Developing frameworks for secure, privacy-preserving data collection and sharing for AI-powered cyber threat intelligence

ABSTRACT : The increasing intricacy of cyber threats underscores the criticality of acquiring resilient cyber threat intelligence (CTI). CTI could be transformed by artificial intelligence (AI) by automating detection and response. Nonetheless, this requires privacy-preserving and secure mechanisms for data collection and sharing. To tackle these challenges, this paper presents innovative frameworks that merge state-of-the-art cryptographic methods with artificial intelligence algorithms to guarantee the confidentiality and integrity of data. By analyzing the present state of cyber threats and the function of AI in CTI, we bring attention to the privacy implications arising from data exchange between various entities. The frameworks we have put forth employ privacy-enhancing technologies, including secure multi-party computation and homomorphic encryption, to enable the secure exchange of threat intelligence. A set of metrics for assessing the efficacy and dependability of these frameworks in real-world scenarios is also presented. The findings indicate a potentially advantageous pathway for organisations to collaborate in order to enhance their security protocols and safeguard sensitive information against unauthorised intrusions and disturbances. This study highlights the convergence of artificial intelligence and privacy-preserving approaches, representing a transformative moment in cyber threat prevention and intelligence exchange.

KEYWORDS: Developing frameworks, Secure, Privacy-preserving, Data collection, Sharing, AI-powered, Cyber threat intelligence