Design Principles for the Construction of a Benchmark Evaluating Security Operation Capabilities of Multi-agent AI Systems
Summary: arXiv:2603.28998v1 Announce Type: cross
As Large Language Models (LLMs) and multi-agent AI systems continue to showcase their potential in the realm of cybersecurity operations, there is a growing interest among organizations, policymakers, model providers, and researchers to quantify the capabilities of such AI systems. This interest aims to achieve more autonomous Security Operation Centers (SOCs) while minimizing manual efforts in threat detection and response.
Recently, the AI and cybersecurity communities have developed multiple benchmarks to evaluate the red team capabilities of multi-agent AI systems. However, the operations within SOCs are predominantly characterized by blue team activities, which focus on defense and incident response. Therefore, the evaluation of AI systems and agents in achieving more autonomous SOCs remains incomplete without a benchmark that concentrates on blue team operations.
To the best of our knowledge, there has yet to be a systematic benchmark designed specifically for assessing coordinated multi-task blue team AI capabilities in the existing literature. The current blue team benchmarks tend to emphasize specific tasks rather than providing a comprehensive evaluation framework. This article aims to outline a set of design principles for the development of a new benchmark, referred to as SOC-bench, which will evaluate the blue team capabilities of AI systems.
Design Principles for SOC-bench
The development of SOC-bench is guided by a series of design principles that ensure the benchmark is relevant, comprehensive, and effective in evaluating blue team operations. The key principles include:
- Task Diversity: The benchmark must encompass a wide range of tasks that blue teams typically perform, including threat detection, incident response, and post-incident analysis.
- Realism: Scenarios used in the benchmark should reflect real-world cybersecurity incidents, particularly large-scale ransomware attacks, to ensure practical applicability.
- Coordination Assessment: The benchmark should evaluate how well multiple agents can coordinate their actions in response to a security incident.
- Metric Development: Clear metrics need to be established to quantitatively assess the performance of AI systems in the context of blue team operations.
- Adaptability: The benchmark should be designed to evolve over time, incorporating new threats and developments in the cybersecurity landscape.
Conceptual Design of SOC-bench
Following the outlined design principles, we have developed a conceptual design for SOC-bench. This benchmark consists of a family of five blue team tasks, specifically tailored to address the challenges associated with large-scale ransomware attack incident response. These tasks will evaluate the AI systems’ capabilities in:
- Identifying ransomware signatures and behaviors
- Analyzing network traffic for unusual patterns
- Coordinating response efforts among multiple agents
- Implementing containment strategies
- Conducting post-incident reviews to improve future responses
In conclusion, the creation of SOC-bench represents a significant step towards developing a robust framework for evaluating the blue team capabilities of multi-agent AI systems. By adhering to these design principles, researchers and practitioners can ensure that AI systems are not only capable of defending against cyber threats but are also effective in enhancing the overall security posture of organizations.