Privacy-Preserving Techniques in AI-Powered Cyber Security: Challenges and Opportunities

Abstract

As the intersection between artificial intelligence (AI) and cybersecurity grows, the significance of privacy preservation in AI-powered cyber defense mechanisms becomes paramount. This paper conducts an in-depth exploration of privacy-preserving techniques within the realm of AI-powered cybersecurity. It evaluates various methods such as homomorphic encryption, differential privacy, federated learning, and secure multiparty computation aimed at safeguarding sensitive data while leveraging AI for threat detection and mitigation. The study assesses the challenges associated with implementing these techniques, including computational overhead, data utility, and scalability issues. Furthermore, it identifies the opportunities presented by privacy-preserving AI models, emphasizing their potential to enhance trust, compliance with regulatory frameworks, and collaboration among diverse entities without compromising confidentiality. This research aims to elucidate the complexities, trade-offs, and emerging opportunities in deploying privacy-preserving techniques within AI-powered cybersecurity frameworks.