Design Principles for the Construction of a Benchmark Evaluating Security Operation Capabilities of Multi-agent AI Systems

1. Problem Statement

As Large Language Models (LLMs) and multi-agent AI systems gain traction in cybersecurity, there is a critical need to quantify their ability to automate Security Operations Centers (SOCs). While existing benchmarks (e.g., Cybench, CyberGym) effectively evaluate Red Team capabilities (penetration testing, vulnerability discovery), there is a significant gap in evaluating Blue Team capabilities.

• The Gap: SOC operations are dominated by Blue Team activities (incident response, threat hunting, containment), which are complex, multi-task, and real-time. Existing Blue Team benchmarks focus on isolated, single tasks (e.g., extracting Indicators of Compromise from a report) rather than the coordinated, multi-step nature of a real-world incident response.
• The Challenge: Evaluating autonomous multi-agent systems requires a benchmark that simulates the "as-is" reality of a SOC, including imperfect data, uncertainty, and the need for agents to discover inter-task dependencies without human hints.

2. Methodology: The SOC-bench Framework

The authors propose SOC-bench, a conceptual benchmark design grounded in five specific Design Principles (DPs) and instantiated through a family of five coordinated tasks based on a large-scale ransomware incident (modeled after the Colonial Pipeline attack).

A. Five Core Design Principles

1. DP1 (As-Is Gold Standard): The benchmark must be based on a real-world, historical incident response system ("as-is"), not a hypothetical future system. Humans act only as supervisors; AI agents must operate with the same imperfect visibility and uncertainty as human teams.
2. DP2 (No Cross-Task Hints): To test true autonomy, the benchmark must not provide hints regarding the dependencies between different tasks. Agents must autonomously explore how data from one task (e.g., forensics) informs another (e.g., containment).
3. DP3 (Outcome-Only Evaluation): Evaluation focuses on the outcome, not the process. AI systems are allowed to solve problems in ways that deviate from human methods, provided the final result is correct and evidence-backed.
4. DP4 (Inherent Imperfection): The benchmark must reflect real-world system flaws, including false positives/negatives in alerts, latency in data, and missing data sources.
5. DP5 (Future-Proofing): The design must not rely on specific current AI capabilities (e.g., specific RAG implementations or Model Context Protocols) to remain relevant as agentic AI evolves.

B. The Five Task Family

The benchmark is structured around five distinct but interdependent tasks, each simulating a specific phase of a ransomware incident response:

1. Task Fox (Early Detection):

   • Goal: Detect a coordinated attack campaign early and distinguish it from isolated anomalies.
   • Inputs: Host telemetry, network flows, SIEM alerts, and helpdesk tickets.
   • Outputs: Classification of campaign scale (isolated vs. campaign), type inference (ransomware vs. other), and an incident alert triage summary.
   • Scoring: Ring-based model (Bullseye/Inner/Outer) based on correctness and evidence linkage.

2. Task Goat (File System Forensics):

   • Goal: Analyze file system metadata to identify encryption patterns, host impact, and Volume Shadow Copy (VSS) tampering.
   • Inputs: File system journals, EDR logs, VSS logs, and helpdesk reports.
   • Outputs: Encryption state labels, impact aggregation, VSS tamper facts, process attribution, and a one-page summary.
   • Scoring: Strict matching of file/directory states and impact fractions against ground truth.

3. Task Mouse (Data Exfiltration Analysis):

   • Goal: Determine if data exfiltration occurred, estimate volume/timing, and identify involved hosts and protocols.
   • Inputs: PCAP files, firewall logs, Windows/Linux system logs.
   • Outputs: Binary determination of exfiltration, start time, data volume, host list, and protocol identification.
   • Scoring: Precision-based scoring (e.g., time within ±5 mins, volume within ±10GB).

4. Task Tiger (Threat Attribution & TTPs):

   • Goal: Act as a Tier 2 analyst to attribute the attack, identify Tactics, Techniques, and Procedures (TTPs), and construct a threat graph.
   • Inputs: Diverse sources including CTI, OS logs, malware samples, and user tickets (with "traps" like irrelevant noise).
   • Outputs: List of relevant data sources, a structured Threat Graph (nodes/edges), and an initial entrypoint summary.
   • Scoring: Deduction-based rubric for irrelevant sources, incorrect graph edges, and missing evidence.

5. Task Panda (Containment Recommendations):

   • Goal: Recommend containment actions (isolation, blocking) based on the evolving situation.
   • Inputs: Synthesized data from previous stages (simulated in real-time 30-minute intervals).
   • Outputs: A BLUF (Bottom Line Up Front) report containing actions, targets, and reasoning (trade-offs between security and availability).
   • Scoring: Evaluates accuracy of actions, target identification, and the quality of reasoning/evidence.

3. Key Contributions

• First Systematic Blue Team Benchmark: The paper proposes the first systematic framework specifically designed to evaluate coordinated, multi-task Blue Team AI capabilities, moving beyond single-task evaluations.
• Design Principles for Agentic AI: The five DPs provide a theoretical foundation for building robust, realistic, and future-proof cybersecurity benchmarks that prioritize autonomy and outcome over process mimicry.
• Realistic Simulation Environment: By modeling the benchmark on the Colonial Pipeline incident and incorporating "traps" (noise, false positives, missing data), SOC-bench offers a high-fidelity simulation of real-world SOC constraints.
• Inter-Task Dependency Modeling: The framework explicitly tests the AI's ability to link disparate tasks (e.g., using forensics from "Goat" to inform containment in "Panda") without explicit guidance, a critical capability for autonomous SOCs.

4. Results

Note: As this paper presents a conceptual design and design principles rather than empirical experimental results of an AI system running the benchmark, there are no quantitative performance scores of specific AI models.

• Validation: The authors validated the design principles by constructing the conceptual framework of SOC-bench. They demonstrated that the five tasks cover the essential coordination activities of a real SOC and that the scoring mechanisms (Ring models, BLUF reports) are objective and reproducible.
• Feasibility: The paper argues that the proposed design allows for objective evaluation of multi-agent systems, ensuring that performance on SOC-bench correlates with real-world operational capability.

5. Significance

• Bridging the Gap: SOC-bench addresses the critical imbalance in AI cybersecurity research, which has heavily favored offensive (Red Team) capabilities over defensive (Blue Team) automation.
• Accelerating Autonomous SOCs: By providing a rigorous standard for evaluation, SOC-bench enables organizations and researchers to safely test and deploy multi-agent AI systems for incident response, potentially reducing the manual burden on human analysts.
• Standardization: The design principles offer a blueprint for the broader community to develop future benchmarks that are resilient to rapid AI advancements and grounded in the messy reality of cybersecurity operations.
• Safety and Reliability: By emphasizing "outcome-only" evaluation and realistic constraints (DP1, DP3, DP4), the framework ensures that AI systems are judged on their ability to make safe, effective decisions under uncertainty, rather than just their ability to follow scripted prompts.