Privacy-preserving AI for cybersecurity: Balancing threat intelligence collection with user data protection

Abstract

This paper explores the case of using privacy-preserving artificial intelligence in cybersecurity by analyzing the importance of effective threat intelligence in the conflict with potential invasions and high user data protection standards. With the increased articulation of cyber threats, AI is crucial in fortifying detection, reaction, and prevention measures for cyber threats in CSFs. However, such large-scale information feeding these systems raises many privacy issues, and hence, strong privacy preservation mechanisms that ensure user anonymity and protect the information from misuse are needed. This study reveals how AI threat detection accuracy can be preserved while protecting users' privacy through data obfuscation, differential privacy, and federated learning. Furthermore, the article highlights the need to apply privacy-enhancing patterns, including Privacy by Design, as new patterns in cybersecurity lifecycles. The recommendations derived here are intended to help researchers and practitioners achieve equal data protection results and threat intelligence efficiency when employing AI models. This approach promotes a secure and highly sensitive terrain for disseminating AI-assisted cybersecurity innovations.