The field of cybersecurity is moving towards the development of innovative deception techniques and advanced threat detection methods. Researchers are focusing on creating systems that can effectively identify and deter ransomware attacks, as well as detect and analyze in-memory threats. The use of dynamic binary instrumentation and kernel-assisted systems is becoming increasingly popular in the detection of fileless threats. Additionally, the development of observability frameworks for AI agents is gaining attention, as it enables the correlation of high-level intent and low-level actions. Noteworthy papers include: ranDecepter, which introduces a novel approach to identifying and deterring ransomware attacks using active cyber deception. AgentSight, which presents an AgentOps observability framework that bridges the semantic gap between high-level intent and low-level actions of AI agents. RX-INT, which features a kernel-assisted system for real-time detection and analysis of in-memory threats. Secure Development of a Hooking-Based Deception Framework, which presents a deception framework that leverages API hooking to intercept input-related API calls invoked by keyloggers.