275 million fake reviews blocked on one platform alone in 2024. That number should change how every business thinks about digital trust. Here's the problem most companies don't see coming: The tools built to catch AI-generated fraud are themselves built on the same shallow technology creating the fraud. Basic LLM detectors — essentially prompt-based classifiers — were shown to be manipulated over 90% of the time in controlled tests. That's not a security layer. That's a screen door on a submarine. Our latest whitepaper breaks down what actually works: → Stylometric analysis that separates writing style from topic, catching machine patterns humans can't see → Graph neural networks that map relationships between accounts, devices, and behaviors to expose coordinated fraud rings hiding behind "verified" profiles → Pixel-level image forensics that detect synthetic photos by analyzing compression signatures and sensor noise real cameras leave behind Why this matters right now: The FTC's 2024 rule on fake reviews introduced penalties of over $50K per violation. Platforms like Tripadvisor pulled 2.7 million fraudulent reviews last year. Scammers are building entire fake hotel listings with AI-generated photos that fool both algorithms and travelers. The old playbook of "monitor and moderate" is dead. Enterprises need authentication infrastructure — systems that verify not just what was written, but how it was generated and who is behind it. We mapped the full technical framework across text, network behavior, and visual forensics in one resource. Save this if your team is navigating AI trust, content verification, or platform compliance. What's the first place you'd deploy deeper fraud detection — customer reviews, internal AI outputs, or something else entirely? #SyntheticFraudDetection #EnterpriseAI #DigitalTrustFramework #AIauthentication #CognitiveIntegrity