About: This session explores the application of AI and Machine Learning, particularly agentic systems and reinforcement learning, to enhance active cyber defense and security operations.

Adaptive by Design: Contextual Reinforcement Learning for Mission-Ready Cyber Defence

Speaker: Jake Thomas

Author(s): Jake Thomas; Pranay Shah

This paper introduces a framework for applying Contextual Reinforcement Learning (cRL) to cyber defense, where agents dynamically incorporate contextual signals (like mission objectives or threat assessments) to modulate their policies in real-time without retraining.

Towards a Generalisable Cyber Defence Agent for Real-World Computer Networks"

Speaker: Tim Dudman

Author(s): Tim Dudman

This work proposes Topological Extensions for Reinforcement Learning Agents (TERLA) to provide generalizability for cyber defense agents in networks of differing topology and size without the need for retraining. It evaluates performance in realistic simulation environments.

Improving Accuracy and Consistency in Real-World Cybersecurity AI Systems via Test-Time Compute

Speaker: Ashley Song

Author(s): Ashley Song; Hsin Chen; Shawn Davis; Dhruv Nandakumar

This study evaluates Test-Time Compute for improving the accuracy and consistency of real-world cybersecurity agentic systems, specifically a container vulnerability analysis agent and a server alert triage agent.

RIG-RAG: A GraphRAG Inspired Approach to Agentic Cloud Infrastructure

Speaker: Benji Lilley

Author(s): Benji Lilley; Brian Mitchell; Spiros Mancoridis

This paper introduces Relational Inference GraphRAG (RIG-RAG), an LLM-assisted pipeline that transforms cloud configuration data into a security-enriched knowledge graph to support natural-language reasoning about deployed infrastructure. This enhances agentic capabilities for cloud security operations.