The increasing integration of Artificial Intelligence (AI) in the Cybersecurity domain has changed the way that countries defend themselves against ever-evolving and dynamic threats. This paper provides a broad survey of the use of AI in Cybersecurity, focusing on the new and emerging trends, the main issues, and the need for ethical steering (Harshith J, Gill MS, Jothimani M (2023) Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks. arXiv preprint arXiv:2308.12918. https://arxiv.org/pdf/2308.12918 ). Based on a multi-disciplinary analysis, it reviews the development of AI-enhanced threat detection, real-time threat intelligence, automated incident response, and predictive analytics, with a critical assessment of adversarial machine learning and the use of AI in cyber weapons (Rafique SH, Abdallah A, Musa NS, Murugan T (2024) Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends. Sensors 24(6):1968. https://doi.org/10.3390/s24061968 ). The research identifies the weaknesses of conventional security measures and explains how AI, especially deep learning and behavior-based models, improve detection and swift response (Schmit (2023) J Ind Inf Integr 36). In addition, the study looks at systemic issues such as data scarcity, explainability, systemic bias, and regulatory gaps. It suggests that a human-AI teaming approach is a viable way to achieve cyber resilience (Sarker IH, Janicke H, Mohammad N, Watters P, Nepal S (2023) AI potentiality and awareness: a position paper from the perspective of human-AI teaming in cybersecurity. Int Conf Intell Comput Optimiz 140–149. https://arxiv.org/pdf/2310.12162 ). The discussion is informed by current empirical evidence and policy, legal, and ethical frameworks for AI-driven cybersecurity practices, which emphasize transparency, fairness, and accountability (Ibid.; Bernardez Molina S, Nespoli P, Gómez Mármol F (2023) Tackling cyberattacks through ai-based reactive systems: a holistic review and future vision. arXiv e-prints, arXiv: 2312.06229. https://doi.org/10.48550/arXiv.2312.06229 ). Finally, this paper argues for the need for cooperation across sectors, investment in the security of AI, and the creation of adaptive and ethically aligned AI structures to protect the digital environment in a connected world with increasing cyber risks (Andrada et al. (2023) AI & Soc 38:1321–1331; Musser M, Lohn A, Dempsey JX, Spring J, Kumar RSS, Leong B, Liaghati C, Martinez C, Grant CD, Rohrer D (2023) Adversarial machine learning and cybersecurity: risks, challenges, and legal implications. arXiv preprint arXiv:2305.14553. https://arxiv.org/abs/2305.14553 ; Oseni A, Moustafa N, Janicke H, Liu P, Tari Z, Vasilakos A (2021) Security and privacy for artificial intelligence: opportunities and challenges. arXiv preprint arXiv:2102.04661. https://arxiv.org/pdf/2102.04661 ).