Utilizing Prompt Engineering to Operationalize Cybersecurity

Ken Huang (), Grace Huang, Yuyan Duan and Ju Hyun ()
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Ken Huang: DistributedApps.ai
Grace Huang: PIMCO
Yuyan Duan: Silicon Valley AI+
Ju Hyun: Meta

Chapter Chapter 9 in Generative AI Security, 2024, pp 271-303 from Springer

Abstract: Abstract This chapter provides a comprehensive guide to prompt engineering techniques for cybersecurity operations. Core concepts establish a foundation for constructing specialized prompts that tap the power of GenAI for threat analysis, incident response, and security enhancement. Specific methods including few shot learning, Retrieval Augmented Generation, Chain of Thought, Tree of Thought, ReAct, and automated reasoning are elucidated to improve model capabilities on complex cybersecurity tasks. However, prudent practices are emphasized to address risks around adversarial attacks, biases, and ethical breaches. The chapter aims to equip security professionals with prompt engineering proficiencies to leverage GenAI responsibly based on principles of accountability and transparency.

Keywords: Prompt engineering; Threat detection; Vulnerability analysis; Incident response; Zero shot learning; Few shot learning; Retrieval Augmented Generation; Chain of Thought; Tree of Thought; ReAct; Automatic reasoning; Self-consistency; Adversarial attacks (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_9

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DOI: 10.1007/978-3-031-54252-7_9

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