The field of cybersecurity and anomaly detection is rapidly evolving, with a growing focus on leveraging artificial intelligence and machine learning to improve threat detection and response. Recent research has emphasized the importance of developing more robust and explainable models, particularly in high-stakes environments such as healthcare and finance. Hybrid neuro-symbolic models are gaining traction, as they combine the strengths of both neural networks and symbolic reasoning to provide more transparent and accountable AI systems. Another key area of research is the development of more effective anomaly detection methods, including those that utilize contrastive learning, active learning, and transfer learning to improve detection rates and reduce false positives. Noteworthy papers in this area include: The paper on Hybrid Neuro-Symbolic Models for Ethical AI in Risk-Sensitive Domains, which surveys hybrid architectures and deployment patterns for balancing accuracy with accountability. The paper on APT-CGLP, which presents a novel cross-modal APT hunting system via Contrastive Graph-Language Pre-training, facilitating end-to-end semantic matching between provenance graphs and CTI reports without human intervention.