Generative AI Cybersecurity and Resilience

Generative Artificial Intelligence marks a critical inflection point in the evolution of machine learning systems, enabling the autonomous synthesis of content across text, image, audio, and biomedical domains. While these capabilities are advancing at pace, their deployment raises profound ethical, security, and privacy concerns that remain inadequately addressed by existing governance mechanisms. This study undertakes a systematic inquiry into these challenges, combining a PRISMA-guided literature review with thematic and quantitative analyses to interrogate the socio-technical implications of generative Artificial Intelligence. The article develops an integrated theoretical framework, grounded in established models of technology adoption, cybersecurity resilience, and normative governance. Structured across five lifecycle stages (design, implementation, monitoring, compliance, and feedback) the framework offers a practical schema for evaluating and guiding responsible AI deployment. The analysis reveals a disconnection between the fast adoption of generative systems and the maturity of institutional safeguards, resulting with new risks from the shadow Artificial Intelligence, and underscoring the need for adaptive, sector-specific governance. This study offers a coherent pathway towards ethically aligned and secure application of Artificial Intelligence in national critical infrastructure.