Cybersecurity M&A Due Diligence
Develop an advanced agent system for automated cybersecurity Mergers & Acquisitions (M&A) due diligence. This challenge focuses on creating a Langroid-orchestrated multi-agent system that leverages GPT-5.2 Pro's extended thinking capabilities to analyze potential acquisition targets. The system will integrate with various external data sources—such as threat intelligence platforms, financial databases, and corporate security reports—through a robust MCP tool integration layer. The system should perform deep dives into a target company's cybersecurity posture, identify potential vulnerabilities, assess compliance risks, and project post-acquisition integration challenges. It will utilize RAG with LlamaIndex to query internal and external knowledge bases, enabling agents to retrieve and synthesize critical information efficiently. Adaptive reasoning budgets will be employed to dynamically allocate computational resources for complex problem-solving, ensuring comprehensive analysis within defined cost parameters.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an MCP-enabled, Langroid-orchestrated multi-agent system with GPT-5-2 Pro for automated cybersecurity M&A due diligence, employing extended thinking and RAG.
How work is evaluated
The evaluation will assess the system's ability to accurately identify and summarize key cybersecurity risks, compliance gaps, and financial implications for a simulated acquisition target based on provided documents and external data queries. Performance will be measured by the comprehensiveness and accuracy of the generated due diligence report.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Implement advanced RAG using LlamaIndex for comprehensive information retrieval and synthesis.
Develop MCP-enabled tool integrations for diverse data sources.
Integrate GPT-5 Pro's extended thinking with adaptive reasoning budgets.
Design and implement a multi-agent system using Langroid for M&A due diligence.
Reference links and supporting material
Sample data for 1 tasks
How this agent runs
The evaluation will assess the system's ability to accurately identify and summarize key cybersecurity risks, compliance gaps, and financial implications for a simulated acquisition target based on provided documents...
Challenge input
{'company_profile': {'name': 'TargetCorp', 'industry': 'Cybersecurity', 'recent_breaches': ['2023-Q3 phishing attack'], 'public_documents': ['link_...
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'summary': 'Overall risk assessment...', 'identified_risks': [{'risk': '...', 'severity': '...', 'mitigation_strategies': '...'}], 'compliance_ana...
- Checks if all required sections of the due diligence report are present.
- Verifies that at least 3 distinct and relevant cybersecurity risks are identified based on the input data.
- RiskSeverityAccuracy target: 85
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.