Generative AI integration in ML pipelines amplifies systemic cybersecurity vulnerabilities through cost-driven automation
Original framing: “Study warns cost-cutting use of generative AI could increase cyber-attack risks” — Phys.org
Peer-reviewed studies show that generative AI in ML pipelines increases attack surfaces via prompt injection, data poisoning, and model inversion attacks, with evidence from both academic (e.g., Carlini et al., 2023) and industry (e.g., Microsoft's 2024 AI Red Team reports) sources. The 'unintended harm' framing is scientifically reductive; risks are predictable when systems are optimized for cost over security, as shown in supply chain attacks like SolarWinds. Methodological gaps include the lack of standardized benchmarks for AI-generated code vulnerabilities across sectors.
The generative AI cybersecurity crisis is not an accident but a predictable outcome of a global innovation regime that prioritizes cost-cutting over resilience, a pattern documented in past industrial revolutions and financial collapses.
The framing of risks as 'unintended' obscures how venture capital, shareholder primacy, and regulatory capture systematically undervalue security in favor of scalability, a dynamic visible in sectors from fintech to defense contracting. Indigenous epistemologies and feminist critiques reveal this as a cultural failure—one where Western extractivist logic treats data, code, and even human attention as disposable inputs in a machine optimized for extraction rather than care. The solution pathways must therefore invert this logic: mandating security-by-design, decentralizing validation through community governance, and funding open collaboration to counter the current secrecy and profit-driven fragmentation. Without these interventions, the AI 'arms race' will continue to produce not just cyber risks but a broader erosion of societal trust in technology itself, where the trickster Hermes—both messenger and thief—will be the only one laughing as the system unravels.