AI in Cybersecurity: The Paradox
Katina Michael, Roba Abbas, George Roussos
Abstract
Modern artificial intelligence is inherently paradoxical in many ways. While AI aims to increase automation, it also requires more intimate human involvement to reflect on the insights generated (automation paradox). While AI results in job displacement, it also creates new jobs, some simply to provide the necessary support systems for those newly unemployed (transition paradox). And as generative AI takes away control over the creative process, it also offers new creative opportunities (creativity paradox). This article considers another paradox, that relates to the fact that computational systems created using AI can be used both for public good in civilian applications and for harm across a range of application areas and settings [A1]. This contradiction is explored within an organizational and governmental context, where modern AI relies on data which might be externally or internally-sourced [A2]. External data sources [A3] are inclusive of open-source intelligence (OS-INT), such as information available on the Internet and the dark web, and internal data sources may include proprietary data found within an organizational or a wider governmental context [A4]. A further relevant consideration is the expanding role of the Internet of Things to support smart infrastructures, which has created new vulnerabilities [A5].