# CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders

arXiv: https://arxiv.org/abs/2504.07457 · HTML: https://mlsanity.com/paper/2504.07457/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy](https://mlsanity.com/paper/2505.23397/) (2025)
- [COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies](https://mlsanity.com/paper/2606.30479/) (2026)
- [Structured but Fragile: On the Limits of LLMs in Cybersecurity Decision-Making](https://mlsanity.com/paper/2608.20966/) (2026)

## Abstract (arXiv)

The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks to organizations, making secure, isolated cyber ranges an essential tool for conducting Red vs. Blue Team training events. These events enable security teams to refine their skills without impacting operational environments. While such training provides a strong foundation, the ever-evolving nature of cyber threats necessitates additional support for effective defense. To address this challenge, we introduce CyberAlly, a knowledge graph-enhanced AI assistant designed to enhance the efficiency and effectiveness of Blue Teams during incident response. Integrated into our cyber range alongside an open-source SIEM platform, CyberAlly monitors alerts, tracks Blue Team actions, and suggests tailored mitigation recommendations based on insights from prior Red vs. Blue Team exercises. This demonstration highlights the feasibility and impact of CyberAlly in augmenting incident response and equipping defenders to tackle evolving threats with greater precision and confidence.