Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Financial Trust and Compliance, Cybersecurity, Privacy & AI Safety: A Comprehensive Survey, Roadmap & Implementation Blueprint

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Authors: Kiarash Ahi

Published: 2025-06-10 18:03:19+00:00

AI Summary

This paper surveys the risks and benefits of LLMs and generative AI, highlighting their dual nature as both threat sources and mitigation tools for platform integrity. It proposes a strategic roadmap and operational blueprint for using these technologies to automate review, detect abuse, and enhance trust across digital ecosystems and clinical diagnostics.

Abstract

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.

Key findings

The analysis reveals alarming trends in LLM-assisted malware, AI-generated reviews, scams, and misinformation. Leading platforms are deploying LLM-based defenses, showing their potential for mitigating risks. The LLM-DA stack is proposed as a solution to scale trust and safety operations.

Approach

The paper proposes an LLM-DA stack, a cross-domain infrastructure layer for safety verification and responsible deployment. This stack integrates several LLM-powered defenses, including static code analysis, multimodal storefront validation, and policy auditing, for various applications.

Datasets

UNKNOWN

Model(s)

Various LLMs are mentioned (ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion), but specific models used for the core contributions are not explicitly detailed.

Author countries

Germany, USA

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