Darwinium Abuse Prevention is a platform that detects and prevents online abuse by analyzing behavioral signals across the full digital customer journey. It monitors interactions across web, mobile, and API endpoints in real time, using device, behavioral, network, identity, and journey-level data to identify abusive patterns. Key use cases addressed include: - Returns abuse and wardrobing: identifying users who repeatedly exploit return policies - Account sharing and collusion: detecting unusual access patterns and linked identities across sessions - Promo and loyalty abuse: identifying multi-account creation by bots or click farms to claim offers - Content and review spam: detecting duplicate or rapid-fire fake reviews and spam listings - Scraping, automation, and API abuse: monitoring velocity, sequence, and parameter anomalies to detect and prevent API misuse - Business logic abuse: identifying behavior that games system rules for financial gain The platform deploys at the edge to profile every user interaction and builds behavioral identity graphs that persistently link devices, sessions, and accounts across identities — even when users clear cookies, switch browsers, or change IPs. It supports configurable rules, machine learning models, and decision strategies, allowing organizations to define abuse thresholds based on their specific risk tolerance. Real-time response actions include blocking requests, redirecting traffic, displaying dynamic content, and applying labels for follow-up. An incident management capability is also included.