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This paper surveys the risks and benefits of LLMs and GenAI across various domains, including platform integrity, healthcare, finance, cybersecurity, and privacy, highlighting both the threats and defenses enabled by these technologies. It quantifies the increasing prevalence of LLM-assisted malware, AI-generated misinformation, and deepfake attacks, while also showcasing the use of LLMs in fraud detection and regulatory compliance. The paper proposes a strategic roadmap and operational blueprint, including an LLM-DA stack with modular components and governance layers, to address AI threats and promote responsible innovation.
LLMs are a double-edged sword: while driving a surge in AI-generated scams and misinformation, they're also being deployed by platforms like JPMorgan Chase and Google Play to fight fraud and enhance security.
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, and Copilot (by OpenAI, Anthropic, Google, Meta, and Microsoft, respectively), are reshaping digital platforms and app ecosystems while introducing critical challenges in cybersecurity, privacy, and platform integrity. Our analysis reveals alarming trends: LLM-assisted malware is projected to rise from 2% (2021) to 50% (2025); AI-generated Google reviews grew nearly tenfold (1.2% in 2021 to 12.21% in 2023, expected to reach 30% by 2025); AI scam reports surged 456%; misinformation sites increased over 1500%; and deepfake attacks are projected to rise over 900% in 2025. In finance, LLM-driven threats like synthetic identity fraud and AI-generated scams are accelerating. Platforms such as JPMorgan Chase, Stripe, and Plaid deploy LLMs for fraud detection, regulation parsing, and KYC/AML automation, reducing fraud loss by up to 21% and accelerating onboarding by 40-60%. LLM-facilitated code development has driven mobile app submissions from 1.8 million (2020) to 3.0 million (2024), projected to reach 3.6 million (2025). To address AI threats, platforms like Google Play, Apple App Store, GitHub Copilot, TikTok, Facebook, and Amazon deploy LLM-based defenses, highlighting their dual nature as both threat sources and mitigation tools. In clinical diagnostics, LLMs raise concerns about accuracy, bias, and safety, necessitating strong governance. Drawing on 445 references, this paper surveys LLM/GenAI and proposes a strategic roadmap and operational blueprint integrating policy auditing (such as CCPA and GDPR compliance), fraud detection, and demonstrates an advanced LLM-DA stack with modular components, multi-LLM routing, agentic memory, and governance layers. We provide actionable insights, best practices, and real-world case studies for scalable trust and responsible innovation.
