Generative artificial intelligence is reshaping cybersecurity operations faster than most organizations can adapt their frameworks, governance structures, and operational processes. This paper presents a structured analytical account of how cybersecurity organizations are adapting to generative AI, distinguishing adaptation from mere tool adoption and identifying four progression stages: exploratory deployment, functional integration, operational embedding, and strategic alignment. The resulting model is designated the Cybersecurity Generative AI Adaptation Framework (CGAF). Drawing on 25 sources spanning empirical studies, surveys, technical reviews, preprints, and framework-oriented literature published between 2023 and early 2025, the reviewed evidence suggests that organizational adaptation remains uneven, with many documented cases reflecting exploratory or function-level integration rather than institutionally embedded generative AI capability. Financial sector organizations and central banks provide the clearest examples of strategic alignment in the reviewed literature, driven by regulatory pressure and elevated risk profiles. The offensive-defensive capability asymmetry compounds these constraints, accelerating threat sophistication faster than defensive adaptation can respond. The paper concludes with implications for security operations centers, cyber threat intelligence functions, and risk management practice.
Christopher Nott (Sat,) studied this question.