Advancing Cybersecurity: A Hybrid AI Red Teaming Framework for Robust SOAR Systems

The Evolving Landscape of Cyber Threats and AI Defenses

      The digital battleground is intensifying. Cyber threats against enterprise networks are not just increasing; they are becoming more sophisticated and frequent, with reports indicating a staggering number of weekly attacks per organization globally by 2025. In response to this relentless onslaught, enterprises are rapidly deploying Security Orchestration, Automation, and Response (SOAR) systems. These advanced platforms leverage Artificial Intelligence (AI) to automate threat detection, streamline incident response, and enforce security policies at machine speed. While promising, the very AI that powers these defenses also introduces a new challenge: how resilient are these systems against adaptive, intelligent adversaries?

      Traditional cybersecurity testing methods, such as standard penetration testing and static rule-based red team scripts, often fall short. They struggle to replicate the dynamic, multi-stage nature of real-world cyberattack campaigns. This leaves a critical vulnerability in understanding the true robustness of AI-enabled cyber defense systems. To effectively counter this, there's a growing need for autonomous red teaming, where AI agents actively emulate sophisticated attackers, following structured kill-chain behaviors like reconnaissance, initial access, privilege escalation, and lateral movement. This proactive approach aims to rigorously stress-test and fortify cyber defenses before real threats exploit them.

The Critical Need for Autonomous Red Teaming

      The rise of AI in cyber defense necessitates a parallel evolution in adversarial testing. Imagine an attacker who learns and adapts in real-time, executing complex, multi-stage campaigns. Traditional static testing cannot adequately simulate such an adversary, leading to a false sense of security. Autonomous red teaming addresses this by deploying AI agents designed to think like and behave as human attackers, constantly probing for weaknesses and exploiting vulnerabilities. This method provides an invaluable mechanism for evaluating the true resilience of AI-enabled SOAR systems and identifying unforeseen gaps in their defensive capabilities.

      ARSA Technology, with its focus on practical AI deployment, understands the importance of rigorous testing to ensure robust security. Solutions such as ARSA AI Video Analytics are deployed to detect anomalies and potential threats, making the need for advanced red teaming even more pertinent to validate their effectiveness against evolving attacks. By embracing autonomous red teaming, organizations can gain a deeper, more actionable understanding of their security posture, moving beyond reactive defense to proactive cyber resilience.

Bridging Strategy and Tactics: A Hybrid AI Framework

      The latest advancements in AI offer powerful tools for autonomous red teaming, yet each approach has its limitations. Large Language Models (LLMs) excel at strategic planning, understanding attack objectives, and selecting high-level strategies. However, they can struggle with maintaining long-horizon state tracking, ensuring action consistency over time, and adapting dynamically to real-time environment feedback. Conversely, Reinforcement Learning (RL) agents are adept at tactical execution, learning optimal action sequences through trial and error in dynamic environments. Their weaknesses lie in sample inefficiency, limited interpretability, and poor generalization across diverse cyber scenarios.

      To overcome these individual limitations, a novel hierarchical hybrid framework has been developed that integrates LLM-based strategic planning with RL-based tactical execution for red team operations. In this model, the LLM functions as a high-level planner, setting strategic intent, formulating attack objectives, and modulating the overall risk posture of the red team. The RL controller, meanwhile, executes low-level actions, dynamically responding to the immediate environment state and adhering to the directives issued by the LLM planner. This architectural decoupling allows the framework to leverage the strengths of both paradigms: the LLM's vast knowledge and reasoning for strategy, and the RL agent's adaptive learning for precise, environment-grounded execution. During this process, the LLM's parameters are kept static to preserve its accumulated knowledge, while the RL component continuously optimizes its execution policies through interaction with the simulated environment.

The Adversarial Playbook: Kill-Chain Aligned Reinforcement Learning

      A crucial element of this hybrid framework is its integration of a kill-chain-aligned RL approach. The "kill chain" refers to the distinct stages of a cyberattack, from initial reconnaissance to achieving the attacker's objective. By structuring the reinforcement learning rewards to align with the progression through these kill-chain stages (such as initial access, privilege escalation, lateral movement, and impact, as defined by frameworks like MITRE ATT&CK), the RL agent is intrinsically guided toward long-horizon adversarial behaviors. This reward shaping incentivizes the AI to pursue sequential, multi-step attacks that accurately reflect the complex methodologies of human adversaries, rather than focusing on isolated, short-term gains.

      This structured guidance ensures that the autonomous red team doesn't just launch random attacks but executes sophisticated, goal-oriented campaigns. For instance, when the RL agent successfully achieves "initial access," it receives a reward, prompting it to then focus on "privilege escalation" for further rewards. This methodical progression is critical for thoroughly testing SOAR systems, as it forces them to detect and respond to threats across various stages of an attack, not just at the initial breach point. Such advanced capabilities can be implemented on platforms like the ARSA AI Box Series, which offers pre-configured edge AI systems for rapid deployment, processing video streams at the edge to deliver instant insights and contribute to robust security postures.

Evaluating Robustness in High-Fidelity Simulations

      To accurately assess the effectiveness of such a sophisticated red teaming framework, evaluation must occur in highly realistic and demanding environments. The researchers utilized the Cyber Operations Research Gym (CybORG) CAGE Challenge 4 environment, a U.S. government-sponsored benchmark. CAGE 4 is a high-fidelity enterprise network simulation characterized by partial observability, meaning the red team agent does not have a complete view of the network at all times, mimicking real-world blind spots. It also features long-horizon decision-making, requiring sustained strategic planning, and, critically, includes adaptive defensive responses.

      Within this environment, the red team framework faces five coordinated blue team defenders, powered by the Hierarchical Multi-Agent Reinforcement Learning (H-MARL) Expert policy. This H-MARL policy represents one of the strongest published autonomous defenders in this setting, making CAGE 4 an exceptionally rigorous benchmark for evaluating robustness under realistic adversarial conditions. The environment also includes dynamic defensive actions such as host restoration, network isolation, and deception deployment, compelling the red agent to constantly adapt its strategy in real-time. The findings from such simulations are invaluable, providing clear insights into how security systems truly perform under pressure.

Key Findings: The Power of Hybrid Intelligence

      The empirical study conducted in the challenging CAGE 4 environment yielded crucial insights into the efficacy of different AI approaches for red teaming (Source: A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems). The evaluation demonstrated a clear distinction in performance:

• Standalone LLM Agents: While powerful in reasoning and planning, pure LLM agents struggled significantly to sustain multi-stage attack campaigns against adaptive autonomous defenders. Their limitations in long-horizon state tracking, action consistency, and dynamic adaptation hindered their ability to achieve deep compromise.
• Domain-Specific Cybersecurity Models: Even specialized models, such as Cisco’s Foundation-Sec-8B and DeepHat-7B (formerly WhiteRabbitNeo), designed for cybersecurity tasks, achieved only limited levels of compromise when operating independently. This highlighted that while specialized knowledge is beneficial, it doesn't fully compensate for the shortcomings in real-time, adaptive execution.
• Hybrid LLM-RL Architecture: The proposed hierarchical hybrid framework, combining the strategic planning of LLMs with the tactical execution and adaptive learning of RL, proved to be significantly more effective. This architecture successfully generated adaptive, multi-stage attack campaigns, demonstrating a superior capability to compromise autonomous defenders in high-fidelity enterprise simulations.

      These findings underscore the necessity of hybrid LLM-RL approaches for robust autonomous red teaming. It confirms that the combination of strategic foresight from LLMs and the adaptive, environment-grounded control of RL agents is crucial for creating red teams that can truly stress-test and fortify the complex AI-enabled SOAR systems of today's enterprise networks. For organizations looking to develop tailored security solutions leveraging these insights, ARSA's Custom AI Solution offers the expertise to engineer such advanced, mission-critical systems.

Conclusion: Fortifying Future Cyber Defenses

      As cyber threats continue to escalate in frequency and sophistication, the imperative to build truly robust AI-enabled security systems has never been clearer. The development of a hierarchical hybrid framework, which intelligently combines the strategic planning capabilities of Large Language Models with the tactical execution prowess of Reinforcement Learning, represents a significant leap forward in autonomous red teaming. This innovative approach provides a powerful means to rigorously evaluate the resilience of AI-powered SOAR systems, uncovering vulnerabilities that traditional testing methods often miss.

      By simulating adaptive, multi-stage attack campaigns, this framework ensures that enterprise defenses are not just reactive but proactively hardened against the most advanced adversaries. The demonstrated superiority of hybrid AI in challenging, high-fidelity environments underlines a critical path for the future of cybersecurity. It empowers organizations to move beyond mere compliance, building genuinely resilient digital fortresses capable of withstanding the complex cyber warfare of tomorrow.

      Elevate your enterprise cybersecurity posture with ARSA Technology’s cutting-edge AI and IoT solutions. Explore our comprehensive offerings and initiate a strategic dialogue with our experts today to build more resilient defenses by leveraging advanced AI for robust security.

      Ready to engineer your competitive advantage in cybersecurity? contact ARSA for a free consultation.

      Source: Shaikh, A. J., Bastian, N. D., & Shah, A. (2026). A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems. arXiv preprint arXiv:2605.17075.