Short abstract:

My contribution examines the imaginaries generated to support advances in AI in the cybersecurity domain and the ways these epistemic claims and future visions can omit the differential vulnerabilities that contribute to insecurities in the first place.

Long abstract:

Generative artificial intelligence has been heralded as a ‘democratizing force’ in cybersecurity for how AI-based solutions can detect and respond to known and unknown threats in real-time with minimal human intervention. My contribution considers how automating repetitive tasks like data collection, extraction, and threat search and detection can also automate a normative bias regarding what constitutes risks and threats and how to mitigate them.

Three cases are examined to trace the imaginaries generated about the future to support advances in AI: IBM’s Watson for Cybersecurity, CrowdStrike’s Generative AI Security Analyst Charlotte AI, and Google’s security large-language model Sec-PaLM. I examine the imaginaries to excavate what I call “automating insecurities.” How do the future imaginaries by IBM, CrowdStrike and Google about AI contain and corral collective notions of (in)security? How do these firms stabilize AI inevitability narratives through epistemic claims and future predictions? What can an analysis of the relationship between innovation and containment in the discourses of AI inevitability in cybersecurity reveal about variants of non-technical insecurities? How do these imaginaries bolster the aspirations of these firms to dominate in the “AI arms race”?

Cybersecurity is often presented as a set of universal measures to protect computer systems, networks, and digital infrastructures. However, how cyber insecurities are understood and experienced is not universal. My contribution examines the imaginaries generated to support advances in AI in the cybersecurity domain and the ways these epistemic claims and future visions can omit the differential vulnerabilities that contribute to insecurities in the first place.