The Future of Pen Testing: AI Coaches and Virtual Labs
▼ Summary
– Researchers propose a cybersecurity training framework using Cyber Digital Twins (CDTs) and generative AI to create realistic, interactive environments for education.
– The framework integrates the Red Team Knife (RTK) toolkit to guide learners through the Cyber Kill Chain model, prompting reflection and deeper understanding of attack phases.
– Large Language Models (LLMs) provide natural-language explanations, summarize attack patterns, and offer real-time tactical suggestions, acting as adaptive mentors.
– The training structure includes horizontal simulation categories (applications, networks, etc.) and vertical Cyber Kill Chain stages, covering diverse assets and attack strategies.
– While offering benefits like risk-free testing and improved communication, the system requires strict isolation and controls to prevent misuse of AI-assisted tools.
The landscape of cybersecurity training is rapidly evolving to meet the escalating complexity of digital threats. Traditional methods often fall short in preparing professionals for the unpredictable nature of real-world attacks. A groundbreaking approach now merges digital twin technology with generative AI to create dynamic, responsive learning environments that mirror actual cyber operations.
A team from the University of Bari Aldo Moro has introduced a framework built around Cyber Digital Twins (CDTs) and large language models. This system replicates intricate IT, operational technology, and Internet of Things setups within a secure virtual space. It overlays these simulations with intelligent, real-time feedback driven by artificial intelligence, aiming to deepen comprehension of penetration testing and the complete lifecycle of cyber intrusions.
Central to this innovative setup is the Red Team Knife (RTK), a specialized toolkit that incorporates widely-used security tools such as Nmap, theHarvester, and sqlmap. What distinguishes RTK is its guided progression through the Cyber Kill Chain model. It encourages learners to pause, reconsider earlier discoveries, and recognize how each phase interconnects, fostering a more strategic mindset.
Supporting this process, large language models deliver natural-language explanations, condense attack patterns, and propose tactical adjustments during exercises. This transforms the training from a static drill into an adaptive mentoring experience, where AI provides contextual advice exactly when it’s needed.
The framework organizes training along two axes. Horizontally, it spans various simulation categories, applications, networks, physical systems, and even social engineering scenarios. Vertically, it aligns with each stage of the Cyber Kill Chain, from reconnaissance and weaponization to command and control and final objectives. This two-dimensional structure ensures a comprehensive and methodical hands-on practice across diverse assets and attack methodologies.
This design intentionally mirrors the non-linear reality of penetration testing. A learner might begin with network scanning, pivot to exploitation, then circle back to refine their initial reconnaissance based on new findings. RTK assists throughout, offering situational guidance that evolves with the user’s actions.
The research also situates this training within the broader concept of Cyber Social Security, emphasizing the role of human behavior and social dynamics in cybersecurity. With social engineering becoming a dominant attack vector, the authors stress that effective training must incorporate psychological and social dimensions. Here, LLMs prove invaluable by parsing unstructured data from forums, threat reports, or dark web sources and presenting actionable intelligence in clear, accessible language.
Beyond education, digital twins enable organizations to safely test detection and response protocols without operational risk. Teams can simulate attacks, model adversary behavior, and evaluate countermeasures in a consequence-free setting. The integration of LLMs also aids communication by translating technical events into plain language, making complex scenarios understandable for non-specialists, an asset for security operations centers and interdisciplinary teams.
Nevertheless, experts highlight the importance of robust safeguards for AI-assisted training systems. Jason Soroko, Senior Fellow at Sectigo, emphasized the need for stringent isolation, frequent system reimaging, and careful filtering of AI inputs and outputs to prevent misuse. He recommended implementing role-based access controls, immutable logs, and routine red team assessments of both the tutoring AI and the training environment itself.
Although still in development, the framework is poised for real-world testing through upcoming user studies. Its potential applications extend beyond training into active threat modeling, system diagnostics, and adaptive defense mechanisms. By uniting high-fidelity simulation with advanced language-based reasoning, this research heralds a new era of cybersecurity preparation, one that is deeply immersive, intelligently responsive, and closely aligned with genuine attack workflows.
(Source: HelpNet Security)