GenAI Data Security

Ken Huang (), Jerry Huang () and Daniele Catteddu ()
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Ken Huang: DistributedApps.ai
Jerry Huang: Metabase
Daniele Catteddu: Cloud Security Alliance

Chapter Chapter 5 in Generative AI Security, 2024, pp 133-162 from Springer

Abstract: Abstract This chapter provides an in-depth exploration of data security within the realm of GenAI. Highlighting the pivotal role of data, often likened to the “oil” of the digital age, the chapter navigates data’s lifecycle from collection to disposal. The narrative underscores the importance of secure collection, preprocessing, storage, and transmission. The chapter delves into data provenance, stressing the need to understand, verify, and validate data’s journey. Training data management is highlighted, with a focus on how training data can impact model performance, data diversity, and responsible disposal. Throughout, the chapter accentuates the significance of trust, transparency, and responsibility, offering insights into best practices in GenAI data security.

Keywords: GenAI; Data security; GPT models; Diffusion models; Data provenance; Data lifecycle; Training data management; Privacy by Design; Responsible data disposal (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-031-54252-7_5

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DOI: 10.1007/978-3-031-54252-7_5

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