Content moderation is critical for any platform hosting user-generated content. AI-powered approach: 1) Multi-stage pipeline: Stage 1 - fast rule-based filters (blocklist matching, regex for phone numbers/emails), <1ms. Stage 2 - ML classifier for hate speech, violence, spam, NSFW (DistilBERT or custom model), <50ms. Stage 3 - LLM for ambiguous cases requiring contextual understanding, <2s. Stage 4 - human review for escalated and high-stakes decisions. 2) Multi-modal coverage: text (toxic language, threats, spam), images (NSFW, violence, hate symbols - use OpenAI moderation API or Google SafeSearch), video (frame sampling + audio transcription + visual analysis). 3) Confidence-based routing: high confidence violations -> auto-action, medium confidence -> human review queue, low confidence -> pass through with monitoring. 4) Policy management: separate model behavior from policy rules. Store policies in database, evaluate them against model outputs programmatically. Allows policy updates without model retraining. 5) Appeals process: every action should be appeallable. Track false positive rates by content type and demographic. 6) Adversarial robustness: bad actors continuously probe for bypasses. Regular adversarial testing, model updates, rule updates required. Tools: Jigsaw Perspective API for toxicity, AWS Rekognition for image moderation, OpenAI moderation API for text.