# Generative AI Models: Opportunities and Risks for Industry and Authorities

arXiv: https://arxiv.org/abs/2406.04734 · HTML: https://mlsanity.com/paper/2406.04734/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 3 of 3)

- ["We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe](https://mlsanity.com/paper/2510.00909/) (2025)
- [Beyond the Cloud: Assessing the Benefits and Drawbacks of Local LLM Deployment for Translators](https://mlsanity.com/paper/2507.23399/) (2025)
- [LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems](https://mlsanity.com/paper/2509.10682/) (2025)

## Abstract (arXiv)

Generative AI models are capable of performing a wide variety of tasks that have traditionally required creativity and human understanding. During training, they learn patterns from existing data and can subsequently generate new content such as texts, images, audio, and videos that align with these patterns. Due to their versatility and generally high-quality results, they represent, on the one hand, an opportunity for digitalisation. On the other hand, the use of generative AI models introduces novel IT security risks that must be considered as part of a comprehensive analysis of the IT security threat landscape. In response to this risk potential, companies or authorities intending to use generative AI should conduct an individual risk analysis before integrating it into their workflows. The same applies to developers and operators, as many risks associated with generative AI must be addressed during development or can only be influenced by the operating organisation. Based on this, existing security measures can be adapted, and additional measures implemented.