Generative AI Models: Opportunities and Risks for Industry and Authorities

2024-06-07

arXiv.org

AI Summary

Generative AI, while accelerating digital transformation in government and enterprise sectors, introduces novel security risks—including data leakage, prompt injection, and model misuse—necessitating systematic governance. Method: This study proposes the first comprehensive, lifecycle-oriented risk analysis framework specifically designed for generative AI. It delineates clear responsibility boundaries among users, developers, and operators, enabling proactive security control during development and deployment phases. The framework integrates threat modeling, compliance assessment (aligned with ISO/IEC 27001 and related standards), and scenario-specific risk identification to deliver an actionable risk evaluation methodology. Contribution/Results: The framework empowers organizations to formulate differentiated governance strategies, significantly enhancing the security, controllability, and regulatory compliance of generative AI applications.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Generative AI Models
• Cybersecurity Risks
• Risk Management

Innovation

• Generative AI
• Cybersecurity Adjustments
• Risk Analysis