AI Moderation & Misinformation Detection covers the tools, workflows, and governance practices used to identify misleading AI‑generated content and support community decision‑making on social platforms. The topic spans two complementary categories: AI Content Detectors (automated models and provenance systems that flag synthetic text, images, audio, and video) and Community Moderation Tools (human‑in‑the‑loop workflows, case management, and platform design that prioritize context and appeal). This area is timely as of 2026 because synthetic media has become more realistic and ubiquitous, regulators and platforms expect demonstrable mitigation measures, and users demand transparent, accountable moderation. Practical solutions increasingly combine multi‑modal detection, provenance signals, and human review. Key capabilities include real‑time flagging, evidence search, trainable agent handoffs, and operational tooling for triage and incident tracking. Representative tools and roles: Fond Peace illustrates platform design choices that reduce algorithmic amplification and emphasize community‑led moderation; Tribotic demonstrates how trainable AI agents with real‑time in‑app training and seamless handoffs can support moderation teams and customer‑support workflows; Xinsight-style extensions show how contextual aids can help users and moderators draft responses or context notes; Asana and Linear represent the project and incident‑management systems used to track cases, SLAs, and policy workflows; AI Knowledge Search (Amurex) exemplifies evidence‑gathering and RAG grounding to surface corroborating documents and provenance when verifying claims. Effective strategies favor hybrid approaches: automated detectors to prioritize and surface risk, integrated search to assemble evidence, and clear human workflows to apply policy and appeals. Attention to privacy, auditability, and user transparency remains central to deploying these tools responsibly.