Cloudflare-native infrastructure for a practical editorial problem: keeping comment sections and user-generated text safe without outsourcing every decision to a black-box vendor. The upstream package is described as leveraging Workers AI and Llama Guard–style guardrails; verify the exact model and policy knobs in packages/plugins/ai-moderation for your release.
Installation
- Confirm your EmDash deployment runs on a stack where Workers AI (or the documented AI backend for this plugin) is available and permitted by your account plan.
- Enable the AI Moderation plugin from the EmDash admin or wire it in from the monorepo during development.
- Set required secrets and environment variables as described in the plugin README—typically API tokens or bindings that must not be committed to Git.
- Smoke-test in a non-production environment: submit benign and edge-case text to confirm moderation decisions and logging behave as expected.
Configuration
You will usually tune:
- Sensitivity or policy presets — what counts as block versus flag versus allow (exact options depend on the shipped integration).
- Fallback behavior — when the AI service is unavailable, fail closed (hold for review) or fail open (post with a warning), according to your risk appetite.
- Audit visibility — whether moderators see scores, categories, or only final decisions.
Example (conceptual): a community blog might auto-publish comments that score below a risk threshold, queue borderline submissions for manual review, and block high-confidence policy violations. Map those thresholds using the controls exposed in admin or config files for your version.
Usage scenarios
- Publications with comments — reduce moderation load while keeping humans in the loop for ambiguous cases.
- User-generated submissions — short text attached to forms or pitches where automated triage speeds editorial review.
- Internal wikis or docs with suggestions — lightweight screening before content goes live.
Operational tips
- Review false positives regularly; tune thresholds after you have real traffic, not only synthetic tests.
- Combine with Audit Log if compliance requires traceable decisions on moderated content.
- Monitor Workers AI usage and latency so moderation stays within budget and UX expectations.