Critical infrastructure (CI), including healthcare, transportation, energy management, and water supply, becomes increasingly digitized to improve efficiency. At the same time, this digital transformation increases CI’s vulnerability to cyberthreats. On one hand, integrating CI with artificial intelligence (AI) and machine learning (ML) algorithms can enable real-time response to attacks and highly accurate threat detection, enhancing cybersecurity. However, these novel technologies introduce unprecedented risks, including attacks that target and misuse AI models. This article explores the multifaceted nature of using AI in CI cybersecurity, presenting the ways it can be used as a powerful tool and a potential threat vector at the same time. The most appropriate ML algorithms for CI use cases are examined, and the vulnerabilities of AI systems are analyzed, discussing ethical, operational, and data-related challenges. Moreover, suggested frameworks for governments, policy makers, and regulations for ethical and secure AI integration to CI cybersecurity systems are provided. This article offers a comprehensive overview for students and professionals interested in efficiently securing CI through AI technology, while also identifying future research and implementation directions.