  Developments in artificial intelligence (AI) are likely to affect social
engineering and change cyber defense operations. The broad and sweeping nature
of AI impact means that many aspects of social engineering could be automated,
potentially giving adversaries an advantage. In this review, we assess the ways
phishing and spear-phishing might be affected by machine learning techniques.
By performing a systematic review of demonstrated ML-enabled phishing
campaigns, we take a broad survey the space for current developments. We
develop a detailed approach for evaluation by creating a risk framework for
analyzing and contextualizing these developments. The object of this review is
to answer the research questions: (1) Are there high-risk ML-enabled phishing
use cases? (2) Is there a meaningful difference between traditional targeted
phishing campaigns and ML-enabled phishing campaigns? Practitioners may use
this review to inform standards, future research directions, and cyber defense
strategies.
