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Generative AI Models: Opportunities and Risks for Industry and Authorities

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arxiv 2406.04734 v2 pith:QTGUKQVF submitted 2024-06-07 cs.AI cs.CLcs.CR

classification cs.AIcs.CLcs.CR
keywords generativemodelsriskssecurityanalysisauthoritiesduringexisting
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Secure human oversight of AI: Threat modeling in a socio-technical context

    cs.CR 2025-09 conditional novelty 6.0 of 10

    Human oversight of AI is itself an attackable component; the paper catalogs how cyberattacks can undermine the people, channels, and system, plus hardening strategies.

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