The field of cybersecurity and digital forensics is rapidly evolving, with a growing focus on proactive approaches to threat detection and mitigation. Recent research has highlighted the importance of analyzing privacy threats in IoT systems, with a particular emphasis on understanding the intentions and actions of threat actors. Additionally, there is a increasing need for adaptive and low-latency ransomware detection methods, as well as comprehensive evaluations of system penetration testing practices. Noteworthy papers in this area include: PTMF: A Privacy Threat Modeling Framework for IoT, which proposes a novel framework for analyzing privacy threats in IoT systems. Towards Low-Latency and Adaptive Ransomware Detection Using Contrastive Learning, which introduces a framework that integrates self-supervised contrastive learning with neural architecture search to address the challenges of ransomware detection. APThreatHunter: An automated planning-based threat hunting framework, which presents an automated threat hunting solution that generates hypotheses with minimal human intervention.