Benchmarking AI Playbooks: The Ultimate Guide to Public AI SOC Datasets

As Security Operations Centers (SOCs) rapidly transition from static orchestration scripts to advanced Large Language Model (LLM) agents, a massive engineering hurdle has emerged: How do you evaluate if an AI playbook is safe, accurate, and effective? Unlike traditional deterministic software tools, an autonomous AI responder handles vast, chaotic security telemetry with varying levels of reasoning. Blindly deploying an uncalibrated LLM agent into production can result in catastrophic operational downtime, breaking of business infrastructure, or missed lateral-movement indicators.

To solve this, leading research labs, open-source security coalitions, and hyperscalers have open-sourced specialized datasets and testing evaluation suites. This comprehensive guide reviews the critical public datasets and frameworks available today to rigorously benchmark and stress-test your AI SOC playbooks.

1. CyberSecEval 4 (by Meta Purple Llama)

The Repository: https://github.com/meta-llama/PurpleLlama

What It Is: Developed under Meta’s Purple Llama safety initiative, CyberSecEval is an industry-recognized standard for auditing security-focused LLMs. Its latest iteration introduces a tailored subsystem called CyberSOCEval. This dataset explicitly measures an AI model’s capability to enable Security Operation Center efficiency improvements or automation, focusing on threat intelligence reasoning, automated security patching, log analysis, and malware triage.

How to Use It for AI Playbooks: Use CyberSOCEval to test your agent’s telemetry accuracy and guardrail calibration. It features specific data components to evaluate False Refusal Rates (FRR). This ensures that when your AI playbook isolates a suspicious host or extracts a live malware payload for parsing, the underlying LLM won’t trigger generic safety guardrails and refuse to execute the task out of an overabundance of caution.

2. ExCyTIn-Bench (Extended Cyber Threat Investigation)

The Repository: https://github.com/kaebvcidn/Excytin-Bench

What It Is: ExCyTIn-Bench is an enterprise-grade benchmark designed exclusively to evaluate LLM agents acting on Extended Cyber Threat Investigations. The dataset centers around sophisticated, multi-stage cloud attacks mapping natively across corporate cloud environments (focusing heavily on Azure telemetry and cloud enterprise infrastructure).

How to Use It for AI Playbooks: This is the perfect training ground for Tier-2 and Tier-3 incident response playbooks. Instead of testing basic single-indicator alerts (like an isolated bad IP address), ExCyTIn-Bench presents complex, fragmented log streams. You can run your autonomous agents through these scenarios to evaluate if they can successfully chain distinct cloud anomalies, map attacker actions back to the MITRE ATT&CK matrix, and correctly recommend enterprise mitigation strategies.

3. SEC-bench

The Repository: https://github.com/SEC-bench/SEC-bench

What It Is: Published as a premier benchmark for evaluating LLM agents on real-world software security tasks, SEC-bench relies heavily on containerized Docker infrastructure coupled with structured multi-agentic workflows. It dynamically pulls real vulnerability data from the Open Source Vulnerability (OSV) database and live CVE registries to measure AI logic against active exploits.

How to Use It for AI Playbooks: Secure code execution and autonomous remediation playbooks thrive on this dataset. If your AI playbook is tasked with diagnosing a vulnerable endpoint, spinning up a proof-of-concept (PoC) to verify the flaw, or automatically generating an infrastructure patch, SEC-bench serves as an automated sandbox to measure your agent’s success rate without risking live production assets.

4. The Open-Agent AgentBench Suite (OpenSec Framework)

The Repository: https://github.com/the-open-agent/agentbench

What It Is: Built specifically for multi-stage LLM agent validation, this benchmark includes highly critical data splits such as HardChat and Tool-Invocation. In an AI SOC context, these scenarios challenge an agent’s reasoning capability across long context windows, deep technical log normalization, and shell environment diagnostics.

How to Use It for AI Playbooks: Use this framework to stress-test your AI’s execution safety (often referred to as agent calibration). The Tool suite monitors whether an agent accurately calls local diagnostic tools and reads shell output without breaking validation rules. This ensures your autonomous containment playbooks don’t accidentally fire destructive commands due to formatting or structural errors.

5. CybORG (Cyber Operations Research Gym)

The Repository: https://github.com/cage-challenge/CybORG

What It Is: Developed by defense researchers, CybORG is an interactive simulation engine conforming to the OpenAI Gym interface. It models complete adversarial networks (such as enterprise Active Directory environments or drone networks) where an active red-team agent attempts network traversal and privilege escalation against a defending blue-team agent.

How to Use It for AI Playbooks: CybORG is uniquely tailored for Reinforcement Learning (RL) and hybrid agentic playbooks. By implementing the environment wrapper, you can plug your automated playbook logic straight into a live simulation. The agent will receive actual network states and rewards based on the quality of its defensive choices (e.g., executing actions like DeployDecoy, ControlTraffic, or Analyse host logs).

Strategic Takeaway for Modern SOCs

When building an automated testing pipeline for your autonomous SOC, combine these resources to address different aspects of your pipelines: Use CyberSecEval to ensure your base LLM possesses the domain knowledge to analyze malware; apply ExCyTIn-Bench to refine multi-step cloud log synthesis; and utilize CybORG or SEC-bench to safely run live verification runs before production rollouts.