Abstract— Large Language Models (LLMs) and generative AI (GenAI) systems—such as ChatGPT, Claude, Gemini, LLaMA, Copilot, and Stable Diffusion, developed by OpenAI, Anthropic, Google, Meta, Microsoft, and Stability AI, respectively—are profoundly transforming digital platforms, marketplaces, and app ecosystems, while introducing significant challenges for cybersecurity and user privacy and opening new frontiers in high-stakes domains like healthcare diagnostics. This rapid acceleration has driven mobile app submissions from 1.8 million in 2020 to 3.0 million in 2024, with a projected 3.6 million by 2025. However, while empowering innovation, this technological shift presents a critical double-edged sword: concurrently introducing novel and rapidly escalating risks to platform integrity, financial trust and compliance, cybersecurity, user privacy, and opening new frontiers in high-stakes domains like healthcare diagnostics.

Our comprehensive analysis reveals alarming trends across diverse abuse vectors, including a projected surge in LLM-assisted malware from 2% in 2021 to 50% by 2025. We document a nearly tenfold rise in AI-generated Google reviews to 12.21% in 2023, projected to reach 30% by 2025. Additionally, we observe a 456% increase in AI-enabled scam reports and over a 1500% rise in AI-generated misinformation sites over the past year, alongside a projected 900% surge in deepfake fraud by 2025 compared to 2023 levels. In the financial sector, LLM-powered threats like synthetic identity fraud and sophisticated AI-generated scams are rapidly evolving, necessitating advanced defenses. Despite platforms’ proactive use of AI to block millions of policy-violating apps and content, the scale and velocity of these threats underscore an urgent and unmet need for scalable integrity infrastructure to safeguard digital security and data privacy. Leading platforms such as Google Play, Apple App Store, Hugging Face Spaces, GitHub Copilot, OpenAI Plugin Stores, TikTok, Facebook, Amazon, Etsy, and Shopify now face unprecedented challenges in maintaining integrity at scale. Similarly, the integration of LLMs into clinical diagnostics presents unique challenges related to diagnostic accuracy, bias, and patient safety, necessitating robust governance.

Drawing on a review of over 400 academic papers, industry reports, and technical documents, this paper presents a comprehensive survey and data-driven analysis of the risks LLMs and GenAI pose to platform integrity and financial trust and compliance, and medical AI safety. Critically, we propose a strategic roadmap framework for using these same technologies to automate review and moderation through semantic code analysis, multimodal storefront validation, and intelligent policy auditing; detect abuse and fraud; enforce compliance across global jurisdictions (e.g., GDPR, CCPA , FinCEN, SEC, MiFID II); and enhance trust, user experience, and safety across digital ecosystems, financial systems, and clinical applications. Unlike prior work focused on isolated technical components or policy domains, our approach outlines a cross-functional architecture that integrates product, engineering, trust & safety, legal, and policy teams to operationalize AI-driven defenses. We ground our analysis in case studies of major platforms—including Google, Apple, Amazon, Meta, and Hugging Face—highlighting deployed LLM-powered systems, practical implementation insights, and lessons learned. Specifically, we examine how leading financial services platforms (e.g., JPMorgan Chase, Capital One, Stripe, Plaid, Revolut) are leveraging LLMs for synthetic identity detection, KYC/AML automation, regulatory parsing, and real-time financial scam detection, including the reported impact of reducing fraud loss rates by up to 21% and accelerating onboarding by 40–60%. Finally, we extend our proposed integrity framework to the domain of clinical diagnostics, introducing a novel multimodal AI system that interprets natural-language patient symptom descriptions using LLMs, aligns them with image-derived biomarkers, and delivers explainable treatment recommendations with physician oversight. We identify actionable best practices and emerging opportunities in explainable AI, federated review pipelines, and multi-agent compliance parsing.

We conclude that LLMs—when deployed with transparent governance and robust evaluation—can serve as a force multiplier for scalable integrity enforcement. To operationalize this vision, we propose Virelya: an envisioned framework and implementation blueprint for high-stakes domains like platform integrity, financial trust, and healthcare diagnostics. Drawing from successful paradigms in Electronic Design Automation (EDA), cybersecurity, and software quality assurance, Virelya is built upon an LLM Design & Assurance (LLM-DA) stack—an independent, cross-domain infrastructure layer for safety verification, compliance-as-code, and responsible deployment. It provides the integrated orchestration, trust, and governance capabilities needed to address the full spectrum of post-deployment challenges, offering features like advanced multi-LLM routing, agentic memory and planning, RAG evaluation, and audit/compliance tracking. This framework provides the operational blueprint for building trustworthy, compliant platforms and clinical systems in the generative AI era.

Keywords— Large Language Models (LLMs), Generative AI, Cybersecurity, Platform Integrity, Review Automation, Content Moderation, Abuse Detection, Fraud Prevention, Regulatory Compliance, Trust and Safety, Digital Marketplaces, Privacy, App Ecosystems, Federated Review Systems, Explainable AI, AI Governance, Synthetic Content, Developer Experience.