Cyber threats continue to evolve in complexity, thereby traditional Cyber Threat Intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating Artificial Intelligence (AI) into CTI. We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities. The pipeline highlights the collaboration of AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence. We also explore the automated generation of mitigation recommendations, harnessing AI's capabilities to provide real-time, contextual, and predictive insights. However, the integration of AI into CTI is not without challenges. Thereby, we discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions. We address the need for data privacy, consent mechanisms, and the potential misuse of technology. Moreover, we highlights the importance of addressing biases both during CTI analysis and AI models warranting their transparency and interpretability. Lastly, our work points out future research directions such as the exploration of advanced AI models to augment cyber defences, and the human-AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in cybersecurity domain.

翻译：网络威胁持续演化其复杂性，传统的网络威胁情报（CTI）方法难以跟上其发展步伐。人工智能（AI）提供了潜在的解决方案，能够自动化和增强从数据摄取到韧性验证的各项任务。本文探讨了将人工智能集成到CTI中的潜力。我们提出了一种AI增强型CTI处理流水线的蓝图，并详细阐述了其组件与功能。该流水线强调了AI与人类专业知识的协作，这对于生成及时且高保真的网络威胁情报至关重要。我们还探索了缓解建议的自动生成，利用AI的能力提供实时、情境化且具有预测性的洞察。然而，将AI集成到CTI中并非没有挑战。因此，我们讨论了伦理困境、潜在偏差以及AI驱动决策中透明度的必要性。我们探讨了数据隐私、同意机制以及技术滥用的需求。此外，我们强调了在CTI分析和AI模型中处理偏差的重要性，以确保其透明度与可解释性。最后，我们的工作指出了未来研究方向，例如探索先进AI模型以增强网络防御，以及优化人机协作。总之，AI与CTI的融合在网络安全领域展现出显著潜力。