This paper investigates the practical barriers to trustworthy AI-driven Cyber Threat Intelligence (CTI) in financial institutions. Through a mixed-methods study, it identifies socio-technical challenges that hinder AI adoption and proposes security-oriented safeguards for effective deployment.

This paper examines how cyber threat intelligence (CTI) needs to adapt for artificial intelligence (AI) systems, detailing AI-specific assets, vulnerabilities, and attack patterns. It reviews existing AI security knowledge, outlines requirements for an AI-oriented threat intelligence knowledge base with concrete indicators of compromise (IoC), and discusses techniques for measuring similarity between IoCs and AI artifacts.

This paper proposes a comprehensive reliability assessment framework for deepfake detectors, moving beyond traditional performance metrics. It evaluates five state-of-the-art methods across four crucial pillars: transferability, robustness, interpretability, and computational efficiency, revealing both progress and critical limitations in current detection technologies.

This survey comprehensively analyzes the growing cybersecurity challenges in Internet of Things (IoT) networks, examining vulnerabilities and threats across different layers. It also evaluates advanced defense mechanisms leveraging AI, Blockchain, and Zero Trust Architecture to enhance IoT ecosystem resilience.