Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline

Abstract

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 between 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 its challenges. Thereby, we discuss the 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 highlight the importance of addressing biases both during CTI analysis and within 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 defenses, and human–AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in the cybersecurity domain.