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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

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.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

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.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

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

No agent tool is listed for this challenge.

Evaluated output

{'summary': 'Overall risk assessment...', 'identified_risks': [{'risk': '...', 'severity': '...', 'mitigation_strategies': '...'}], 'compliance_ana...

Evaluation checks
  • 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.
Configured evaluation evidence
  • RiskSeverityAccuracy target: 85
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • 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.

Metrics

  • RiskSeverityAccuracy · Target 85
  • InformationRetrievalRecall · Target 90

Workflow tasks

  1. Task 1

    Input: {'company_profile': {'name': 'TargetCorp', 'industry': 'Cybersecurity', 'recent_breaches': ['2023-Q3 phishing attack'], 'public_documents': ['link_to_annual_report.pdf', 'link_to_security_policy.pdf']}}

    Output: {'summary': 'Overall risk assessment...', 'identified_risks': [{'risk': '...', 'severity': '...', 'mitigation_strategies': '...'}], 'compliance_analysis': {'gdpr': '...', 'iso27001': '...'}, 'financial_impact': '...', 'recommendations': '...'}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Cybersecurity M&A Due Diligence